Journal Description
Robotics
Robotics
is an international, peer-reviewed, open access journal on robotics published monthly online by MDPI. The IFToMM is affiliated with Robotics and its members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Robotics) / CiteScore - Q1 (Mechanical Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.9 (2023);
5-Year Impact Factor:
3.1 (2023)
Latest Articles
Hybrid Sensor Fusion Mixed with Reinforcement Learning in Autonomous Dual-Arm Lifting Tasks Performed by Humanoid Robots
Robotics 2024, 13(8), 121; https://doi.org/10.3390/robotics13080121 (registering DOI) - 11 Aug 2024
Abstract
Humanoid robots often struggle with tasks such as lifting objects due to the complexity involved in identifying contact points, applying the correct force, and tracking task progress. We propose an integrated solution that leverages the dual-arm capability of humanoids and utilizes sensor fusion
[...] Read more.
Humanoid robots often struggle with tasks such as lifting objects due to the complexity involved in identifying contact points, applying the correct force, and tracking task progress. We propose an integrated solution that leverages the dual-arm capability of humanoids and utilizes sensor fusion from vision and force sensors. Our system employs a computer vision algorithm to detect and characterize object properties (shape, size, position, orientation) and differentiate between parallel and non-parallel bi-manipulation tasks. The controller then identifies optimal contact points for the end effectors, generating trajectories fed into a closed-loop controller using force feedback. For parallel bi-manipulation, momentum cancellation is achieved through sensor fusion. For non-parallel surfaces, a reinforcement learning algorithm determines the appropriate lifting force to prevent slippage using only two contact points. Experimental validation on a real humanoid platform demonstrates the effectiveness of our approach in autonomously lifting objects, regardless of contact surface configuration. This advancement significantly enhances the reliability and versatility of humanoid robots in performing complex manipulation tasks, contributing to their practical deployment in human-oriented environments.
Full article
(This article belongs to the Section Humanoid and Human Robotics)
Open AccessArticle
Design and Evaluation of a Novel Passive Shoulder Exoskeleton Based on a Variable Stiffness Mechanism Torque Generator for Industrial Applications
by
Yu Zhu, Felix Balser, Ming Shen and Shaoping Bai
Robotics 2024, 13(8), 120; https://doi.org/10.3390/robotics13080120 - 8 Aug 2024
Abstract
Work-related musculoskeletal disorders (WMSDs) are a common occupational health problem in industries, and they can lead to decreased productivity and a reduced quality of life for workers. Exoskeletons, as an emerging technology, have the potential to solve this challenge by assisting arm movements
[...] Read more.
Work-related musculoskeletal disorders (WMSDs) are a common occupational health problem in industries, and they can lead to decreased productivity and a reduced quality of life for workers. Exoskeletons, as an emerging technology, have the potential to solve this challenge by assisting arm movements and reducing muscle effort during load lifting tasks. In this paper, a passive exoskeleton based on a variable stiffness mechanism (VSM) torque generator is proposed and evaluated. This exoskeleton can provide adjustable torque curves and accommodate three degrees of freedom (DOFs) while remaining compact and lightweight. The workspace analysis shows that the workspace of this exoskeleton is sufficient for most industrial manual handling tasks. The experimental results demonstrate that the exoskeleton effectively reduces muscle effort during overhead reaching and load-lifting tasks, highlighting its effectiveness for repetitive tasks in industrial settings.
Full article
(This article belongs to the Section Intelligent Robots and Mechatronics)
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![](https://pub.mdpi-res.com/robotics/robotics-13-00120/article_deploy/html/images/robotics-13-00120-g001-550.jpg?1723101264)
Figure 1
Figure 1
<p>The basic concept of the VSM. (<b>a</b>) The special case of the four-bar linkage, (<b>b</b>) the implementation of the VSM with pulleys and cable, (<b>c</b>) torque generator concept with VSM, where the torque is generated due to the restoring force of the spring.</p> Full article ">Figure 2
<p>Torque distribution of the VSM with respect to pretension force and deflection angle.</p> Full article ">Figure 3
<p>Definition of the shoulder and elbow extension/flexion angles with a simplified model of the human arm.</p> Full article ">Figure 4
<p>Torque curve with optimized parameters.</p> Full article ">Figure 5
<p>Views of the shoulder exoskeleton. (<b>a</b>) Isometric view of the torque generator, (<b>b</b>) top view of the torque generator with the cover removed: (1) input plate, (2) output plate, (3) cable, (4) encoder, (5) linear spring, (<b>c</b>) side view with (6) free revolute joint, (7) manual adjusting module. (<b>d</b>) The shoulder exoskeleton worn by the lead author.</p> Full article ">Figure 6
<p>Workspace point cloud of the human arm with the exoskeleton. (<b>a</b>) Isometric workspace, (<b>b</b>) workspace projected into the XOY plane. The units in these plots are meters.</p> Full article ">Figure 7
<p>Placement of the electrodes.</p> Full article ">Figure 8
<p>Illustration of static load lifting experiments. (<b>a</b>) Load lifting at 90 degrees, (<b>b</b>) load lifting at 135 degrees.</p> Full article ">Figure 9
<p>Illustration of dynamic load lifting experiment where participants were asked to lift the load from 0 to 135 degrees and hold it at 135 degrees for 3 s.</p> Full article ">Figure 10
<p>Raw and processed EMG signals recorded during the static load lifting experiment. (<b>a</b>) Raw EMG signal, (<b>b</b>) processed EMG signal.</p> Full article ">Figure 11
<p>Normalized mean muscle activation during static load lifting of 90 degrees (<b>left</b>) and 135 degrees (<b>right</b>).</p> Full article ">Figure 12
<p>Normalized mean muscle activation during dynamic load lifting.</p> Full article ">
<p>The basic concept of the VSM. (<b>a</b>) The special case of the four-bar linkage, (<b>b</b>) the implementation of the VSM with pulleys and cable, (<b>c</b>) torque generator concept with VSM, where the torque is generated due to the restoring force of the spring.</p> Full article ">Figure 2
<p>Torque distribution of the VSM with respect to pretension force and deflection angle.</p> Full article ">Figure 3
<p>Definition of the shoulder and elbow extension/flexion angles with a simplified model of the human arm.</p> Full article ">Figure 4
<p>Torque curve with optimized parameters.</p> Full article ">Figure 5
<p>Views of the shoulder exoskeleton. (<b>a</b>) Isometric view of the torque generator, (<b>b</b>) top view of the torque generator with the cover removed: (1) input plate, (2) output plate, (3) cable, (4) encoder, (5) linear spring, (<b>c</b>) side view with (6) free revolute joint, (7) manual adjusting module. (<b>d</b>) The shoulder exoskeleton worn by the lead author.</p> Full article ">Figure 6
<p>Workspace point cloud of the human arm with the exoskeleton. (<b>a</b>) Isometric workspace, (<b>b</b>) workspace projected into the XOY plane. The units in these plots are meters.</p> Full article ">Figure 7
<p>Placement of the electrodes.</p> Full article ">Figure 8
<p>Illustration of static load lifting experiments. (<b>a</b>) Load lifting at 90 degrees, (<b>b</b>) load lifting at 135 degrees.</p> Full article ">Figure 9
<p>Illustration of dynamic load lifting experiment where participants were asked to lift the load from 0 to 135 degrees and hold it at 135 degrees for 3 s.</p> Full article ">Figure 10
<p>Raw and processed EMG signals recorded during the static load lifting experiment. (<b>a</b>) Raw EMG signal, (<b>b</b>) processed EMG signal.</p> Full article ">Figure 11
<p>Normalized mean muscle activation during static load lifting of 90 degrees (<b>left</b>) and 135 degrees (<b>right</b>).</p> Full article ">Figure 12
<p>Normalized mean muscle activation during dynamic load lifting.</p> Full article ">
Open AccessArticle
Hand Teleoperation with Combined Kinaesthetic and Tactile Feedback: A Full Upper Limb Exoskeleton Interface Enhanced by Tactile Linear Actuators
by
Daniele Leonardis, Massimiliano Gabardi, Simone Marcheschi, Michele Barsotti, Francesco Porcini, Domenico Chiaradia and Antonio Frisoli
Robotics 2024, 13(8), 119; https://doi.org/10.3390/robotics13080119 - 7 Aug 2024
Abstract
Manipulation involves both fine tactile feedback, with dynamic transients perceived by fingerpad mechanoreceptors, and kinaesthetic force feedback, involving the whole hand musculoskeletal structure. In teleoperation experiments, these fundamental aspects are usually divided between different setups at the operator side: those making use of
[...] Read more.
Manipulation involves both fine tactile feedback, with dynamic transients perceived by fingerpad mechanoreceptors, and kinaesthetic force feedback, involving the whole hand musculoskeletal structure. In teleoperation experiments, these fundamental aspects are usually divided between different setups at the operator side: those making use of lightweight gloves and optical tracking systems, oriented toward tactile-only feedback, and those implementing exoskeletons or grounded manipulators as haptic devices delivering kinaesthetic force feedback. At the level of hand interfaces, exoskeletons providing kinaesthetic force feedback undergo a trade-off between maximum rendered forces and bandpass of the embedded actuators, making these systems unable to properly render tactile feedback. To overcome these limitations, here, we investigate a full upper limb exoskeleton, covering all the upper limb body segments from shoulder to finger phalanxes, coupled with linear voice coil actuators at the fingertips. These are developed to render wide-bandwidth tactile feedback together with the kinaesthetic force feedback provided by the hand exoskeleton. We investigate the system in a pick-and-place teleoperation task, under two different feedback conditions (visual-only and visuo-haptic). The performance based on measured interaction forces and the number of correct trials are evaluated and compared. The study demonstrates the overall feasibility and effectiveness of a complex full upper limb exoskeleton (seven limb-actuated DoFs plus five hand DoFs) capable of combined kinaesthetic and tactile haptic feedback. Quantitative results show significant performance improvements when haptic feedback is provided, in particular for the mean and peak exerted forces, and for the correct rate of the pick-and-place task.
Full article
(This article belongs to the Section Neurorobotics)
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Figure 1
Figure 1
<p>The proposed system is composed of a hand exoskeleton integrating additional cutaneous force feedback actuators. The teleoperated robotic hand includes force sensors at the fingertips.</p> Full article ">Figure 2
<p>Detail of the leader interface consisting of the developed full upper limb exoskeleton integrated with haptic thimbles.</p> Full article ">Figure 3
<p>Detail of the follower interface implementing the robotic hand equipped with two sensitive force sensors (index and thumb).</p> Full article ">Figure 4
<p>Teleoperation scheme involving two different force feedback rendering to the user: kinaesthetic and tactile feedback at fingerpads.</p> Full article ">Figure 5
<p>The experimental teleoperation setup.</p> Full article ">Figure 6
<p>Sequence of the proposed pick-and-place task.</p> Full article ">Figure 7
<p>Representative time plot of the position and force profiles during grasping in the HV condition.</p> Full article ">Figure 8
<p>Results of the pick-and-place tasks with respect to the correctly placed, fallen, or virtually broken objects (<b>left</b>). Plot of the correct rate results with circles and dotted lines showing the performance of each subject (<b>right</b>). On each box, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The ** symbol represents a statistically significant difference with <math display="inline"><semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.01</mn> </mrow> </semantics></math>.</p> Full article ">Figure 9
<p>Pick-and-place task results as average grasping force (<b>left</b>), peak grasping force (<b>middle</b>), and completion time (<b>right</b>). Only successful trials are considered. On each box, circles and dotted lines show the result of each subject, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The * and ** symbols represent a statistically significant difference with <math display="inline"><semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.05</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.01</mn> </mrow> </semantics></math>, respectively.</p> Full article ">
<p>The proposed system is composed of a hand exoskeleton integrating additional cutaneous force feedback actuators. The teleoperated robotic hand includes force sensors at the fingertips.</p> Full article ">Figure 2
<p>Detail of the leader interface consisting of the developed full upper limb exoskeleton integrated with haptic thimbles.</p> Full article ">Figure 3
<p>Detail of the follower interface implementing the robotic hand equipped with two sensitive force sensors (index and thumb).</p> Full article ">Figure 4
<p>Teleoperation scheme involving two different force feedback rendering to the user: kinaesthetic and tactile feedback at fingerpads.</p> Full article ">Figure 5
<p>The experimental teleoperation setup.</p> Full article ">Figure 6
<p>Sequence of the proposed pick-and-place task.</p> Full article ">Figure 7
<p>Representative time plot of the position and force profiles during grasping in the HV condition.</p> Full article ">Figure 8
<p>Results of the pick-and-place tasks with respect to the correctly placed, fallen, or virtually broken objects (<b>left</b>). Plot of the correct rate results with circles and dotted lines showing the performance of each subject (<b>right</b>). On each box, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The ** symbol represents a statistically significant difference with <math display="inline"><semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.01</mn> </mrow> </semantics></math>.</p> Full article ">Figure 9
<p>Pick-and-place task results as average grasping force (<b>left</b>), peak grasping force (<b>middle</b>), and completion time (<b>right</b>). Only successful trials are considered. On each box, circles and dotted lines show the result of each subject, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The * and ** symbols represent a statistically significant difference with <math display="inline"><semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.05</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>p</mi> <mo><</mo> <mn>0.01</mn> </mrow> </semantics></math>, respectively.</p> Full article ">
Open AccessArticle
PRF: A Program Reuse Framework for Automated Programming by Learning from Existing Robot Programs
by
Tyler Toner, Dawn M. Tilbury and Kira Barton
Robotics 2024, 13(8), 118; https://doi.org/10.3390/robotics13080118 - 6 Aug 2024
Abstract
This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data structure
[...] Read more.
This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data structure introduced in this work, to learn affordances, workspaces, and skills from historical data. Historical data comprise raw robot joint trajectories and descriptions of the robot task being completed. Given new tasks, motion classes are then used again to formulate an optimization problem capable of generating new open-loop, skill-based programs to complete the tasks. To cope with a lack of geometric models, a technique to learn safe workspaces from demonstrations is developed, allowing the risk of new programs to be estimated before execution. A new learnable motion primitive for redundant manipulators is introduced, called a redundancy dynamical movement primitive, which enables new end-effector goals to be reached while mimicking the whole-arm behavior of a demonstration. A mobile manipulator part transportation task is used throughout to illustrate each step of the framework.
Full article
(This article belongs to the Section Industrial Robots and Automation)
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Figure 1
<p>Schematic of the Program Reuse Framework. Sections of the paper are indicated in the title of each module, e.g., “Sec. 3” correponding to <a href="#sec3-robotics-13-00118" class="html-sec">Section 3</a>. programmer generates programs to complete tasks offline, using a ground truth knowledge base available only to the programmer; as a result, a historical dataset <math display="inline"><semantics> <mi mathvariant="bold">D</mi> </semantics></math> of programs is generated. To apply the PRF, an SME designs a motion class set <math display="inline"><semantics> <mi mathvariant="bold">M</mi> </semantics></math>, which is used to learn from <math display="inline"><semantics> <mi mathvariant="bold">D</mi> </semantics></math> to generate sets of affordances <math display="inline"><semantics> <mi mathvariant="bold">A</mi> </semantics></math>, skills <math display="inline"><semantics> <mi mathvariant="bold">K</mi> </semantics></math>, and safe workspaces <math display="inline"><semantics> <mo mathvariant="bold">Ω</mo> </semantics></math>, forming a learned knowledge base. When a new task specification is given <math display="inline"><semantics> <mi mathvariant="script">T</mi> </semantics></math>, <span class="html-small-caps">SkillPlan</span> uses the learned knowledge base to generate a new program to complete <math display="inline"><semantics> <mi mathvariant="script">T</mi> </semantics></math>.</p> Full article ">Figure 2
<p>Specification of an illustrative example: mobile manipulator part transportation. All possible tasks involve three types of locations (CNC, Printer, and Table) and two types of parts (Part1 and Part2). The CNC has two approaches (front and side) while the Printer (front) and Table (side) each have one. Both parts have two grasps (top and angle). Note that each approach or grasp is unique to a particular location or part, and shared names are incidental.</p> Full article ">Figure 3
<p>RDMP and DMP risk-based generalization comparison, part 1; see the continuation in <a href="#robotics-13-00118-f004" class="html-fig">Figure 4</a>. For four examples (two here and two in <a href="#robotics-13-00118-f004" class="html-fig">Figure 4</a>), we take a nominal demonstration and learn a workspace (<b>i</b>), DMP, and RDMP. Then, generalizing over a grid of new goals, we compute risk for both the DMP and RDMP (<b>ii</b>,<b>iii</b>). We observe that the RDMP enables either similar or superior (lower-risk) generalization. To capture the trajectory-level differences, we also consider the position trajectory of the robot’s elbow joint in (<b>iv</b>) and see that the RDMP remains closer to the demonstration, explaining its lower risk. Note that in <b>iii</b>, the appearance of violet is the intersection of the red DMP and blue RDMP histograms.</p> Full article ">Figure 4
<p>RDMP and DMP-risk-based generalization comparison, part 2; see the the first part and complete explanation in <a href="#robotics-13-00118-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 5
<p>PRF generalization performance over 50 randomized traces. In (<b>a</b>), blue values correspond to symbolically feasible afforded tasks while the red lines correspond to physically feasible afforded tasks. In (<b>b</b>), the green line corresponds physically feasible tasks. In each plot, the transparent area indicates the range of values across the random samples, while the solid line indicates the mean value.</p> Full article ">
<p>Schematic of the Program Reuse Framework. Sections of the paper are indicated in the title of each module, e.g., “Sec. 3” correponding to <a href="#sec3-robotics-13-00118" class="html-sec">Section 3</a>. programmer generates programs to complete tasks offline, using a ground truth knowledge base available only to the programmer; as a result, a historical dataset <math display="inline"><semantics> <mi mathvariant="bold">D</mi> </semantics></math> of programs is generated. To apply the PRF, an SME designs a motion class set <math display="inline"><semantics> <mi mathvariant="bold">M</mi> </semantics></math>, which is used to learn from <math display="inline"><semantics> <mi mathvariant="bold">D</mi> </semantics></math> to generate sets of affordances <math display="inline"><semantics> <mi mathvariant="bold">A</mi> </semantics></math>, skills <math display="inline"><semantics> <mi mathvariant="bold">K</mi> </semantics></math>, and safe workspaces <math display="inline"><semantics> <mo mathvariant="bold">Ω</mo> </semantics></math>, forming a learned knowledge base. When a new task specification is given <math display="inline"><semantics> <mi mathvariant="script">T</mi> </semantics></math>, <span class="html-small-caps">SkillPlan</span> uses the learned knowledge base to generate a new program to complete <math display="inline"><semantics> <mi mathvariant="script">T</mi> </semantics></math>.</p> Full article ">Figure 2
<p>Specification of an illustrative example: mobile manipulator part transportation. All possible tasks involve three types of locations (CNC, Printer, and Table) and two types of parts (Part1 and Part2). The CNC has two approaches (front and side) while the Printer (front) and Table (side) each have one. Both parts have two grasps (top and angle). Note that each approach or grasp is unique to a particular location or part, and shared names are incidental.</p> Full article ">Figure 3
<p>RDMP and DMP risk-based generalization comparison, part 1; see the continuation in <a href="#robotics-13-00118-f004" class="html-fig">Figure 4</a>. For four examples (two here and two in <a href="#robotics-13-00118-f004" class="html-fig">Figure 4</a>), we take a nominal demonstration and learn a workspace (<b>i</b>), DMP, and RDMP. Then, generalizing over a grid of new goals, we compute risk for both the DMP and RDMP (<b>ii</b>,<b>iii</b>). We observe that the RDMP enables either similar or superior (lower-risk) generalization. To capture the trajectory-level differences, we also consider the position trajectory of the robot’s elbow joint in (<b>iv</b>) and see that the RDMP remains closer to the demonstration, explaining its lower risk. Note that in <b>iii</b>, the appearance of violet is the intersection of the red DMP and blue RDMP histograms.</p> Full article ">Figure 4
<p>RDMP and DMP-risk-based generalization comparison, part 2; see the the first part and complete explanation in <a href="#robotics-13-00118-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 5
<p>PRF generalization performance over 50 randomized traces. In (<b>a</b>), blue values correspond to symbolically feasible afforded tasks while the red lines correspond to physically feasible afforded tasks. In (<b>b</b>), the green line corresponds physically feasible tasks. In each plot, the transparent area indicates the range of values across the random samples, while the solid line indicates the mean value.</p> Full article ">
Open AccessReview
Holistic Review of UAV-Centric Situational Awareness: Applications, Limitations, and Algorithmic Challenges
by
Somaiyeh MahmoudZadeh, Amirmehdi Yazdani, Yashar Kalantari, Bekir Ciftler, Fathi Aidarus and Mhd Omar Al Kadri
Robotics 2024, 13(8), 117; https://doi.org/10.3390/robotics13080117 - 29 Jul 2024
Abstract
This paper presents a comprehensive survey of UAV-centric situational awareness (SA), delineating its applications, limitations, and underlying algorithmic challenges. It highlights the pivotal role of advanced algorithmic and strategic insights, including sensor integration, robust communication frameworks, and sophisticated data processing methodologies. The paper
[...] Read more.
This paper presents a comprehensive survey of UAV-centric situational awareness (SA), delineating its applications, limitations, and underlying algorithmic challenges. It highlights the pivotal role of advanced algorithmic and strategic insights, including sensor integration, robust communication frameworks, and sophisticated data processing methodologies. The paper critically analyzes multifaceted challenges such as real-time data processing demands, adaptability in dynamic environments, and complexities introduced by advanced AI and machine learning techniques. Key contributions include a detailed exploration of UAV-centric SA’s transformative potential in industries such as precision agriculture, disaster management, and urban infrastructure monitoring, supported by case studies. In addition, the paper delves into algorithmic approaches for path planning and control, as well as strategies for multi-agent cooperative SA, addressing their respective challenges and future directions. Moreover, this paper discusses forthcoming technological advancements, such as energy-efficient AI solutions, aimed at overcoming current limitations. This holistic review provides valuable insights into the UAV-centric SA, establishing a foundation for future research and practical applications in this domain.
Full article
(This article belongs to the Special Issue UAV Systems and Swarm Robotics)
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<p>Endsley’s model of SA in the context of robotics.</p> Full article ">Figure 2
<p>UAV for capturing SA and EA.</p> Full article ">Figure 3
<p>Components of SA within UAV operations.</p> Full article ">Figure 4
<p>Example of the classified UAVs [<a href="#B4-robotics-13-00117" class="html-bibr">4</a>,<a href="#B6-robotics-13-00117" class="html-bibr">6</a>,<a href="#B33-robotics-13-00117" class="html-bibr">33</a>,<a href="#B34-robotics-13-00117" class="html-bibr">34</a>,<a href="#B35-robotics-13-00117" class="html-bibr">35</a>,<a href="#B36-robotics-13-00117" class="html-bibr">36</a>,<a href="#B37-robotics-13-00117" class="html-bibr">37</a>].</p> Full article ">Figure 5
<p>Popular multirotor UAVs for SA [<a href="#B44-robotics-13-00117" class="html-bibr">44</a>].</p> Full article ">Figure 5 Cont.
<p>Popular multirotor UAVs for SA [<a href="#B44-robotics-13-00117" class="html-bibr">44</a>].</p> Full article ">Figure 6
<p>Conceptual example of capturing SA using distributed sensors, on-board camera, and LiDAR.</p> Full article ">
<p>Endsley’s model of SA in the context of robotics.</p> Full article ">Figure 2
<p>UAV for capturing SA and EA.</p> Full article ">Figure 3
<p>Components of SA within UAV operations.</p> Full article ">Figure 4
<p>Example of the classified UAVs [<a href="#B4-robotics-13-00117" class="html-bibr">4</a>,<a href="#B6-robotics-13-00117" class="html-bibr">6</a>,<a href="#B33-robotics-13-00117" class="html-bibr">33</a>,<a href="#B34-robotics-13-00117" class="html-bibr">34</a>,<a href="#B35-robotics-13-00117" class="html-bibr">35</a>,<a href="#B36-robotics-13-00117" class="html-bibr">36</a>,<a href="#B37-robotics-13-00117" class="html-bibr">37</a>].</p> Full article ">Figure 5
<p>Popular multirotor UAVs for SA [<a href="#B44-robotics-13-00117" class="html-bibr">44</a>].</p> Full article ">Figure 5 Cont.
<p>Popular multirotor UAVs for SA [<a href="#B44-robotics-13-00117" class="html-bibr">44</a>].</p> Full article ">Figure 6
<p>Conceptual example of capturing SA using distributed sensors, on-board camera, and LiDAR.</p> Full article ">
Open AccessArticle
Enhanced Design of an Adaptive Anthropomorphic Finger through Integration of Modular Soft Actuators and Kinematic Modeling
by
Sheng-Guan Lin and Jen-Yuan (James) Chang
Robotics 2024, 13(8), 116; https://doi.org/10.3390/robotics13080116 - 28 Jul 2024
Abstract
This study introduces a novel modular soft actuator designed for an anthropomorphic robotic finger that addresses the need for adaptive behavior and precise joint-angle control. The key innovation is its modular design, which enables independent pressure regulation in each air chamber, thus achieving
[...] Read more.
This study introduces a novel modular soft actuator designed for an anthropomorphic robotic finger that addresses the need for adaptive behavior and precise joint-angle control. The key innovation is its modular design, which enables independent pressure regulation in each air chamber, thus achieving superior precision compared to traditional PneuNets soft actuators. A rigid skeleton is integrated to enhance force transmission and measurement capabilities and thus ensure effective force handling and transmission within each module. The versatility of the actuator is demonstrated through its adaptability in various scenarios, and its features include adaptive positional control achieved by modulating the inflation in each air chamber. This research includes kinematic and kinetostatic analyses to ensure precise control of joint angles and forces at the finger’s endpoint. Experimental results confirm the actuator’s excellent performance and adaptability, providing valuable insights for advancing soft-actuator technology. The findings suggest significant potential for this actuator in diverse applications, emphasizing its role in the future development of precise and adaptable robotic systems.
Full article
(This article belongs to the Special Issue Recent Trends and Advances on Robotics and Mechatronics within the IFToMM Technical Committee on Robotics and Mechatronics)
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<p>Comparison between the human finger (<b>left</b>) and the anthropomorphic MSA finger (<b>right</b>).</p> Full article ">Figure 2
<p>Detailed structure illustrations of the MSA and the anthropomorphic MSA finger.</p> Full article ">Figure 3
<p>A linkage representation is provided, illustrating a conventional dual-axis robotic finger (<b>left</b>) and an anthropomorphic finger equipped with the proposed MSAs (<b>right</b>).</p> Full article ">Figure 4
<p>The anthropomorphic soft actuator finger is described using D–H coordinates [<a href="#B30-robotics-13-00116" class="html-bibr">30</a>].</p> Full article ">Figure 5
<p>Treating the robotic finger as a six-axis robot, with each MSA consisting of three axes.</p> Full article ">Figure 6
<p>Graphical illustration of kinematic analyses on each MSA of the robotic finger.</p> Full article ">Figure 7
<p>Photograph showing the experimental setup.</p> Full article ">Figure 8
<p>Grasping of circular, square, and rectangular objects was tested using the proposed MSA finger, as shown in (<b>a</b>–<b>c</b>), as well as with the conventional PneuNets soft actuator. In comparison, conventional PneuNets soft actuators grasp the same objects, as shown in (<b>d</b>) for a circular object, (<b>e</b>) for a square object, and (<b>f</b>) for a rectangular object. The reachable workspace of the robot’s fingertip is depicted in (<b>g</b>).</p> Full article ">Figure 9
<p>Lateral-force-resistance test with (<b>a</b>) the proposed exoskeletal MSA and (<b>b</b>) a conventional PneuNets actuator.</p> Full article ">Figure 10
<p>The correlation between input pressure and resultant output angle of (<b>a</b>) MSA1 and (<b>b</b>) MSA2. The black dashed lines represent linear fitting.</p> Full article ">Figure 11
<p>Comparison of measured and predicted endpoint positions of the proposed exoskeletal MSA finger.</p> Full article ">Figure 12
<p>IK verifications for the (<b>a</b>) X (<b>b</b>) Y positions using forward kinematics (<b>top</b>) and with the method using 5th-degree polynomial fitting (<b>bottom</b>).</p> Full article ">Figure 13
<p>3D residuals with 4th-degree polynomial fitting for the (<b>a</b>) X and (<b>b</b>) Y positions, respectively. (<b>c</b>,<b>d</b>) are 2D residuals for the <span class="html-italic">X</span> and <span class="html-italic">Y</span> positions, respectively.</p> Full article ">Figure 14
<p>3D residuals with 5th-degree polynomial fitting for the (<b>a</b>) X and (<b>b</b>) Y positions, respectively. (<b>c</b>,<b>d</b>) are 2D residuals for the <span class="html-italic">X</span> and <span class="html-italic">Y</span> positions, respectively.</p> Full article ">Figure 15
<p>Force output as a function of applied pressure at various angles for (<b>a</b>) MSA1 and (<b>b</b>) MSA2.</p> Full article ">Figure 16
<p>The calculated (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics></math>, and (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>6</mn> </mrow> </msub> </mrow> </semantics></math> torques as a function of applied pressure and chamber rotating angle.</p> Full article ">Figure 17
<p>Photograph showing the experimental apparatus for force measurement.</p> Full article ">Figure 18
<p>Comparison of predicted and measured forces exerted by the proposed robotic finger, with error bars.</p> Full article ">
<p>Comparison between the human finger (<b>left</b>) and the anthropomorphic MSA finger (<b>right</b>).</p> Full article ">Figure 2
<p>Detailed structure illustrations of the MSA and the anthropomorphic MSA finger.</p> Full article ">Figure 3
<p>A linkage representation is provided, illustrating a conventional dual-axis robotic finger (<b>left</b>) and an anthropomorphic finger equipped with the proposed MSAs (<b>right</b>).</p> Full article ">Figure 4
<p>The anthropomorphic soft actuator finger is described using D–H coordinates [<a href="#B30-robotics-13-00116" class="html-bibr">30</a>].</p> Full article ">Figure 5
<p>Treating the robotic finger as a six-axis robot, with each MSA consisting of three axes.</p> Full article ">Figure 6
<p>Graphical illustration of kinematic analyses on each MSA of the robotic finger.</p> Full article ">Figure 7
<p>Photograph showing the experimental setup.</p> Full article ">Figure 8
<p>Grasping of circular, square, and rectangular objects was tested using the proposed MSA finger, as shown in (<b>a</b>–<b>c</b>), as well as with the conventional PneuNets soft actuator. In comparison, conventional PneuNets soft actuators grasp the same objects, as shown in (<b>d</b>) for a circular object, (<b>e</b>) for a square object, and (<b>f</b>) for a rectangular object. The reachable workspace of the robot’s fingertip is depicted in (<b>g</b>).</p> Full article ">Figure 9
<p>Lateral-force-resistance test with (<b>a</b>) the proposed exoskeletal MSA and (<b>b</b>) a conventional PneuNets actuator.</p> Full article ">Figure 10
<p>The correlation between input pressure and resultant output angle of (<b>a</b>) MSA1 and (<b>b</b>) MSA2. The black dashed lines represent linear fitting.</p> Full article ">Figure 11
<p>Comparison of measured and predicted endpoint positions of the proposed exoskeletal MSA finger.</p> Full article ">Figure 12
<p>IK verifications for the (<b>a</b>) X (<b>b</b>) Y positions using forward kinematics (<b>top</b>) and with the method using 5th-degree polynomial fitting (<b>bottom</b>).</p> Full article ">Figure 13
<p>3D residuals with 4th-degree polynomial fitting for the (<b>a</b>) X and (<b>b</b>) Y positions, respectively. (<b>c</b>,<b>d</b>) are 2D residuals for the <span class="html-italic">X</span> and <span class="html-italic">Y</span> positions, respectively.</p> Full article ">Figure 14
<p>3D residuals with 5th-degree polynomial fitting for the (<b>a</b>) X and (<b>b</b>) Y positions, respectively. (<b>c</b>,<b>d</b>) are 2D residuals for the <span class="html-italic">X</span> and <span class="html-italic">Y</span> positions, respectively.</p> Full article ">Figure 15
<p>Force output as a function of applied pressure at various angles for (<b>a</b>) MSA1 and (<b>b</b>) MSA2.</p> Full article ">Figure 16
<p>The calculated (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>e</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>5</mn> </mrow> </msub> </mrow> </semantics></math>, and (<b>f</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>m</mi> <mn>6</mn> </mrow> </msub> </mrow> </semantics></math> torques as a function of applied pressure and chamber rotating angle.</p> Full article ">Figure 17
<p>Photograph showing the experimental apparatus for force measurement.</p> Full article ">Figure 18
<p>Comparison of predicted and measured forces exerted by the proposed robotic finger, with error bars.</p> Full article ">
Open AccessArticle
Vision-Based Formation Control of Quadrotors Using a Bearing-Only Approach
by
David L. Ramírez-Parada, Héctor M. Becerra, Carlos A. Toro-Arcila and Gustavo Arechavaleta
Robotics 2024, 13(8), 115; https://doi.org/10.3390/robotics13080115 - 28 Jul 2024
Abstract
In this paper, we present a vision-based leader–follower strategy for formation control of multiple quadrotors. The leaders use a decoupled visual control scheme based on invariant features. The followers use a control scheme based only on bearing measurements, and a robust control is
[...] Read more.
In this paper, we present a vision-based leader–follower strategy for formation control of multiple quadrotors. The leaders use a decoupled visual control scheme based on invariant features. The followers use a control scheme based only on bearing measurements, and a robust control is introduced to deal with perturbations generated by the unknown movement of the leaders. Using this formulation, we study a geometrical pattern formation that can use the distance between the leaders to scale the formation and cross constrained spaces, such as a window. A condition is defined for which a formation has rigidity properties considering the constrained field of view of the cameras, such that invariance to translation and scaling is achieved. This condition allows us to specify a desired formation where the followers do not need to share information between them. Results obtained in a dynamic simulator and real experiments show the effectiveness of the approach.
Full article
(This article belongs to the Special Issue UAV Systems and Swarm Robotics)
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Figure 1
Figure 1
<p>Low-level quadrotor control and definition of reference frames. (<b>a</b>) Low-level quadrotor control. (<b>b</b>) Bebop 2 reference frames.</p> Full article ">Figure 2
<p>Estimation of bearing measurements using ArUco markers whose central point is represented as a ray beam within the unitary sphere.</p> Full article ">Figure 3
<p>Generic image−based visual servoing scheme for quadrotors, where the visual information is transformed through <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>(</mo> <mi>I</mi> <mo>,</mo> <msup> <mi>I</mi> <mo>*</mo> </msup> <mo>)</mo> </mrow> </semantics></math> to obtain invariant features or bearings.</p> Full article ">Figure 4
<p>Invariant image features for IBVS, drone rotation, and invariant distance. (<b>a</b>) Invariant image features for IBVS. (<b>b</b>) Drone’s rotation and invariant distance.</p> Full article ">Figure 5
<p>Geometric visualization of bearing-only control laws: the agents are depicted as black points, with the current bearings <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">g</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </semantics></math> represented by red arrows, the desired bearings <math display="inline"><semantics> <msubsup> <mi mathvariant="bold-italic">g</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mo>*</mo> </msubsup> </semantics></math> by green arrows, and the resulting velocity with their corresponding control law by a blue arrow. (<b>a</b>) Orthogonal projection control law (<a href="#FD17-robotics-13-00115" class="html-disp-formula">17</a>). (<b>b</b>) Difference in bearings’ control law (<a href="#FD18-robotics-13-00115" class="html-disp-formula">18</a>).</p> Full article ">Figure 6
<p>For an IBR network (encircled with two leaders <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </mrow> </semantics></math> and three followers <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">F</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi mathvariant="script">F</mi> <mn>3</mn> </msub> </mrow> </semantics></math>), adding a new agent requires setting up bearing links with at least two agents to maintain the IBR condition. In this case, the new agent <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>4</mn> </msub> </semantics></math> can join the fleet by following, for instance, any two (<math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math>), three (<math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>3</mn> </msub> </semantics></math>), or four agents (<math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>4</mn> </msub> </semantics></math>).</p> Full article ">Figure 7
<p>Performance of the invariant control laws including rotation control; the initial pose was rotated in yaw by 10 degrees. The 3D pose, velocity input, pose, and distance errors are shown. The blue camera frame represents the final pose, and the black one shows the starting pose. Both simulations used the same starting poses and fixed gains (<math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mi>v</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mi>ω</mi> </msub> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>). (<b>a</b>) Performance of the control law (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="false"> <mfrac> <mn>1</mn> <msub> <mi>s</mi> <mi>l</mi> </msub> </mfrac> </mstyle> </mrow> </semantics></math> using distances. (<b>b</b>) Performance of the control law (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <msubsup> <mi>s</mi> <mi>l</mi> <mn>3</mn> </msubsup> </mrow> </semantics></math> using the inverse of distances.</p> Full article ">Figure 8
<p>Comparison of a flying-free camera trajectory using two control laws in a 3D simulation, highlighting the superior performance of the inverse-distance control (98%) over distance-based control (2%). The black frames represent initial positions, the red lines represent trajectories using control law (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mn>1</mn> <msub> <mi>s</mi> <mi>l</mi> </msub> </mfrac> </mstyle> </mrow> </semantics></math>, the blue lines represent trajectories using control law (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <msubsup> <mi>s</mi> <mi>l</mi> <mn>3</mn> </msubsup> </mrow> </semantics></math>, and the red dot represents the desired and final positions for all simulations.</p> Full article ">Figure 9
<p>Performance comparison of bearing-only-based control laws. The visualization includes 3D pose, velocity input, pose error, and bearing errors. The final pose is marked by the blue frame, and the initial pose is indicated by the black frame. The simulations maintained consistent initial poses and utilized fixed linear and angular control gains with <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mi>v</mi> </msub> <mo>=</mo> <mn>1.3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mi>ω</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. (<b>a</b>) Performance of the control law (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>) based on orthogonal projection. (<b>b</b>) Performance of the control law (<a href="#FD19-robotics-13-00115" class="html-disp-formula">19</a>) based on the difference in bearings.</p> Full article ">Figure 10
<p>Performance of a bearing-only control law with simulated moving leader agents. The 3D pose, velocity input, pose errors, and bearing errors are shown, respectively. The initial configuration is depicted as black cameras and black dots, the trajectory and current position are shown in blue, and the desired configuration is represented by the red camera and royal blue dots. The control gains were set as <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mrow> <mi>k</mi> <mi>p</mi> </mrow> </msub> <mo>=</mo> <mn>2.3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> for both implementations and <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mi>ω</mi> </msub> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math> for the STSMC. (<b>a</b>) Performance of the control law given by (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>), i.e., without STSMC. (<b>b</b>) Performance of the control law given by (<a href="#FD21-robotics-13-00115" class="html-disp-formula">21</a>), i.e., with STSMC.</p> Full article ">Figure 11
<p>Environment simulated in Gazebo; there are two leaders, <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </semantics></math>, represented by red drones, and two followers, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math>, depicted in white. (<b>a</b>) Initial drone positions. (<b>b</b>) Desired drone positions.</p> Full article ">Figure 12
<p>Example of a smooth transition using the time intervals <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>5</mn> <mo>)</mo> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>6.5</mn> <mo>,</mo> <mn>8</mn> <mo>)</mo> </mrow> </semantics></math>, employing the function in (<a href="#FD24-robotics-13-00115" class="html-disp-formula">24</a>).</p> Full article ">Figure 13
<p>Three−dimensional paths followed by the quadrotors in the Gazebo simulation. Leaders <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </semantics></math> correspond to drones 1 and 2, while followers <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math> correspond to drones 3 and 4.</p> Full article ">Figure 14
<p>A series of snapshots showing different instants in the video of the Gazebo simulation, available at <a href="https://youtu.be/FIPAQ5luHYw" target="_blank">https://youtu.be/FIPAQ5luHYw</a> (accessed on 24 July 2024). The video is approximately <math display="inline"><semantics> <mrow> <mn>35.7</mn> </mrow> </semantics></math> s long. (<b>a</b>) At 0 s. (<b>b</b>) At <math display="inline"><semantics> <mrow> <mn>7.1</mn> </mrow> </semantics></math> s. (<b>c</b>) At <math display="inline"><semantics> <mrow> <mn>14.2</mn> </mrow> </semantics></math> s. (<b>d</b>) At <math display="inline"><semantics> <mrow> <mn>17.8</mn> </mrow> </semantics></math> s. (<b>e</b>) At 25 s. (<b>f</b>) At <math display="inline"><semantics> <mrow> <mn>35.7</mn> </mrow> </semantics></math> s.</p> Full article ">Figure 14 Cont.
<p>A series of snapshots showing different instants in the video of the Gazebo simulation, available at <a href="https://youtu.be/FIPAQ5luHYw" target="_blank">https://youtu.be/FIPAQ5luHYw</a> (accessed on 24 July 2024). The video is approximately <math display="inline"><semantics> <mrow> <mn>35.7</mn> </mrow> </semantics></math> s long. (<b>a</b>) At 0 s. (<b>b</b>) At <math display="inline"><semantics> <mrow> <mn>7.1</mn> </mrow> </semantics></math> s. (<b>c</b>) At <math display="inline"><semantics> <mrow> <mn>14.2</mn> </mrow> </semantics></math> s. (<b>d</b>) At <math display="inline"><semantics> <mrow> <mn>17.8</mn> </mrow> </semantics></math> s. (<b>e</b>) At 25 s. (<b>f</b>) At <math display="inline"><semantics> <mrow> <mn>35.7</mn> </mrow> </semantics></math> s.</p> Full article ">Figure 15
<p>Plots of important variables in the Gazebo simulation. (<b>a</b>) Velocity inputs from control law for leaders (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <msubsup> <mi>s</mi> <mi>l</mi> <mn>3</mn> </msubsup> </mrow> </semantics></math> and followers (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>). (<b>b</b>) Feedback error for the control law. (<b>c</b>) Normalized pixel error computed as the average error between matched points in the current and target images. (<b>d</b>) Adaptive gains for every drone.</p> Full article ">Figure 16
<p>Desired images for the experiment of the leaders using the control law (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <msubsup> <mi>s</mi> <mi>l</mi> <mn>3</mn> </msubsup> </mrow> </semantics></math> along with (<a href="#FD15-robotics-13-00115" class="html-disp-formula">15</a>). (<b>a</b>) First desired image for leader <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math>. (<b>b</b>) First desired image for leader <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </semantics></math>. (<b>c</b>) Second desired image for leader <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math>. (<b>d</b>) Second desired image for leader <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </semantics></math>.</p> Full article ">Figure 17
<p>Snapshots taken from the video of the experiment for the leaders at different times <a href="https://youtu.be/eyNs8avFves" target="_blank">https://youtu.be/eyNs8avFves</a> (accessed on 24 July 2024). The video lasts about <math display="inline"><semantics> <mrow> <mn>47.4</mn> </mrow> </semantics></math> s. (<b>a</b>) At 0 s. (<b>b</b>) At <math display="inline"><semantics> <mrow> <mn>6.9</mn> </mrow> </semantics></math> s. (<b>c</b>) At <math display="inline"><semantics> <mrow> <mn>9.7</mn> </mrow> </semantics></math> s. (<b>d</b>) At <math display="inline"><semantics> <mrow> <mn>16.7</mn> </mrow> </semantics></math> s. (<b>e</b>) At <math display="inline"><semantics> <mrow> <mn>23.7</mn> </mrow> </semantics></math> s. (<b>f</b>) At <math display="inline"><semantics> <mrow> <mn>34.9</mn> </mrow> </semantics></math> s.</p> Full article ">Figure 18
<p>Plots of important variables for the experiment with the two Bebop 2 quadrotors as leaders. (<b>a</b>) Input velocities from control law for leaders (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <msubsup> <mi>s</mi> <mi>l</mi> <mn>3</mn> </msubsup> </mrow> </semantics></math>. (<b>b</b>) Feedback error for the control law. (<b>c</b>) Normalized pixel error computed as the average error between matched points in the current and target images.</p> Full article ">Figure 19
<p>Illustration of the paths followed by the DDRs acting as leaders, ensuring alignment of their ArUco markers to be detected by the Bebop 2 followers.</p> Full article ">Figure 20
<p>Hovering initial position and desired images for the Bebop 2 followers, used in their control law using the orthogonal projection (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>). (<b>a</b>) Desired image for follower <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>1</mn> </msub> </semantics></math>. (<b>b</b>) Desired image for follower <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math>.</p> Full article ">Figure 21
<p>Snapshots taken from a video of the experiment for the followers at different times <a href="https://youtu.be/xyGV7B5biZU" target="_blank">https://youtu.be/xyGV7B5biZU</a> (accessed on 24 July 2024). The video lasts about <math display="inline"><semantics> <mrow> <mn>78.5</mn> </mrow> </semantics></math> s. (<b>a</b>) At 0 s. (<b>b</b>) At <math display="inline"><semantics> <mrow> <mn>12.5</mn> </mrow> </semantics></math> s. (<b>c</b>) At <math display="inline"><semantics> <mrow> <mn>28.2</mn> </mrow> </semantics></math> s. (<b>d</b>) At <math display="inline"><semantics> <mrow> <mn>34.5</mn> </mrow> </semantics></math> s. (<b>e</b>) At <math display="inline"><semantics> <mrow> <mn>53.4</mn> </mrow> </semantics></math> s. (<b>f</b>) At <math display="inline"><semantics> <mrow> <mn>72.2</mn> </mrow> </semantics></math> s.</p> Full article ">Figure 22
<p>Plots of important variables for the experiment with the two Bebop 2 quadrotors as followers. (<b>a</b>) Velocity inputs from the control law for followers (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>). (<b>b</b>) Feedback error for the control law. (<b>c</b>) Normalized pixel error computed as the average error between matched points in the current and target images.</p> Full article ">Figure 22 Cont.
<p>Plots of important variables for the experiment with the two Bebop 2 quadrotors as followers. (<b>a</b>) Velocity inputs from the control law for followers (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>). (<b>b</b>) Feedback error for the control law. (<b>c</b>) Normalized pixel error computed as the average error between matched points in the current and target images.</p> Full article ">
<p>Low-level quadrotor control and definition of reference frames. (<b>a</b>) Low-level quadrotor control. (<b>b</b>) Bebop 2 reference frames.</p> Full article ">Figure 2
<p>Estimation of bearing measurements using ArUco markers whose central point is represented as a ray beam within the unitary sphere.</p> Full article ">Figure 3
<p>Generic image−based visual servoing scheme for quadrotors, where the visual information is transformed through <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>(</mo> <mi>I</mi> <mo>,</mo> <msup> <mi>I</mi> <mo>*</mo> </msup> <mo>)</mo> </mrow> </semantics></math> to obtain invariant features or bearings.</p> Full article ">Figure 4
<p>Invariant image features for IBVS, drone rotation, and invariant distance. (<b>a</b>) Invariant image features for IBVS. (<b>b</b>) Drone’s rotation and invariant distance.</p> Full article ">Figure 5
<p>Geometric visualization of bearing-only control laws: the agents are depicted as black points, with the current bearings <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">g</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </semantics></math> represented by red arrows, the desired bearings <math display="inline"><semantics> <msubsup> <mi mathvariant="bold-italic">g</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> <mo>*</mo> </msubsup> </semantics></math> by green arrows, and the resulting velocity with their corresponding control law by a blue arrow. (<b>a</b>) Orthogonal projection control law (<a href="#FD17-robotics-13-00115" class="html-disp-formula">17</a>). (<b>b</b>) Difference in bearings’ control law (<a href="#FD18-robotics-13-00115" class="html-disp-formula">18</a>).</p> Full article ">Figure 6
<p>For an IBR network (encircled with two leaders <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </mrow> </semantics></math> and three followers <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">F</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi mathvariant="script">F</mi> <mn>3</mn> </msub> </mrow> </semantics></math>), adding a new agent requires setting up bearing links with at least two agents to maintain the IBR condition. In this case, the new agent <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>4</mn> </msub> </semantics></math> can join the fleet by following, for instance, any two (<math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math>), three (<math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>3</mn> </msub> </semantics></math>), or four agents (<math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>4</mn> </msub> </semantics></math>).</p> Full article ">Figure 7
<p>Performance of the invariant control laws including rotation control; the initial pose was rotated in yaw by 10 degrees. The 3D pose, velocity input, pose, and distance errors are shown. The blue camera frame represents the final pose, and the black one shows the starting pose. Both simulations used the same starting poses and fixed gains (<math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mi>v</mi> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mi>ω</mi> </msub> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>). (<b>a</b>) Performance of the control law (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="false"> <mfrac> <mn>1</mn> <msub> <mi>s</mi> <mi>l</mi> </msub> </mfrac> </mstyle> </mrow> </semantics></math> using distances. (<b>b</b>) Performance of the control law (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <msubsup> <mi>s</mi> <mi>l</mi> <mn>3</mn> </msubsup> </mrow> </semantics></math> using the inverse of distances.</p> Full article ">Figure 8
<p>Comparison of a flying-free camera trajectory using two control laws in a 3D simulation, highlighting the superior performance of the inverse-distance control (98%) over distance-based control (2%). The black frames represent initial positions, the red lines represent trajectories using control law (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mn>1</mn> <msub> <mi>s</mi> <mi>l</mi> </msub> </mfrac> </mstyle> </mrow> </semantics></math>, the blue lines represent trajectories using control law (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <msubsup> <mi>s</mi> <mi>l</mi> <mn>3</mn> </msubsup> </mrow> </semantics></math>, and the red dot represents the desired and final positions for all simulations.</p> Full article ">Figure 9
<p>Performance comparison of bearing-only-based control laws. The visualization includes 3D pose, velocity input, pose error, and bearing errors. The final pose is marked by the blue frame, and the initial pose is indicated by the black frame. The simulations maintained consistent initial poses and utilized fixed linear and angular control gains with <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mi>v</mi> </msub> <mo>=</mo> <mn>1.3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mi>ω</mi> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. (<b>a</b>) Performance of the control law (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>) based on orthogonal projection. (<b>b</b>) Performance of the control law (<a href="#FD19-robotics-13-00115" class="html-disp-formula">19</a>) based on the difference in bearings.</p> Full article ">Figure 10
<p>Performance of a bearing-only control law with simulated moving leader agents. The 3D pose, velocity input, pose errors, and bearing errors are shown, respectively. The initial configuration is depicted as black cameras and black dots, the trajectory and current position are shown in blue, and the desired configuration is represented by the red camera and royal blue dots. The control gains were set as <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mrow> <mi>k</mi> <mi>p</mi> </mrow> </msub> <mo>=</mo> <mn>2.3</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> for both implementations and <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mi>ω</mi> </msub> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math> for the STSMC. (<b>a</b>) Performance of the control law given by (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>), i.e., without STSMC. (<b>b</b>) Performance of the control law given by (<a href="#FD21-robotics-13-00115" class="html-disp-formula">21</a>), i.e., with STSMC.</p> Full article ">Figure 11
<p>Environment simulated in Gazebo; there are two leaders, <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </semantics></math>, represented by red drones, and two followers, <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math>, depicted in white. (<b>a</b>) Initial drone positions. (<b>b</b>) Desired drone positions.</p> Full article ">Figure 12
<p>Example of a smooth transition using the time intervals <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>5</mn> <mo>)</mo> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>6.5</mn> <mo>,</mo> <mn>8</mn> <mo>)</mo> </mrow> </semantics></math>, employing the function in (<a href="#FD24-robotics-13-00115" class="html-disp-formula">24</a>).</p> Full article ">Figure 13
<p>Three−dimensional paths followed by the quadrotors in the Gazebo simulation. Leaders <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </semantics></math> correspond to drones 1 and 2, while followers <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math> correspond to drones 3 and 4.</p> Full article ">Figure 14
<p>A series of snapshots showing different instants in the video of the Gazebo simulation, available at <a href="https://youtu.be/FIPAQ5luHYw" target="_blank">https://youtu.be/FIPAQ5luHYw</a> (accessed on 24 July 2024). The video is approximately <math display="inline"><semantics> <mrow> <mn>35.7</mn> </mrow> </semantics></math> s long. (<b>a</b>) At 0 s. (<b>b</b>) At <math display="inline"><semantics> <mrow> <mn>7.1</mn> </mrow> </semantics></math> s. (<b>c</b>) At <math display="inline"><semantics> <mrow> <mn>14.2</mn> </mrow> </semantics></math> s. (<b>d</b>) At <math display="inline"><semantics> <mrow> <mn>17.8</mn> </mrow> </semantics></math> s. (<b>e</b>) At 25 s. (<b>f</b>) At <math display="inline"><semantics> <mrow> <mn>35.7</mn> </mrow> </semantics></math> s.</p> Full article ">Figure 14 Cont.
<p>A series of snapshots showing different instants in the video of the Gazebo simulation, available at <a href="https://youtu.be/FIPAQ5luHYw" target="_blank">https://youtu.be/FIPAQ5luHYw</a> (accessed on 24 July 2024). The video is approximately <math display="inline"><semantics> <mrow> <mn>35.7</mn> </mrow> </semantics></math> s long. (<b>a</b>) At 0 s. (<b>b</b>) At <math display="inline"><semantics> <mrow> <mn>7.1</mn> </mrow> </semantics></math> s. (<b>c</b>) At <math display="inline"><semantics> <mrow> <mn>14.2</mn> </mrow> </semantics></math> s. (<b>d</b>) At <math display="inline"><semantics> <mrow> <mn>17.8</mn> </mrow> </semantics></math> s. (<b>e</b>) At 25 s. (<b>f</b>) At <math display="inline"><semantics> <mrow> <mn>35.7</mn> </mrow> </semantics></math> s.</p> Full article ">Figure 15
<p>Plots of important variables in the Gazebo simulation. (<b>a</b>) Velocity inputs from control law for leaders (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <msubsup> <mi>s</mi> <mi>l</mi> <mn>3</mn> </msubsup> </mrow> </semantics></math> and followers (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>). (<b>b</b>) Feedback error for the control law. (<b>c</b>) Normalized pixel error computed as the average error between matched points in the current and target images. (<b>d</b>) Adaptive gains for every drone.</p> Full article ">Figure 16
<p>Desired images for the experiment of the leaders using the control law (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <msubsup> <mi>s</mi> <mi>l</mi> <mn>3</mn> </msubsup> </mrow> </semantics></math> along with (<a href="#FD15-robotics-13-00115" class="html-disp-formula">15</a>). (<b>a</b>) First desired image for leader <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math>. (<b>b</b>) First desired image for leader <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </semantics></math>. (<b>c</b>) Second desired image for leader <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>1</mn> </msub> </semantics></math>. (<b>d</b>) Second desired image for leader <math display="inline"><semantics> <msub> <mi mathvariant="script">L</mi> <mn>2</mn> </msub> </semantics></math>.</p> Full article ">Figure 17
<p>Snapshots taken from the video of the experiment for the leaders at different times <a href="https://youtu.be/eyNs8avFves" target="_blank">https://youtu.be/eyNs8avFves</a> (accessed on 24 July 2024). The video lasts about <math display="inline"><semantics> <mrow> <mn>47.4</mn> </mrow> </semantics></math> s. (<b>a</b>) At 0 s. (<b>b</b>) At <math display="inline"><semantics> <mrow> <mn>6.9</mn> </mrow> </semantics></math> s. (<b>c</b>) At <math display="inline"><semantics> <mrow> <mn>9.7</mn> </mrow> </semantics></math> s. (<b>d</b>) At <math display="inline"><semantics> <mrow> <mn>16.7</mn> </mrow> </semantics></math> s. (<b>e</b>) At <math display="inline"><semantics> <mrow> <mn>23.7</mn> </mrow> </semantics></math> s. (<b>f</b>) At <math display="inline"><semantics> <mrow> <mn>34.9</mn> </mrow> </semantics></math> s.</p> Full article ">Figure 18
<p>Plots of important variables for the experiment with the two Bebop 2 quadrotors as leaders. (<b>a</b>) Input velocities from control law for leaders (<a href="#FD14-robotics-13-00115" class="html-disp-formula">14</a>) with <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mo>−</mo> <msubsup> <mi>s</mi> <mi>l</mi> <mn>3</mn> </msubsup> </mrow> </semantics></math>. (<b>b</b>) Feedback error for the control law. (<b>c</b>) Normalized pixel error computed as the average error between matched points in the current and target images.</p> Full article ">Figure 19
<p>Illustration of the paths followed by the DDRs acting as leaders, ensuring alignment of their ArUco markers to be detected by the Bebop 2 followers.</p> Full article ">Figure 20
<p>Hovering initial position and desired images for the Bebop 2 followers, used in their control law using the orthogonal projection (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>). (<b>a</b>) Desired image for follower <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>1</mn> </msub> </semantics></math>. (<b>b</b>) Desired image for follower <math display="inline"><semantics> <msub> <mi mathvariant="script">F</mi> <mn>2</mn> </msub> </semantics></math>.</p> Full article ">Figure 21
<p>Snapshots taken from a video of the experiment for the followers at different times <a href="https://youtu.be/xyGV7B5biZU" target="_blank">https://youtu.be/xyGV7B5biZU</a> (accessed on 24 July 2024). The video lasts about <math display="inline"><semantics> <mrow> <mn>78.5</mn> </mrow> </semantics></math> s. (<b>a</b>) At 0 s. (<b>b</b>) At <math display="inline"><semantics> <mrow> <mn>12.5</mn> </mrow> </semantics></math> s. (<b>c</b>) At <math display="inline"><semantics> <mrow> <mn>28.2</mn> </mrow> </semantics></math> s. (<b>d</b>) At <math display="inline"><semantics> <mrow> <mn>34.5</mn> </mrow> </semantics></math> s. (<b>e</b>) At <math display="inline"><semantics> <mrow> <mn>53.4</mn> </mrow> </semantics></math> s. (<b>f</b>) At <math display="inline"><semantics> <mrow> <mn>72.2</mn> </mrow> </semantics></math> s.</p> Full article ">Figure 22
<p>Plots of important variables for the experiment with the two Bebop 2 quadrotors as followers. (<b>a</b>) Velocity inputs from the control law for followers (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>). (<b>b</b>) Feedback error for the control law. (<b>c</b>) Normalized pixel error computed as the average error between matched points in the current and target images.</p> Full article ">Figure 22 Cont.
<p>Plots of important variables for the experiment with the two Bebop 2 quadrotors as followers. (<b>a</b>) Velocity inputs from the control law for followers (<a href="#FD16-robotics-13-00115" class="html-disp-formula">16</a>). (<b>b</b>) Feedback error for the control law. (<b>c</b>) Normalized pixel error computed as the average error between matched points in the current and target images.</p> Full article ">
Open AccessArticle
Fixed-Wing UAV Pose Estimation Using a Self-Organizing Map and Deep Learning
by
Nuno Pessanha Santos
Robotics 2024, 13(8), 114; https://doi.org/10.3390/robotics13080114 - 27 Jul 2024
Abstract
In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and
[...] Read more.
In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure.
Full article
(This article belongs to the Special Issue UAV Systems and Swarm Robotics)
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Figure 1
<p>Standard mission profile (<b>left</b>) and typical trajectory state machine (<b>right</b>) [<a href="#B24-robotics-13-00114" class="html-bibr">24</a>].</p> Full article ">Figure 2
<p>Simplified system architecture.</p> Full article ">Figure 3
<p>System architecture with a representation of the used variables.</p> Full article ">Figure 4
<p>Used UAV CAD model illustration.</p> Full article ">Figure 5
<p>Camera and UAV reference frames.</p> Full article ">Figure 6
<p>Example of generated UAV binary images.</p> Full article ">Figure 7
<p>Example I of obtained clustering maps using SOM after 250 iterations: <math display="inline"><semantics> <mrow> <mn>2</mn> <mo>×</mo> <mn>2</mn> </mrow> </semantics></math> grid (<b>left</b>), <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> grid (<b>center</b>) and <math display="inline"><semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics></math> grid (<b>right</b>). The dots represent the neuron positions according to their weights <math display="inline"><semantics> <mi mathvariant="bold">W</mi> </semantics></math> (output space).</p> Full article ">Figure 8
<p>Example II of obtained clustering maps using SOM after 250 iterations: <math display="inline"><semantics> <mrow> <mn>2</mn> <mo>×</mo> <mn>2</mn> </mrow> </semantics></math> grid (<b>left</b>), <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> grid (<b>center</b>) and <math display="inline"><semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics></math> grid (<b>right</b>). The dots represent the neuron positions according to their weights <math display="inline"><semantics> <mi mathvariant="bold">W</mi> </semantics></math> (output space).</p> Full article ">Figure 9
<p>Example of the obtained sample hits (<b>left</b>), where the numbers indicate the number of input vectors, and neighbor distances (<b>right</b>), where the red lines depict the connections between neighboring neurons for the <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> grid shown in <a href="#robotics-13-00114-f007" class="html-fig">Figure 7</a> center. The colors indicate the distances, with darker colors representing larger distances and lighter colors representing smaller distances.</p> Full article ">Figure 10
<p>Used translation estimation DNN structure.</p> Full article ">Figure 11
<p>Used orientation estimation DNN structure.</p> Full article ">Figure 12
<p>Example of a similar topology shown by a UAV symmetric pose.</p> Full article ">Figure 13
<p>Translation error boxplot in meters.</p> Full article ">Figure 14
<p>Orientation error histogram in degrees.</p> Full article ">Figure 15
<p>Examples of pose estimation using the proposed architecture—Original image (<b>left</b>), SOM output (<b>center</b>), and pose estimation (<b>right</b>). The orientation error for (A3) was 30.6 degrees, for (B3) 3.3 degrees, for (C3) 22.2 degrees, and for (D3) 14.4 degrees.</p> Full article ">Figure 16
<p>Orientation error histogram at 5 m when varying the Gaussian noise SD (degrees).</p> Full article ">Figure 17
<p>Obtained loss during the translation DNN training when removing network layers, as described in <a href="#robotics-13-00114-t007" class="html-table">Table 7</a>.</p> Full article ">Figure 18
<p>Obtained loss during the orientation DNN training when removing network layers, as described in <a href="#robotics-13-00114-t008" class="html-table">Table 8</a>.</p> Full article ">Figure 19
<p>Qualitative analysis example: Real captured frame (<b>left</b>) and BS obtained frame (<b>right</b>).</p> Full article ">Figure 20
<p>Real captured frames obtained clustering maps using SOM with 9 neurons (<math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> grid) after 250 iterations (<b>left</b>) and obtained estimation pose rendering using the network trained after 50,000 iterations (<b>right</b>).</p> Full article ">
<p>Standard mission profile (<b>left</b>) and typical trajectory state machine (<b>right</b>) [<a href="#B24-robotics-13-00114" class="html-bibr">24</a>].</p> Full article ">Figure 2
<p>Simplified system architecture.</p> Full article ">Figure 3
<p>System architecture with a representation of the used variables.</p> Full article ">Figure 4
<p>Used UAV CAD model illustration.</p> Full article ">Figure 5
<p>Camera and UAV reference frames.</p> Full article ">Figure 6
<p>Example of generated UAV binary images.</p> Full article ">Figure 7
<p>Example I of obtained clustering maps using SOM after 250 iterations: <math display="inline"><semantics> <mrow> <mn>2</mn> <mo>×</mo> <mn>2</mn> </mrow> </semantics></math> grid (<b>left</b>), <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> grid (<b>center</b>) and <math display="inline"><semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics></math> grid (<b>right</b>). The dots represent the neuron positions according to their weights <math display="inline"><semantics> <mi mathvariant="bold">W</mi> </semantics></math> (output space).</p> Full article ">Figure 8
<p>Example II of obtained clustering maps using SOM after 250 iterations: <math display="inline"><semantics> <mrow> <mn>2</mn> <mo>×</mo> <mn>2</mn> </mrow> </semantics></math> grid (<b>left</b>), <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> grid (<b>center</b>) and <math display="inline"><semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics></math> grid (<b>right</b>). The dots represent the neuron positions according to their weights <math display="inline"><semantics> <mi mathvariant="bold">W</mi> </semantics></math> (output space).</p> Full article ">Figure 9
<p>Example of the obtained sample hits (<b>left</b>), where the numbers indicate the number of input vectors, and neighbor distances (<b>right</b>), where the red lines depict the connections between neighboring neurons for the <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> grid shown in <a href="#robotics-13-00114-f007" class="html-fig">Figure 7</a> center. The colors indicate the distances, with darker colors representing larger distances and lighter colors representing smaller distances.</p> Full article ">Figure 10
<p>Used translation estimation DNN structure.</p> Full article ">Figure 11
<p>Used orientation estimation DNN structure.</p> Full article ">Figure 12
<p>Example of a similar topology shown by a UAV symmetric pose.</p> Full article ">Figure 13
<p>Translation error boxplot in meters.</p> Full article ">Figure 14
<p>Orientation error histogram in degrees.</p> Full article ">Figure 15
<p>Examples of pose estimation using the proposed architecture—Original image (<b>left</b>), SOM output (<b>center</b>), and pose estimation (<b>right</b>). The orientation error for (A3) was 30.6 degrees, for (B3) 3.3 degrees, for (C3) 22.2 degrees, and for (D3) 14.4 degrees.</p> Full article ">Figure 16
<p>Orientation error histogram at 5 m when varying the Gaussian noise SD (degrees).</p> Full article ">Figure 17
<p>Obtained loss during the translation DNN training when removing network layers, as described in <a href="#robotics-13-00114-t007" class="html-table">Table 7</a>.</p> Full article ">Figure 18
<p>Obtained loss during the orientation DNN training when removing network layers, as described in <a href="#robotics-13-00114-t008" class="html-table">Table 8</a>.</p> Full article ">Figure 19
<p>Qualitative analysis example: Real captured frame (<b>left</b>) and BS obtained frame (<b>right</b>).</p> Full article ">Figure 20
<p>Real captured frames obtained clustering maps using SOM with 9 neurons (<math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> grid) after 250 iterations (<b>left</b>) and obtained estimation pose rendering using the network trained after 50,000 iterations (<b>right</b>).</p> Full article ">
Open AccessArticle
Learning to Walk with Adaptive Feet
by
Antonello Scaldaferri, Franco Angelini and Manolo Garabini
Robotics 2024, 13(8), 113; https://doi.org/10.3390/robotics13080113 - 24 Jul 2024
Abstract
In recent years, tasks regarding autonomous mobility favoredthe use of legged robots rather than wheeled ones thanks to their higher mobility on rough and uneven terrains. This comes at the cost of more complex motion planners and controllers to ensure robot stability and
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In recent years, tasks regarding autonomous mobility favoredthe use of legged robots rather than wheeled ones thanks to their higher mobility on rough and uneven terrains. This comes at the cost of more complex motion planners and controllers to ensure robot stability and balance. However, in the case of quadrupedal robots, balancing is simpler than it is for bipeds thanks to their larger support polygons. Until a few years ago, most scientists and engineers addressed the quadrupedal locomotion problem with model-based approaches, which require a great deal of modeling expertise. A new trend is the use of data-driven methods, which seem to be quite promising and have shown great results. These methods do not require any modeling effort, but they suffer from computational limitations dictated by the hardware resources used. However, only the design phase of these algorithms requires large computing resources (controller training); their execution in the operational phase (deployment), takes place in real time on common processors. Moreover, adaptive feet capable of sensing terrain profile information have been designed and have shown great performance. Still, no dynamic locomotion control method has been specifically designed to leverage the advantages and supplementary information provided by this type of adaptive feet. In this work, we investigate the use and evaluate the performance of different end-to-end control policies trained via reinforcement learning algorithms specifically designed and trained to work on quadrupedal robots equipped with passive adaptive feet for their dynamic locomotion control over a diverse set of terrains. We examine how the addition of the haptic perception of the terrain affects the locomotion performance.
Full article
(This article belongs to the Special Issue Applications of Neural Networks in Robot Control)
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<p>Quadrupedal robot dynamic locomotion with adaptive feet.</p> Full article ">Figure 2
<p>Differences between hybrid [<a href="#B15-robotics-13-00113" class="html-bibr">15</a>] and end-to-end [<a href="#B14-robotics-13-00113" class="html-bibr">14</a>] approaches.</p> Full article ">Figure 3
<p>Proposed approximations of the SoftFoot-Q [<a href="#B10-robotics-13-00113" class="html-bibr">10</a>] (<b>a</b>) model: (<b>b</b>) Adaptive Flat Foot (AFF) and (<b>c</b>) Adaptive Open Foot (AOF). The passive DoF rotation axes are highlighted in red.</p> Full article ">Figure 4
<p>Reinforcement learning environments.</p> Full article ">Figure 5
<p>Terrain setup for the rough terrain environment.</p> Full article ">Figure 6
<p>Robot-centric elevation map with sampled point coordinates.</p> Full article ">Figure 7
<p>Reward trends of the different training sessions. AFF stands for Adaptive Flat Feet (<a href="#robotics-13-00113-f003" class="html-fig">Figure 3</a>b), while AOF stands for Adaptive Open Feet (<a href="#robotics-13-00113-f003" class="html-fig">Figure 3</a>c).</p> Full article ">Figure 8
<p>Performance of the different robot setups tested for the flat terrain locomotion task.</p> Full article ">Figure 9
<p>Performance of the different robot setups tested for the rough terrain locomotion task.</p> Full article ">Figure 10
<p>Performance evaluation when random pushes are applied to the robot base. The disturbance is applied at 8.75 s. The mean and the standard deviation were computed by simulating 1024 robots in parallel, with each one following a random commanded base twist reference.</p> Full article ">Figure 11
<p>Photo sequence of the P-AFF policy on stairs.</p> Full article ">Figure 12
<p>Feet contact schedule of the P-AFF policy on stairs.</p> Full article ">Figure 13
<p>Photo sequence of the PH-AOF policy on a rough slope.</p> Full article ">Figure 14
<p>Feet contact schedule of the PH-AOF policy on a rough slope.</p> Full article ">Figure 15
<p>Foot orientation problem.</p> Full article ">
<p>Quadrupedal robot dynamic locomotion with adaptive feet.</p> Full article ">Figure 2
<p>Differences between hybrid [<a href="#B15-robotics-13-00113" class="html-bibr">15</a>] and end-to-end [<a href="#B14-robotics-13-00113" class="html-bibr">14</a>] approaches.</p> Full article ">Figure 3
<p>Proposed approximations of the SoftFoot-Q [<a href="#B10-robotics-13-00113" class="html-bibr">10</a>] (<b>a</b>) model: (<b>b</b>) Adaptive Flat Foot (AFF) and (<b>c</b>) Adaptive Open Foot (AOF). The passive DoF rotation axes are highlighted in red.</p> Full article ">Figure 4
<p>Reinforcement learning environments.</p> Full article ">Figure 5
<p>Terrain setup for the rough terrain environment.</p> Full article ">Figure 6
<p>Robot-centric elevation map with sampled point coordinates.</p> Full article ">Figure 7
<p>Reward trends of the different training sessions. AFF stands for Adaptive Flat Feet (<a href="#robotics-13-00113-f003" class="html-fig">Figure 3</a>b), while AOF stands for Adaptive Open Feet (<a href="#robotics-13-00113-f003" class="html-fig">Figure 3</a>c).</p> Full article ">Figure 8
<p>Performance of the different robot setups tested for the flat terrain locomotion task.</p> Full article ">Figure 9
<p>Performance of the different robot setups tested for the rough terrain locomotion task.</p> Full article ">Figure 10
<p>Performance evaluation when random pushes are applied to the robot base. The disturbance is applied at 8.75 s. The mean and the standard deviation were computed by simulating 1024 robots in parallel, with each one following a random commanded base twist reference.</p> Full article ">Figure 11
<p>Photo sequence of the P-AFF policy on stairs.</p> Full article ">Figure 12
<p>Feet contact schedule of the P-AFF policy on stairs.</p> Full article ">Figure 13
<p>Photo sequence of the PH-AOF policy on a rough slope.</p> Full article ">Figure 14
<p>Feet contact schedule of the PH-AOF policy on a rough slope.</p> Full article ">Figure 15
<p>Foot orientation problem.</p> Full article ">
Open AccessPerspective
The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare
by
Souren Pashangpour and Goldie Nejat
Robotics 2024, 13(8), 112; https://doi.org/10.3390/robotics13080112 - 23 Jul 2024
Abstract
The potential use of large language models (LLMs) in healthcare robotics can help address the significant demand put on healthcare systems around the world with respect to an aging demographic and a shortage of healthcare professionals. Even though LLMs have already been integrated
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The potential use of large language models (LLMs) in healthcare robotics can help address the significant demand put on healthcare systems around the world with respect to an aging demographic and a shortage of healthcare professionals. Even though LLMs have already been integrated into medicine to assist both clinicians and patients, the integration of LLMs within healthcare robots has not yet been explored for clinical settings. In this perspective paper, we investigate the groundbreaking developments in robotics and LLMs to uniquely identify the needed system requirements for designing health-specific LLM-based robots in terms of multi-modal communication through human–robot interactions (HRIs), semantic reasoning, and task planning. Furthermore, we discuss the ethical issues, open challenges, and potential future research directions for this emerging innovative field.
Full article
(This article belongs to the Special Issue Robots and Artificial Intelligence for a Better Future of Health Care)
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Figure 1
Open AccessArticle
Nonbinary Voices for Digital Assistants—An Investigation of User Perceptions and Gender Stereotypes
by
Sonja Theresa Längle, Stephan Schlögl, Annina Ecker, Willemijn S. M. T. van Kooten and Teresa Spieß
Robotics 2024, 13(8), 111; https://doi.org/10.3390/robotics13080111 - 23 Jul 2024
Abstract
Due to the wide adoption of digital voice assistants (DVAs), interactions with technology have also changed our perceptions, highlighting and reinforcing (mostly) negative gender stereotypes. Regarding the ongoing advancements in the field of human–machine interaction, a developed and improved understanding of and awareness
[...] Read more.
Due to the wide adoption of digital voice assistants (DVAs), interactions with technology have also changed our perceptions, highlighting and reinforcing (mostly) negative gender stereotypes. Regarding the ongoing advancements in the field of human–machine interaction, a developed and improved understanding of and awareness of the reciprocity of gender and DVA technology use is thus crucial. Our work in this field expands prior research by including a nonbinary voice option as a means to eschew gender stereotypes. We used a between-subject quasi-experimental questionnaire study (female voice vs. male voice vs. nonbinary voice), in which participants provided feedback on gender stereotypes connected to voice perceptions and personality traits. Our findings show that the overall gender perception of our nonbinary voice leaned towards male on the gender spectrum, whereas the female-gendered and male-gendered voices were clearly identified as such. Furthermore, we found that feminine attributes were clearly tied to our female-gendered voice, whereas the connection of masculine attributes to the male voice was less pronounced. Most notably, however, we did not find gender-stereotypical trait attributions with our nonbinary voice. Results also show that the likability of our female-gendered and nonbinary voices was lower than it was with our male-gendered voice, and that, particularly with the nonbinary voice, this likability was affected by people’s personality traits. Thus, overall, our findings contribute (1) additional theoretical grounding for gender-studies in human–machine interaction, and (2) insights concerning peoples’ perceptions of nonbinary voices, providing additional guidance for researchers, technology designers, and DVA providers.
Full article
(This article belongs to the Special Issue Chatbots and Talking Robots)
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<p>Proposed research model.</p> Full article ">Figure 2
<p>Respondents’ age distribution.</p> Full article ">Figure 3
<p>Respondents’ stated frequency of using digital voice assistants.</p> Full article ">Figure 4
<p>Gender-typical trait attribution based on perceived DVA voices.</p> Full article ">Figure 5
<p>Likability of voices based on a 5-item semantic differential running from <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>=</mo> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>l</mi> <mi>i</mi> <mi>k</mi> <mi>e</mi> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>=</mo> <mi>l</mi> <mi>i</mi> <mi>k</mi> <mi>e</mi> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Likability of DVA voices by gender.</p> Full article ">
<p>Proposed research model.</p> Full article ">Figure 2
<p>Respondents’ age distribution.</p> Full article ">Figure 3
<p>Respondents’ stated frequency of using digital voice assistants.</p> Full article ">Figure 4
<p>Gender-typical trait attribution based on perceived DVA voices.</p> Full article ">Figure 5
<p>Likability of voices based on a 5-item semantic differential running from <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>=</mo> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mi>l</mi> <mi>i</mi> <mi>k</mi> <mi>e</mi> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>=</mo> <mi>l</mi> <mi>i</mi> <mi>k</mi> <mi>e</mi> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Likability of DVA voices by gender.</p> Full article ">
Open AccessArticle
A Reconfigurable UGV for Modular and Flexible Inspection Tasks in Nuclear Sites
by
Ivan Villaverde, Arkaitz Urquiza and Jose Luis Outón
Robotics 2024, 13(7), 110; https://doi.org/10.3390/robotics13070110 - 22 Jul 2024
Abstract
Current operations involving Dismantling and Decommissioning (D&D) in nuclear and other harsh environments rely on manual inspection and assessment of the sites, exposing human operators to potentially dangerous situations. This work presents a reconfigurable Autonomous Mobile Robot (AMR) able to mount a wide
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Current operations involving Dismantling and Decommissioning (D&D) in nuclear and other harsh environments rely on manual inspection and assessment of the sites, exposing human operators to potentially dangerous situations. This work presents a reconfigurable Autonomous Mobile Robot (AMR) able to mount a wide range of nuclear sensors for flexible and modular inspection tasks in these operations. This AMR is part of the CLEANDEM solution, which uses Unmanned Ground Vehicles (UGVs), nuclear sensors, and a Digital Twin to facilitate a tool for improving D&D operations in nuclear sites. Both the AMR used as a UGV and the system have been successfully tested in real nuclear sites, showing that these tools can greatly aid in operations management and hazard reduction.
Full article
(This article belongs to the Section Aerospace Robotics and Autonomous Systems)
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<p>Modules of the 2.5D navigation suite: (<b>a</b>) 3D map generated by the LIO-SAM module, (<b>b</b>) floor surface extracted from the 3D map, and (<b>c</b>) transitability map computed from the extracted floor.</p> Full article ">Figure 2
<p>Different sensor configurations as designed (<b>a</b>–<b>c</b>) and as mounted on the UGV (<b>d</b>–<b>g</b>). The pixelated detector uses the same mounting as the PSD phoswitch. (<b>a</b>) PSD phoswitch, (<b>b</b>) Nanopix3 + CTZ + MiniRadMeter and MiniSiLiF, (<b>c</b>) GAMON-DRONE + SNIPER-GN + MiniRadMeter and MiniSiLiF, (<b>d</b>) PSD phoswitch, (<b>e</b>) Nanopix3 + CTZ + MiniRadMeter and MiniSiLiF, (<b>f</b>) GAMON-DRONE + SNIPER-GN + MiniRadMeter and MiniSiLiF, (<b>g</b>) Pixelated detector.</p> Full article ">Figure 3
<p>Communications network.</p> Full article ">Figure 4
<p>UGV in Configuration 4 during testing at TECNALIA.</p> Full article ">Figure 5
<p>Testing the reachability with the PSD phoswitch sensor on horizontal (<b>a</b>) and vertical (<b>b</b>) surfaces.</p> Full article ">Figure 6
<p>Results from the AiNT tests: (<b>a</b>) map generated by the navigation suite and (<b>b</b>) followed path (red line) with measurement spots (green arrows) shown in the DT (image (<b>b</b>) courtesy of RINA-CSM).</p> Full article ">Figure 7
<p>Results from the AiNT tests: (<b>a</b>) radioactive sources placement (highlighted with the orange circle) and (<b>b</b>) generated radiological map (image (<b>b</b>) courtesy of RINA-CSM and ORANO).</p> Full article ">Figure 8
<p>UGV ready to be lifted by crane (<b>a</b>) and UGV accessing the site via ramp (<b>b</b>) (images courtesy of Sogin).</p> Full article ">Figure 9
<p>Schema of the demo site (<b>a</b>) and UGV in the corridor with the radioactive samples (<b>b</b>) (images courtesy of Sogin).</p> Full article ">Figure 10
<p>Prerecorded high definition 3D reconstruction of the demo site (<b>a</b>), the down-sampled version used as the map (<b>b</b>), and the map generated using LIO-SAM (<b>c</b>).</p> Full article ">Figure 11
<p>(<b>a</b>) DT showing the path (red line), measurement poses (green arrows), (<b>b</b>) radiological map, and (<b>c</b>) hotspot analysis. (Images courtesy of RINA-CSM and ORANO).</p> Full article ">
<p>Modules of the 2.5D navigation suite: (<b>a</b>) 3D map generated by the LIO-SAM module, (<b>b</b>) floor surface extracted from the 3D map, and (<b>c</b>) transitability map computed from the extracted floor.</p> Full article ">Figure 2
<p>Different sensor configurations as designed (<b>a</b>–<b>c</b>) and as mounted on the UGV (<b>d</b>–<b>g</b>). The pixelated detector uses the same mounting as the PSD phoswitch. (<b>a</b>) PSD phoswitch, (<b>b</b>) Nanopix3 + CTZ + MiniRadMeter and MiniSiLiF, (<b>c</b>) GAMON-DRONE + SNIPER-GN + MiniRadMeter and MiniSiLiF, (<b>d</b>) PSD phoswitch, (<b>e</b>) Nanopix3 + CTZ + MiniRadMeter and MiniSiLiF, (<b>f</b>) GAMON-DRONE + SNIPER-GN + MiniRadMeter and MiniSiLiF, (<b>g</b>) Pixelated detector.</p> Full article ">Figure 3
<p>Communications network.</p> Full article ">Figure 4
<p>UGV in Configuration 4 during testing at TECNALIA.</p> Full article ">Figure 5
<p>Testing the reachability with the PSD phoswitch sensor on horizontal (<b>a</b>) and vertical (<b>b</b>) surfaces.</p> Full article ">Figure 6
<p>Results from the AiNT tests: (<b>a</b>) map generated by the navigation suite and (<b>b</b>) followed path (red line) with measurement spots (green arrows) shown in the DT (image (<b>b</b>) courtesy of RINA-CSM).</p> Full article ">Figure 7
<p>Results from the AiNT tests: (<b>a</b>) radioactive sources placement (highlighted with the orange circle) and (<b>b</b>) generated radiological map (image (<b>b</b>) courtesy of RINA-CSM and ORANO).</p> Full article ">Figure 8
<p>UGV ready to be lifted by crane (<b>a</b>) and UGV accessing the site via ramp (<b>b</b>) (images courtesy of Sogin).</p> Full article ">Figure 9
<p>Schema of the demo site (<b>a</b>) and UGV in the corridor with the radioactive samples (<b>b</b>) (images courtesy of Sogin).</p> Full article ">Figure 10
<p>Prerecorded high definition 3D reconstruction of the demo site (<b>a</b>), the down-sampled version used as the map (<b>b</b>), and the map generated using LIO-SAM (<b>c</b>).</p> Full article ">Figure 11
<p>(<b>a</b>) DT showing the path (red line), measurement poses (green arrows), (<b>b</b>) radiological map, and (<b>c</b>) hotspot analysis. (Images courtesy of RINA-CSM and ORANO).</p> Full article ">
Open AccessArticle
Large-Scale Urban Traffic Management Using Zero-Shot Knowledge Transfer in Multi-Agent Reinforcement Learning for Intersection Patterns
by
Theodore Tranos, Christos Spatharis, Konstantinos Blekas and Andreas-Giorgios Stafylopatis
Robotics 2024, 13(7), 109; https://doi.org/10.3390/robotics13070109 - 19 Jul 2024
Abstract
The automatic control of vehicle traffic in large urban networks constitutes one of the most serious challenges to modern societies, with an impact on improving the quality of human life and saving energy and time. Intersections are a special traffic structure of pivotal
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The automatic control of vehicle traffic in large urban networks constitutes one of the most serious challenges to modern societies, with an impact on improving the quality of human life and saving energy and time. Intersections are a special traffic structure of pivotal importance as they accumulate a large number of vehicles that should be served in an optimal manner. Constructing intelligent models that manage to automatically coordinate and steer vehicles through intersections is a key point in the fragmentation of traffic control, offering active solutions through the flexibility of automatically adapting to a variety of traffic conditions. Responding to this call, this work aims to propose an integrated active solution of automatic traffic management. We introduce a multi-agent reinforcement learning framework that effectively models traffic flow at individual unsignalized intersections. It relies on a compact agent definition, a rich information state space, and a learning process characterized not only by depth and quality, but also by substantial degrees of freedom and variability. The resulting driving profiles are further transferred to larger road networks to integrate their individual elements and compose an effective automatic traffic control platform. Experiments are conducted on simulated road networks of variable complexity, demonstrating the potential of the proposed method.
Full article
(This article belongs to the Special Issue Active Methods in Autonomous Navigation)
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<p>Examples of different configurations covered by the 3-way and 4-way intersection patterns.</p> Full article ">Figure 2
<p>Overview structure of the proposed method for traffic control in road networks consisting of three major modules.</p> Full article ">Figure 3
<p>Traffic control in a 4-way intersection pattern using the proposed MARL scheme. Every road-agent (<math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>A</mi> <mi>i</mi> </msub> </mrow> </semantics></math>) is responsible for safely guiding vehicles through its designated road segment, while cooperatively coordinating with the other agents (in our case three).</p> Full article ">Figure 4
<p>Matching the network’s intersections with two default intersection patterns. The road network contains four and two copies of the default “4-way” and “3-way” intersection patterns, respectively. The light-colored thin strip corresponds to a route that a vehicle may follow within this network.</p> Full article ">Figure 5
<p>Learning curves of the 3-way and 4-way intersection patterns in terms of the average velocity, duration, and collisions per epoch, created by using a rolling window of fifty (50) episodes.</p> Full article ">Figure 6
<p>Dynamic evolution of both the average velocity and the frequency of vehicles being served per second, obtained from the implementation of learned multi-agent policies on intersection patterns within a designated test scenario. Traffic state colored zones are also shown.</p> Full article ">Figure 7
<p>Four artificial road networks of increasing complexity that were generated for evaluating the knowledge transfer process. Every intersection is a noisy copy of either the default 3-way or 4-way intersection pattern.</p> Full article ">
<p>Examples of different configurations covered by the 3-way and 4-way intersection patterns.</p> Full article ">Figure 2
<p>Overview structure of the proposed method for traffic control in road networks consisting of three major modules.</p> Full article ">Figure 3
<p>Traffic control in a 4-way intersection pattern using the proposed MARL scheme. Every road-agent (<math display="inline"><semantics> <mrow> <mi>R</mi> <msub> <mi>A</mi> <mi>i</mi> </msub> </mrow> </semantics></math>) is responsible for safely guiding vehicles through its designated road segment, while cooperatively coordinating with the other agents (in our case three).</p> Full article ">Figure 4
<p>Matching the network’s intersections with two default intersection patterns. The road network contains four and two copies of the default “4-way” and “3-way” intersection patterns, respectively. The light-colored thin strip corresponds to a route that a vehicle may follow within this network.</p> Full article ">Figure 5
<p>Learning curves of the 3-way and 4-way intersection patterns in terms of the average velocity, duration, and collisions per epoch, created by using a rolling window of fifty (50) episodes.</p> Full article ">Figure 6
<p>Dynamic evolution of both the average velocity and the frequency of vehicles being served per second, obtained from the implementation of learned multi-agent policies on intersection patterns within a designated test scenario. Traffic state colored zones are also shown.</p> Full article ">Figure 7
<p>Four artificial road networks of increasing complexity that were generated for evaluating the knowledge transfer process. Every intersection is a noisy copy of either the default 3-way or 4-way intersection pattern.</p> Full article ">
Open AccessArticle
Continuous Online Semantic Implicit Representation for Autonomous Ground Robot Navigation in Unstructured Environments
by
Quentin Serdel, Julien Marzat and Julien Moras
Robotics 2024, 13(7), 108; https://doi.org/10.3390/robotics13070108 - 18 Jul 2024
Abstract
While mobile ground robots have now the physical capacity of travelling in unstructured challenging environments such as extraterrestrial surfaces or devastated terrains, their safe and efficient autonomous navigation has yet to be improved before entrusting them with complex unsupervised missions in such conditions.
[...] Read more.
While mobile ground robots have now the physical capacity of travelling in unstructured challenging environments such as extraterrestrial surfaces or devastated terrains, their safe and efficient autonomous navigation has yet to be improved before entrusting them with complex unsupervised missions in such conditions. Recent advances in machine learning applied to semantic scene understanding and environment representations, coupled with modern embedded computational means and sensors hold promising potential in this matter. This paper therefore introduces the combination of semantic understanding, continuous implicit environment representation and smooth informed path-planning in a new method named COSMAu-Nav. It is specifically dedicated to autonomous ground robot navigation in unstructured environments and adaptable for embedded, real-time usage without requiring any form of telecommunication. Data clustering and Gaussian processes are employed to perform online regression of the environment topography, occupancy and terrain traversability from 3D semantic point clouds while providing an uncertainty modeling. The continuous and differentiable properties of Gaussian processes allow gradient based optimisation to be used for smooth local path-planning with respect to the terrain properties. The proposed pipeline has been evaluated and compared with two reference 3D semantic mapping methods in terms of quality of representation under localisation and semantic segmentation uncertainty using a Gazebo simulation, derived from the 3DRMS dataset. Its computational requirements have been evaluated using the Rellis-3D real world dataset. It has been implemented on a real ground robot and successfully employed for its autonomous navigation in a previously unknown outdoor environment.
Full article
(This article belongs to the Special Issue Decision-Making and Control under Uncertainties for Robotic and Autonomous Systems)
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Figure 1
Figure 1
<p>Example visualisation of the proposed data treatment pipeline producing the simultaneous regression of variables of interest for robot navigation, with an intermediate data compression step.</p> Full article ">Figure 2
<p>Scheme of the GPR environment representation pipeline. <span class="html-italic">p</span> denotes 3D points coordinates, <span class="html-italic">l</span> semantic labels, <math display="inline"><semantics> <mi>κ</mi> </semantics></math> network confidences, <span class="html-italic">T</span> terrain traversability values, <span class="html-italic">d</span> distances to closest obstacles, <span class="html-italic">s</span> the terrain slope and <math display="inline"><semantics> <mi>σ</mi> </semantics></math> the GPR standard deviation. <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi> </mrow> </semantics></math> are 2D coordinates. Variables marked with * are products of the compression and fusion process, <math display="inline"><semantics> <msub> <mrow> <mi>H</mi> <mo>|</mo> </mrow> <mrow> <mi>R</mi> <mo>→</mo> <mi>W</mi> </mrow> </msub> </semantics></math> is the transform between the robot sensor frame and the world frame.</p> Full article ">Figure 3
<p>Scheme of the COSMAu-Nav architecture performing online smooth local path-planning from semantic point clouds and robot localisation. The local planner goal can be given by an external global navigation module such as the one described in <a href="#sec6dot2dot3-robotics-13-00108" class="html-sec">Section 6.2.3</a>.</p> Full article ">Figure 4
<p>Visualisation of the Gazebo simulation (reproduced from [<a href="#B2-robotics-13-00108" class="html-bibr">2</a>]) showing the 3D environment mesh derived from the 3DRMS dataset, the mobile robot model and its camera module outputs.</p> Full article ">Figure 5
<p>Comparative evaluation of the clustering compression methods and their parameters on the ratio between GPR environment representation accuracy and computational load.</p> Full article ">Figure 6
<p>Comparative evaluation of the impact of the process variance threshold <math display="inline"><semantics> <msub> <mi>t</mi> <msup> <mi>σ</mi> <mn>2</mn> </msup> </msub> </semantics></math> on the GPR environment representation quality for both compression methods.</p> Full article ">Figure 7
<p>Diagram of the baseline SMaNA architecture [<a href="#B2-robotics-13-00108" class="html-bibr">2</a>] with different candidate environment representation methods.</p> Full article ">Figure 8
<p>Visualisation of the environment representations produced by the compared methods from sensor data gathered along a trajectory inside the described simulation environment with no input data degradation and nominal parameters. Robot trajectory is displayed in white in all 4 figures. Top left displays the reference navigation grid built from the simulator ground-truth. Top right and bottom left display the navigation graphs built by SMaNA respectively using Semantic Octomap and Kimera-Semantics. Bottom left displays the inference of the GPR environment representation on a 2D uniform grid of same resolution.</p> Full article ">Figure 9
<p>Results of the comparative evaluation of COSMAu-Nav environment representation process with the application of Semantic Octomap and Kimera Semantics in SMaNA in terms of environment representation quality in function of the mapping and inference resolution.</p> Full article ">Figure 10
<p>Results of the comparative evaluation of COSMAu-Nav environment representation process with the application of Semantic Octomap and Kimera Semantics in SMaNA in terms of environment representation quality in function of the robot localisation noise.</p> Full article ">Figure 11
<p>Results of the comparative evaluation of COSMAu-Nav environment representation process with the application of Semantic Octomap and Kimera Semantics in SMaNA in terms of environment representation quality in function of the semantic segmentation error level.</p> Full article ">Figure 12
<p>Results of the comparative evaluation of COSMAu-Nav environment representation process against the integration of Semantic Octomap and Kimera Semantics in SMaNA in terms of computational performances when processing real-time data from the sequence <span class="html-italic">00</span> of the Rellis-3D dataset.</p> Full article ">Figure 13
<p>Scheme of the sensor data treatment pipeline generating the robot localisation and 3D point clouds enriched with semantic labels from data produced by a 3D LiDAR and an RGB camera mounted on a ground robot.</p> Full article ">Figure 14
<p>Example data produced by the online pre-treatment pipeline embedded on the robot.</p> Full article ">Figure 15
<p>Visualisation of COSMAu-Nav local GPR environment representation and its different planning outputs during the robot autonomous navigation mission.</p> Full article ">Figure 16
<p>Visual result of the presented navigation task outcome. <b>Left</b>: aerial view of the reference map cloud with the trajectory followed by the robot and the given successive way-points. <b>Right</b>: global navigation environment reconstructed by COSMAu-Nav with the recorded robot key-poses.</p> Full article ">Figure A1
<p>Detailed results of the ablation study presented in <a href="#sec5dot2-robotics-13-00108" class="html-sec">Section 5.2</a> displaying the influence of the compared compression methods parameters over the map quality metrics and computational characteristics.</p> Full article ">Figure A1 Cont.
<p>Detailed results of the ablation study presented in <a href="#sec5dot2-robotics-13-00108" class="html-sec">Section 5.2</a> displaying the influence of the compared compression methods parameters over the map quality metrics and computational characteristics.</p> Full article ">
<p>Example visualisation of the proposed data treatment pipeline producing the simultaneous regression of variables of interest for robot navigation, with an intermediate data compression step.</p> Full article ">Figure 2
<p>Scheme of the GPR environment representation pipeline. <span class="html-italic">p</span> denotes 3D points coordinates, <span class="html-italic">l</span> semantic labels, <math display="inline"><semantics> <mi>κ</mi> </semantics></math> network confidences, <span class="html-italic">T</span> terrain traversability values, <span class="html-italic">d</span> distances to closest obstacles, <span class="html-italic">s</span> the terrain slope and <math display="inline"><semantics> <mi>σ</mi> </semantics></math> the GPR standard deviation. <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi> </mrow> </semantics></math> are 2D coordinates. Variables marked with * are products of the compression and fusion process, <math display="inline"><semantics> <msub> <mrow> <mi>H</mi> <mo>|</mo> </mrow> <mrow> <mi>R</mi> <mo>→</mo> <mi>W</mi> </mrow> </msub> </semantics></math> is the transform between the robot sensor frame and the world frame.</p> Full article ">Figure 3
<p>Scheme of the COSMAu-Nav architecture performing online smooth local path-planning from semantic point clouds and robot localisation. The local planner goal can be given by an external global navigation module such as the one described in <a href="#sec6dot2dot3-robotics-13-00108" class="html-sec">Section 6.2.3</a>.</p> Full article ">Figure 4
<p>Visualisation of the Gazebo simulation (reproduced from [<a href="#B2-robotics-13-00108" class="html-bibr">2</a>]) showing the 3D environment mesh derived from the 3DRMS dataset, the mobile robot model and its camera module outputs.</p> Full article ">Figure 5
<p>Comparative evaluation of the clustering compression methods and their parameters on the ratio between GPR environment representation accuracy and computational load.</p> Full article ">Figure 6
<p>Comparative evaluation of the impact of the process variance threshold <math display="inline"><semantics> <msub> <mi>t</mi> <msup> <mi>σ</mi> <mn>2</mn> </msup> </msub> </semantics></math> on the GPR environment representation quality for both compression methods.</p> Full article ">Figure 7
<p>Diagram of the baseline SMaNA architecture [<a href="#B2-robotics-13-00108" class="html-bibr">2</a>] with different candidate environment representation methods.</p> Full article ">Figure 8
<p>Visualisation of the environment representations produced by the compared methods from sensor data gathered along a trajectory inside the described simulation environment with no input data degradation and nominal parameters. Robot trajectory is displayed in white in all 4 figures. Top left displays the reference navigation grid built from the simulator ground-truth. Top right and bottom left display the navigation graphs built by SMaNA respectively using Semantic Octomap and Kimera-Semantics. Bottom left displays the inference of the GPR environment representation on a 2D uniform grid of same resolution.</p> Full article ">Figure 9
<p>Results of the comparative evaluation of COSMAu-Nav environment representation process with the application of Semantic Octomap and Kimera Semantics in SMaNA in terms of environment representation quality in function of the mapping and inference resolution.</p> Full article ">Figure 10
<p>Results of the comparative evaluation of COSMAu-Nav environment representation process with the application of Semantic Octomap and Kimera Semantics in SMaNA in terms of environment representation quality in function of the robot localisation noise.</p> Full article ">Figure 11
<p>Results of the comparative evaluation of COSMAu-Nav environment representation process with the application of Semantic Octomap and Kimera Semantics in SMaNA in terms of environment representation quality in function of the semantic segmentation error level.</p> Full article ">Figure 12
<p>Results of the comparative evaluation of COSMAu-Nav environment representation process against the integration of Semantic Octomap and Kimera Semantics in SMaNA in terms of computational performances when processing real-time data from the sequence <span class="html-italic">00</span> of the Rellis-3D dataset.</p> Full article ">Figure 13
<p>Scheme of the sensor data treatment pipeline generating the robot localisation and 3D point clouds enriched with semantic labels from data produced by a 3D LiDAR and an RGB camera mounted on a ground robot.</p> Full article ">Figure 14
<p>Example data produced by the online pre-treatment pipeline embedded on the robot.</p> Full article ">Figure 15
<p>Visualisation of COSMAu-Nav local GPR environment representation and its different planning outputs during the robot autonomous navigation mission.</p> Full article ">Figure 16
<p>Visual result of the presented navigation task outcome. <b>Left</b>: aerial view of the reference map cloud with the trajectory followed by the robot and the given successive way-points. <b>Right</b>: global navigation environment reconstructed by COSMAu-Nav with the recorded robot key-poses.</p> Full article ">Figure A1
<p>Detailed results of the ablation study presented in <a href="#sec5dot2-robotics-13-00108" class="html-sec">Section 5.2</a> displaying the influence of the compared compression methods parameters over the map quality metrics and computational characteristics.</p> Full article ">Figure A1 Cont.
<p>Detailed results of the ablation study presented in <a href="#sec5dot2-robotics-13-00108" class="html-sec">Section 5.2</a> displaying the influence of the compared compression methods parameters over the map quality metrics and computational characteristics.</p> Full article ">
Open AccessArticle
Human–Robot Collaborative Manufacturing Cell with Learning-Based Interaction Abilities
by
Joel Baptista, Afonso Castro, Manuel Gomes, Pedro Amaral, Vítor Santos, Filipe Silva and Miguel Oliveira
Robotics 2024, 13(7), 107; https://doi.org/10.3390/robotics13070107 - 17 Jul 2024
Abstract
This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures
[...] Read more.
This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures for human-to-robot communication, classification of physical-contact-based interaction primitives during handover operations, and detection of hand–object interactions to anticipate human intentions. Due to the nature and complexity of perception, deep-learning-based techniques were used to enhance robustness and adaptability. The main components are integrated in a system containing multiple functionalities, coordinated through a dedicated state machine. This ensures appropriate actions and reactions based on events, enabling the execution of specific modules to complete a given multi-step task. An ROS-based architecture supports the software infrastructure among sensor interfacing, data processing, and robot and gripper controllers nodes. The result is demonstrated by a functional use case that involves multiple tasks and behaviors, paving the way for the deployment of more advanced collaborative cells in manufacturing contexts.
Full article
(This article belongs to the Section Industrial Robots and Automation)
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Figure 1
<p>The prototype collaborative manufacturing cell: LARCC. Several sensors cover the robot and operator spaces for volumetric monitoring and gesture interaction, including LiDARs (red circles) and RGB-D cameras (red rectangles). The cell includes a UR10e COBOT with a Robotiq 2F gripper.</p> Full article ">Figure 2
<p>Overview of the system architecture.</p> Full article ">Figure 3
<p>Overview of the hand gesture recognition system.</p> Full article ">Figure 4
<p>The four histograms represent the logit distribution of the four classes when passing images from one class through the hand classification model. In each histogram, the black line represents the threshold value that optimizes the precision score.</p> Full article ">Figure 5
<p>Confusion matrix, in percentage (%), obtained using logit thresholds and gesture filtering. The confusion matrix is normalized by the column.</p> Full article ">Figure 6
<p>Timeline behavior of FT values, for two different shake primitives [<a href="#B35-robotics-13-00107" class="html-bibr">35</a>]. The top two rows depict the end-effector forces and torques along the three axes. Last row depicts the six robot joint-captured torques.</p> Full article ">Figure 7
<p>Online primitive classification timeline example with three physical interactions.</p> Full article ">Figure 8
<p>Confusion matrix in percentage (%) obtained after testing the feedforward trained network.</p> Full article ">Figure 9
<p>Example of joint efforts (Nm) and wrist torques (Nm) and forces (N) felt by two sequential performed contact primitives, for period of 1 s.</p> Full article ">Figure 10
<p>Example of neural network output confidences for two sequentially performed contact primitives. In this case, the ground truth is composed of 0.5 s of <span class="html-small-caps">pull</span>, followed by 0.5 s of <span class="html-small-caps">push</span>. At each time step, the sum of all four output confidences is equal to 1, since the output layer uses softmax as the activation function.</p> Full article ">Figure 11
<p>Volumetric detection within the collaborative cell. The work volume is depicted by the prominent red prism, while the purple dots represent the point cloud captured by one of the LiDARs. The red dots correspond to the centers of the occupied voxels detected by OctoMap.</p> Full article ">Figure 12
<p>Confusion matrix for object classification using a CNN model.</p> Full article ">Figure 13
<p>Experimental setup depicting the collaborative cell where the study was conducted. On the left, it features two RGB-D cameras (marked as white rectangles 1 and 2) and the UR10e COBOT (marked as white rectangle 3). On the right are the objects used to discriminate based on their grasping patterns.</p> Full article ">Figure 14
<p>Functional blocks of the anticipatory robotic system.</p> Full article ">Figure 15
<p>Temporal evolution of CNN object classifier logits by picking up and dropping the bottle four times.</p> Full article ">Figure 16
<p>Demonstration’s state machine diagram; <tt>Sn</tt> labels are prefixes for states, and <tt>en</tt> labels are prefixes for events (triggers). Events triggered by user interaction are marked in red.</p> Full article ">Figure 17
<p>View of the human–robot interaction space. The red zones are the regions that the robot uses to continuously place and swap objects 1 and 2. The green zone is the designated area for object recovery. The robot is in a position ready for operator physical interaction.</p> Full article ">
<p>The prototype collaborative manufacturing cell: LARCC. Several sensors cover the robot and operator spaces for volumetric monitoring and gesture interaction, including LiDARs (red circles) and RGB-D cameras (red rectangles). The cell includes a UR10e COBOT with a Robotiq 2F gripper.</p> Full article ">Figure 2
<p>Overview of the system architecture.</p> Full article ">Figure 3
<p>Overview of the hand gesture recognition system.</p> Full article ">Figure 4
<p>The four histograms represent the logit distribution of the four classes when passing images from one class through the hand classification model. In each histogram, the black line represents the threshold value that optimizes the precision score.</p> Full article ">Figure 5
<p>Confusion matrix, in percentage (%), obtained using logit thresholds and gesture filtering. The confusion matrix is normalized by the column.</p> Full article ">Figure 6
<p>Timeline behavior of FT values, for two different shake primitives [<a href="#B35-robotics-13-00107" class="html-bibr">35</a>]. The top two rows depict the end-effector forces and torques along the three axes. Last row depicts the six robot joint-captured torques.</p> Full article ">Figure 7
<p>Online primitive classification timeline example with three physical interactions.</p> Full article ">Figure 8
<p>Confusion matrix in percentage (%) obtained after testing the feedforward trained network.</p> Full article ">Figure 9
<p>Example of joint efforts (Nm) and wrist torques (Nm) and forces (N) felt by two sequential performed contact primitives, for period of 1 s.</p> Full article ">Figure 10
<p>Example of neural network output confidences for two sequentially performed contact primitives. In this case, the ground truth is composed of 0.5 s of <span class="html-small-caps">pull</span>, followed by 0.5 s of <span class="html-small-caps">push</span>. At each time step, the sum of all four output confidences is equal to 1, since the output layer uses softmax as the activation function.</p> Full article ">Figure 11
<p>Volumetric detection within the collaborative cell. The work volume is depicted by the prominent red prism, while the purple dots represent the point cloud captured by one of the LiDARs. The red dots correspond to the centers of the occupied voxels detected by OctoMap.</p> Full article ">Figure 12
<p>Confusion matrix for object classification using a CNN model.</p> Full article ">Figure 13
<p>Experimental setup depicting the collaborative cell where the study was conducted. On the left, it features two RGB-D cameras (marked as white rectangles 1 and 2) and the UR10e COBOT (marked as white rectangle 3). On the right are the objects used to discriminate based on their grasping patterns.</p> Full article ">Figure 14
<p>Functional blocks of the anticipatory robotic system.</p> Full article ">Figure 15
<p>Temporal evolution of CNN object classifier logits by picking up and dropping the bottle four times.</p> Full article ">Figure 16
<p>Demonstration’s state machine diagram; <tt>Sn</tt> labels are prefixes for states, and <tt>en</tt> labels are prefixes for events (triggers). Events triggered by user interaction are marked in red.</p> Full article ">Figure 17
<p>View of the human–robot interaction space. The red zones are the regions that the robot uses to continuously place and swap objects 1 and 2. The green zone is the designated area for object recovery. The robot is in a position ready for operator physical interaction.</p> Full article ">
Open AccessArticle
Vision-Based Situational Graphs Exploiting Fiducial Markers for the Integration of Semantic Entities
by
Ali Tourani, Hriday Bavle, Deniz Işınsu Avşar, Jose Luis Sanchez-Lopez, Rafael Munoz-Salinas and Holger Voos
Robotics 2024, 13(7), 106; https://doi.org/10.3390/robotics13070106 - 16 Jul 2024
Abstract
Situational Graphs (S-Graphs) merge geometric models of the environment generated by Simultaneous Localization and Mapping (SLAM) approaches with 3D scene graphs into a multi-layered jointly optimizable factor graph. As an advantage, S-Graphs not only offer a more comprehensive robotic situational awareness by combining
[...] Read more.
Situational Graphs (S-Graphs) merge geometric models of the environment generated by Simultaneous Localization and Mapping (SLAM) approaches with 3D scene graphs into a multi-layered jointly optimizable factor graph. As an advantage, S-Graphs not only offer a more comprehensive robotic situational awareness by combining geometric maps with diverse, hierarchically organized semantic entities and their topological relationships within one graph, but they also lead to improved performance of localization and mapping on the SLAM level by exploiting semantic information. In this paper, we introduce a vision-based version of S-Graphs where a conventional Visual SLAM (VSLAM) system is used for low-level feature tracking and mapping. In addition, the framework exploits the potential of fiducial markers (both visible and our recently introduced transparent or fully invisible markers) to encode comprehensive information about environments and the objects within them. The markers aid in identifying and mapping structural-level semantic entities, including walls and doors in the environment, with reliable poses in the global reference, subsequently establishing meaningful associations with higher-level entities, including corridors and rooms. However, in addition to including semantic entities, the semantic and geometric constraints imposed by the fiducial markers are also utilized to improve the reconstructed map’s quality and reduce localization errors. Experimental results on a real-world dataset collected using legged robots show that our framework excels in crafting a richer, multi-layered hierarchical map and enhances robot pose accuracy at the same time.
Full article
(This article belongs to the Special Issue Localization and 3D Mapping of Intelligent Robotics)
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![](https://pub.mdpi-res.com/robotics/robotics-13-00106/article_deploy/html/images/robotics-13-00106-g001-550.jpg?1721110140)
Figure 1
Figure 1
<p>A reconstructed map with its hierarchical S-Graph representation generated by our framework, containing various detected structural-level entities and the connections among them: (<b>a</b>) the top view of the reconstructed map represented in 2D. (<b>b</b>) The final generated 3D view.</p> Full article ">Figure 2
<p>A transparent iMarker introduced by the authors: (<b>A</b>) an iMarker placed on a door frame captured by a normal camera, (<b>B</b>) the recognized iMarker with pose information, obtained by applying an ArUco detector on the image captured by the camera with a left-handed polarizer.</p> Full article ">Figure 3
<p>The primary system components and pipeline of the proposed approach. The components inherited from ORB-SLAM 3.0 with no changes are shown in gray, and the ones modified to match the current architecture are shown in light blue.</p> Full article ">Figure 4
<p>The hierarchical S-Graph representation of our proposed work incorporates semantic constraints, including walls, doorways, corridors, and rooms. The pre-existing geometric constraints have been complemented with semantic constraints acquired by fiducial markers.</p> Full article ">Figure 5
<p>Dataset collected to evaluate the proposed method: (<b>A</b>) the legged robots used for data collection, (<b>B</b>) some instances of the environment prepared for data collection.</p> Full article ">Figure 6
<p>The qualitative results and Absolute Trajectory Error (ATE) of the proposed approach with respect to translation in <span class="html-italic">meters</span> on <span class="html-italic">Seq-05</span> (<b>A</b>,<b>B</b>) and <span class="html-italic">Seq-06</span> (<b>C</b>,<b>D</b>). The dotted lines in the charts are LiDAR ground truth reference values and the solid lines show the trajectory of the robot. In an ideal representation, the shapes should perfectly match, while the calculated pose errors (from low errors shown in navy blue to high errors shown in dark red) in the Visual SLAM system are inevitable.</p> Full article ">
<p>A reconstructed map with its hierarchical S-Graph representation generated by our framework, containing various detected structural-level entities and the connections among them: (<b>a</b>) the top view of the reconstructed map represented in 2D. (<b>b</b>) The final generated 3D view.</p> Full article ">Figure 2
<p>A transparent iMarker introduced by the authors: (<b>A</b>) an iMarker placed on a door frame captured by a normal camera, (<b>B</b>) the recognized iMarker with pose information, obtained by applying an ArUco detector on the image captured by the camera with a left-handed polarizer.</p> Full article ">Figure 3
<p>The primary system components and pipeline of the proposed approach. The components inherited from ORB-SLAM 3.0 with no changes are shown in gray, and the ones modified to match the current architecture are shown in light blue.</p> Full article ">Figure 4
<p>The hierarchical S-Graph representation of our proposed work incorporates semantic constraints, including walls, doorways, corridors, and rooms. The pre-existing geometric constraints have been complemented with semantic constraints acquired by fiducial markers.</p> Full article ">Figure 5
<p>Dataset collected to evaluate the proposed method: (<b>A</b>) the legged robots used for data collection, (<b>B</b>) some instances of the environment prepared for data collection.</p> Full article ">Figure 6
<p>The qualitative results and Absolute Trajectory Error (ATE) of the proposed approach with respect to translation in <span class="html-italic">meters</span> on <span class="html-italic">Seq-05</span> (<b>A</b>,<b>B</b>) and <span class="html-italic">Seq-06</span> (<b>C</b>,<b>D</b>). The dotted lines in the charts are LiDAR ground truth reference values and the solid lines show the trajectory of the robot. In an ideal representation, the shapes should perfectly match, while the calculated pose errors (from low errors shown in navy blue to high errors shown in dark red) in the Visual SLAM system are inevitable.</p> Full article ">
Open AccessArticle
ANN Enhanced Hybrid Force/Position Controller of Robot Manipulators for Fiber Placement
by
José Francisco Villa-Tiburcio, José Antonio Estrada-Torres, Rodrigo Hernández-Alvarado, Josue Rafael Montes-Martínez, Darío Bringas-Posadas and Edgar Adrián Franco-Urquiza
Robotics 2024, 13(7), 105; https://doi.org/10.3390/robotics13070105 - 13 Jul 2024
Abstract
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In practice, most industrial robot manipulators use PID (Proportional + Integral + Derivative) controllers, thanks to their simplicity and adequate performance under certain conditions. Normally, this type of controller has a good performance in tasks where the robot moves freely, performing movements without
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In practice, most industrial robot manipulators use PID (Proportional + Integral + Derivative) controllers, thanks to their simplicity and adequate performance under certain conditions. Normally, this type of controller has a good performance in tasks where the robot moves freely, performing movements without contact with its environment. However, complications arise in applications such as the AFP (Automated Fiber Placement) process, where a high degree of precision and repeatability is required in the control of parameters such as position and compression force for the production of composite parts. The control of these parameters is a major challenge in terms of quality and productivity of the final product, mainly due to the complex geometry of the part and the type of tooling with which the AFP system is equipped. In the last decades, several control system approaches have been proposed in the literature, such as classical, adaptive or sliding mode control theory based methodologies. Nevertheless, such strategies present difficulties to change their dynamics since their design consider only some set of disturbances. This article presents a novel intelligent type control algorithm based on back-propagation neural networks (BP-NNs) combined with classical PID/PI control schemes for force/position control in manipulator robots. The PID/PI controllers are responsible for the main control action, while the BP-NNs contributes with its ability to estimate and compensate online the dynamic variations of the AFP process. It is proven that the proposed control achieves both, stability in the Lyapunov sense for the desired interaction force between the end-effector and the environment, and position trajectory tracking for the robot tip in Cartesian space. The performance and efficiency of the proposed control is evaluated by numerical simulations in MATLAB-Simulink environment, obtaining as results that the errors for the desired force and the tracking of complex trajectories are reduced to a range below 5% in root mean square error (RMSE).
Full article
![](https://pub.mdpi-res.com/robotics/robotics-13-00105/article_deploy/html/images/robotics-13-00105-g001-550.jpg?1721373809)
Figure 1
Figure 1
<p>3D model of the six-axis robot with an automated fiber placement head.</p> Full article ">Figure 2
<p>Tool-workpiece contact state during AFP process.</p> Full article ">Figure 3
<p>Rigid 6-axis force/torque sensor [<a href="#B59-robotics-13-00105" class="html-bibr">59</a>].</p> Full article ">Figure 4
<p>Block diagram for force/position control by artificial neural network and classical control.</p> Full article ">Figure 5
<p>NN topology for (<b>a</b>) position control, and (<b>b</b>) force control.</p> Full article ">Figure 6
<p>NN cost function in (<b>a</b>) 3D, and (<b>b</b>) 2D.</p> Full article ">Figure 7
<p>Turbine blade section used for simulation force/position control tests.</p> Full article ">Figure 8
<p>Proposed methodology for the generation and validation of complex trajectories.</p> Full article ">Figure 9
<p>Snapshots of the robot manipulator during the trajectory tracking along a complex surface for (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> s, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>648</mn> </mrow> </semantics></math> s, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>775</mn> </mrow> </semantics></math> s, and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1700</mn> </mrow> </semantics></math> s.</p> Full article ">Figure 10
<p>Complex trajectory tracking in Cartesian space: real trajectory <math display="inline"><semantics> <mrow> <mi>x</mi> <mi>y</mi> <msub> <mi>z</mi> <mi>r</mi> </msub> </mrow> </semantics></math> (green solid line) vs. desired trajectory <math display="inline"><semantics> <mrow> <mi>x</mi> <mi>y</mi> <msub> <mi>z</mi> <mi>d</mi> </msub> </mrow> </semantics></math> (red dotted line).</p> Full article ">Figure 11
<p>Position tracking (<b>left</b>) y error position tracking (<b>right</b>) on the axes <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>Movement of the six joints and angular (<b>left</b>) angular errors (<b>right</b>) of the manipulator robot.</p> Full article ">Figure 13
<p>Real linear velocity <math display="inline"><semantics> <msub> <mi>v</mi> <mi>r</mi> </msub> </semantics></math> (green solid line) vs. desired <math display="inline"><semantics> <msub> <mi>v</mi> <mi>d</mi> </msub> </semantics></math> (red dotted line).</p> Full article ">Figure 14
<p>Numerical results of orientation (<b>left</b>) and force (<b>right</b>) along the complex surface. Actual orientation <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>r</mi> </msub> </semantics></math> (green solid line) desired <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>d</mi> </msub> </semantics></math> (red dotted line). Real compaction force <math display="inline"><semantics> <msub> <mi>f</mi> <mi>r</mi> </msub> </semantics></math> (green solid line) vs. desired <math display="inline"><semantics> <msub> <mi>f</mi> <mi>d</mi> </msub> </semantics></math> (red dotted line).</p> Full article ">Figure 15
<p>Control torques values.</p> Full article ">Figure 16
<p>Trajectory tracking without peturbation. Reference (red dotted line) vs. PID (blue solid line) vs. PID+NN (green solid line).</p> Full article ">Figure 17
<p>Trajectory tracking error without perturbation.</p> Full article ">Figure 18
<p>Force applied on the surface (<b>left</b>) and force error generated (<b>right</b>) without perturbation.</p> Full article ">Figure 19
<p>Trajectory tracking with variable perturbation of 1 N. Reference (red dotted line) vs. PID (blue solid line) vs. PID+NN (green solid line).</p> Full article ">Figure 20
<p>Trajectory tracking error with 1 N perturbation.</p> Full article ">Figure 21
<p>Force applied on the surface (<b>left</b>) and force error generated (<b>right</b>) with 1 N perturbation.</p> Full article ">Figure 22
<p>Trajectory tracking with variable perturbation of 5 N. Reference (red dotted line) vs. PID (blue solid line) vs. PID+NN (green solid line).</p> Full article ">Figure 23
<p>Trajectory tracking error with 5 N perturbation.</p> Full article ">Figure 24
<p>Force applied on the surface (<b>left</b>) and force error generated (<b>right</b>) with 5 N perturbation.</p> Full article ">Figure 25
<p>RMSE: classical PID control (<b>left</b>) vs. PID+NN control (<b>right</b>).</p> Full article ">
<p>3D model of the six-axis robot with an automated fiber placement head.</p> Full article ">Figure 2
<p>Tool-workpiece contact state during AFP process.</p> Full article ">Figure 3
<p>Rigid 6-axis force/torque sensor [<a href="#B59-robotics-13-00105" class="html-bibr">59</a>].</p> Full article ">Figure 4
<p>Block diagram for force/position control by artificial neural network and classical control.</p> Full article ">Figure 5
<p>NN topology for (<b>a</b>) position control, and (<b>b</b>) force control.</p> Full article ">Figure 6
<p>NN cost function in (<b>a</b>) 3D, and (<b>b</b>) 2D.</p> Full article ">Figure 7
<p>Turbine blade section used for simulation force/position control tests.</p> Full article ">Figure 8
<p>Proposed methodology for the generation and validation of complex trajectories.</p> Full article ">Figure 9
<p>Snapshots of the robot manipulator during the trajectory tracking along a complex surface for (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> s, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>648</mn> </mrow> </semantics></math> s, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>775</mn> </mrow> </semantics></math> s, and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>1700</mn> </mrow> </semantics></math> s.</p> Full article ">Figure 10
<p>Complex trajectory tracking in Cartesian space: real trajectory <math display="inline"><semantics> <mrow> <mi>x</mi> <mi>y</mi> <msub> <mi>z</mi> <mi>r</mi> </msub> </mrow> </semantics></math> (green solid line) vs. desired trajectory <math display="inline"><semantics> <mrow> <mi>x</mi> <mi>y</mi> <msub> <mi>z</mi> <mi>d</mi> </msub> </mrow> </semantics></math> (red dotted line).</p> Full article ">Figure 11
<p>Position tracking (<b>left</b>) y error position tracking (<b>right</b>) on the axes <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math>.</p> Full article ">Figure 12
<p>Movement of the six joints and angular (<b>left</b>) angular errors (<b>right</b>) of the manipulator robot.</p> Full article ">Figure 13
<p>Real linear velocity <math display="inline"><semantics> <msub> <mi>v</mi> <mi>r</mi> </msub> </semantics></math> (green solid line) vs. desired <math display="inline"><semantics> <msub> <mi>v</mi> <mi>d</mi> </msub> </semantics></math> (red dotted line).</p> Full article ">Figure 14
<p>Numerical results of orientation (<b>left</b>) and force (<b>right</b>) along the complex surface. Actual orientation <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>r</mi> </msub> </semantics></math> (green solid line) desired <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>d</mi> </msub> </semantics></math> (red dotted line). Real compaction force <math display="inline"><semantics> <msub> <mi>f</mi> <mi>r</mi> </msub> </semantics></math> (green solid line) vs. desired <math display="inline"><semantics> <msub> <mi>f</mi> <mi>d</mi> </msub> </semantics></math> (red dotted line).</p> Full article ">Figure 15
<p>Control torques values.</p> Full article ">Figure 16
<p>Trajectory tracking without peturbation. Reference (red dotted line) vs. PID (blue solid line) vs. PID+NN (green solid line).</p> Full article ">Figure 17
<p>Trajectory tracking error without perturbation.</p> Full article ">Figure 18
<p>Force applied on the surface (<b>left</b>) and force error generated (<b>right</b>) without perturbation.</p> Full article ">Figure 19
<p>Trajectory tracking with variable perturbation of 1 N. Reference (red dotted line) vs. PID (blue solid line) vs. PID+NN (green solid line).</p> Full article ">Figure 20
<p>Trajectory tracking error with 1 N perturbation.</p> Full article ">Figure 21
<p>Force applied on the surface (<b>left</b>) and force error generated (<b>right</b>) with 1 N perturbation.</p> Full article ">Figure 22
<p>Trajectory tracking with variable perturbation of 5 N. Reference (red dotted line) vs. PID (blue solid line) vs. PID+NN (green solid line).</p> Full article ">Figure 23
<p>Trajectory tracking error with 5 N perturbation.</p> Full article ">Figure 24
<p>Force applied on the surface (<b>left</b>) and force error generated (<b>right</b>) with 5 N perturbation.</p> Full article ">Figure 25
<p>RMSE: classical PID control (<b>left</b>) vs. PID+NN control (<b>right</b>).</p> Full article ">
Open AccessArticle
A Framework for Enhanced Human–Robot Collaboration during Disassembly Using Digital Twin and Virtual Reality
by
Timon Hoebert, Stephan Seibel, Manuel Amersdorfer, Markus Vincze, Wilfried Lepuschitz and Munir Merdan
Robotics 2024, 13(7), 104; https://doi.org/10.3390/robotics13070104 - 12 Jul 2024
Abstract
This paper presents a framework that integrates digital twin and virtual reality (VR) technologies to improve the efficiency and safety of human–robot collaborative systems in the disassembly domain. With the increasing complexity of the handling of end-of-life electronic products and as the related
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This paper presents a framework that integrates digital twin and virtual reality (VR) technologies to improve the efficiency and safety of human–robot collaborative systems in the disassembly domain. With the increasing complexity of the handling of end-of-life electronic products and as the related disassembly tasks are characterized by variabilities such as rust, deformation, and diverse part geometries, traditional industrial robots face significant challenges in this domain. These challenges require adaptable and flexible automation solutions that can work safely alongside human workers. We developed an architecture to address these challenges and support system configuration, training, and operational monitoring. Our framework incorporates a digital twin to provide a real-time virtual representation of the physical disassembly process, allowing for immediate feedback and dynamic adjustment of operations. In addition, VR is used to simulate and optimize the workspace layout, improve human–robot interaction, and facilitate safe and effective training scenarios without the need for physical prototypes. A unique case study is presented, where the collaborative system is specifically applied to the disassembly of antenna amplifiers, illustrating the potential of our comprehensive approach to facilitate engineering processes and enhance collaborative safety.
Full article
(This article belongs to the Special Issue Digital Twin-Based Human–Robot Collaborative Systems)
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Show Figures
![](https://pub.mdpi-res.com/robotics/robotics-13-00104/article_deploy/html/images/robotics-13-00104-g001-550.jpg?1720764865)
Figure 1
Figure 1
<p>The core components of the framework, each listed with their capabilities and implementation technologies: high-level control consisting of the world model and decision making, the vision system, digital twin with VR Interface, and the lowlLevel control. The interfaces between each component are shown as black lines.</p> Full article ">Figure 2
<p>The core classes of the world model of the disassembly robotics system and their relationships. The unnamed relationships are parent–child relationships (subClassOf).</p> Full article ">Figure 3
<p>The disassembly robotics cell in the virtual (<b>left</b>) and physical worlds (<b>right</b>).</p> Full article ">Figure 4
<p>Information synchronization and transfer (black arrows) between the system components of the physical and physical environments. The detected screws of the vision system are annotated using colored numbers.</p> Full article ">Figure 5
<p>Communication infrastructure between the different system components. (Image sources: ROS logo (<a href="https://www.ros.org/blog/media/" target="_blank">https://www.ros.org/blog/media/</a> (accessed on 31 May 2024)) with permission from Open Source Robotics Foundation, Inc.; Unity logo (<a href="https://unity.com/en/legal/branding-trademarks" target="_blank">https://unity.com/en/legal/branding-trademarks</a> (accessed on 31 May 2024)) with permission from Unity Technologies. HMI (<a href="https://thenounproject.com/icon/noun-hmi-5664890" target="_blank">https://thenounproject.com/icon/noun-hmi-5664890</a> (accessed on 31 May 2024)) by Eris Kusnadi (The Noun Project); Virtual Reality (<a href="https://thenounproject.com/icon/virtual-reality-4177349" target="_blank">https://thenounproject.com/icon/virtual-reality-4177349</a> (accessed on 31 May 2024)) by Abdulloh Fauzan (The Noun Project); Dual Camera (<a href="https://thenounproject.com/icon/noun-dual-camera-598787" target="_blank">https://thenounproject.com/icon/noun-dual-camera-598787</a> (accessed on 31 May 2024)) by Nikita Cherednikov (The Noun Project); Robotic (<a href="https://thenounproject.com/icon/robotic-5610082" target="_blank">https://thenounproject.com/icon/robotic-5610082</a> (accessed on 31 May 2024)) by Rukanicon (The Noun Project); Screwdriver (<a href="https://thenounproject.com/icon/screwdriver-4104291" target="_blank">https://thenounproject.com/icon/screwdriver-4104291</a> (accessed on 31 May 2024)) by DinosoftLabs (The Noun Project). All icons from The Noun Project are licensed under CC BY 3.0 (<a href="https://creativecommons.org/licenses/by/3.0/" target="_blank">https://creativecommons.org/licenses/by/3.0/</a> (accessed on 31 May 2024))).</p> Full article ">Figure 6
<p>The reachability simulation of the robot in the work cell shown in green.</p> Full article ">Figure 7
<p>Virtual reality interface for program testing. The user can spatially interact with each object and scroll through time between the initial state and the goal state computed by the planner.</p> Full article ">Figure 8
<p>Instructions generated for a human worker to remove a screw and report back if the removal was successful.</p> Full article ">Figure 9
<p>Trajectory visualization functionality for collision avoidance. The spatial motion of the robot tool center point generates a pink sweep geometry to oversee possible collisions without monitoring all the robot’s movements.</p> Full article ">Figure 10
<p>Photo (<b>left</b>) and visualization (<b>right</b>) of the digital twin during the disassembly of an antenna amplifier at the same time instance. The antenna amplifier is represented as a point cloud from the RGB-D camera. The screws that have already been removed are shown in dark purple, and the screws still to be removed in bright purple.</p> Full article ">Figure 11
<p>Division of tasks in the disassembly use case: if the robot’s actions (<b>left</b>) for unscrewing and removal fail, the robot stops, and the human worker (<b>right</b>) is instructed to take over.</p> Full article ">
<p>The core components of the framework, each listed with their capabilities and implementation technologies: high-level control consisting of the world model and decision making, the vision system, digital twin with VR Interface, and the lowlLevel control. The interfaces between each component are shown as black lines.</p> Full article ">Figure 2
<p>The core classes of the world model of the disassembly robotics system and their relationships. The unnamed relationships are parent–child relationships (subClassOf).</p> Full article ">Figure 3
<p>The disassembly robotics cell in the virtual (<b>left</b>) and physical worlds (<b>right</b>).</p> Full article ">Figure 4
<p>Information synchronization and transfer (black arrows) between the system components of the physical and physical environments. The detected screws of the vision system are annotated using colored numbers.</p> Full article ">Figure 5
<p>Communication infrastructure between the different system components. (Image sources: ROS logo (<a href="https://www.ros.org/blog/media/" target="_blank">https://www.ros.org/blog/media/</a> (accessed on 31 May 2024)) with permission from Open Source Robotics Foundation, Inc.; Unity logo (<a href="https://unity.com/en/legal/branding-trademarks" target="_blank">https://unity.com/en/legal/branding-trademarks</a> (accessed on 31 May 2024)) with permission from Unity Technologies. HMI (<a href="https://thenounproject.com/icon/noun-hmi-5664890" target="_blank">https://thenounproject.com/icon/noun-hmi-5664890</a> (accessed on 31 May 2024)) by Eris Kusnadi (The Noun Project); Virtual Reality (<a href="https://thenounproject.com/icon/virtual-reality-4177349" target="_blank">https://thenounproject.com/icon/virtual-reality-4177349</a> (accessed on 31 May 2024)) by Abdulloh Fauzan (The Noun Project); Dual Camera (<a href="https://thenounproject.com/icon/noun-dual-camera-598787" target="_blank">https://thenounproject.com/icon/noun-dual-camera-598787</a> (accessed on 31 May 2024)) by Nikita Cherednikov (The Noun Project); Robotic (<a href="https://thenounproject.com/icon/robotic-5610082" target="_blank">https://thenounproject.com/icon/robotic-5610082</a> (accessed on 31 May 2024)) by Rukanicon (The Noun Project); Screwdriver (<a href="https://thenounproject.com/icon/screwdriver-4104291" target="_blank">https://thenounproject.com/icon/screwdriver-4104291</a> (accessed on 31 May 2024)) by DinosoftLabs (The Noun Project). All icons from The Noun Project are licensed under CC BY 3.0 (<a href="https://creativecommons.org/licenses/by/3.0/" target="_blank">https://creativecommons.org/licenses/by/3.0/</a> (accessed on 31 May 2024))).</p> Full article ">Figure 6
<p>The reachability simulation of the robot in the work cell shown in green.</p> Full article ">Figure 7
<p>Virtual reality interface for program testing. The user can spatially interact with each object and scroll through time between the initial state and the goal state computed by the planner.</p> Full article ">Figure 8
<p>Instructions generated for a human worker to remove a screw and report back if the removal was successful.</p> Full article ">Figure 9
<p>Trajectory visualization functionality for collision avoidance. The spatial motion of the robot tool center point generates a pink sweep geometry to oversee possible collisions without monitoring all the robot’s movements.</p> Full article ">Figure 10
<p>Photo (<b>left</b>) and visualization (<b>right</b>) of the digital twin during the disassembly of an antenna amplifier at the same time instance. The antenna amplifier is represented as a point cloud from the RGB-D camera. The screws that have already been removed are shown in dark purple, and the screws still to be removed in bright purple.</p> Full article ">Figure 11
<p>Division of tasks in the disassembly use case: if the robot’s actions (<b>left</b>) for unscrewing and removal fail, the robot stops, and the human worker (<b>right</b>) is instructed to take over.</p> Full article ">
Open AccessArticle
Development, Experimental, and Numerical Characterisation of Novel Flexible Strain Sensors for Soft Robotics Applications
by
Sylvester Ndidiamaka Nnadi, Ivor Ajadalu, Amir Rahmani, Aliyu Aliyu, Khaled Elgeneidy, Allahyar Montazeri and Behnaz Sohani
Robotics 2024, 13(7), 103; https://doi.org/10.3390/robotics13070103 - 11 Jul 2024
Abstract
Medical and agricultural robots that interact with living tissue or pick fruit require tactile and flexible sensors to minimise or eliminate damage. Until recently, research has focused on the development of robots made of rigid materials, such as metal or plastic. Due to
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Medical and agricultural robots that interact with living tissue or pick fruit require tactile and flexible sensors to minimise or eliminate damage. Until recently, research has focused on the development of robots made of rigid materials, such as metal or plastic. Due to their complex configuration, poor spatial adaptability and low flexibility, rigid robots are not fully applicable in some special environments such as limb rehabilitation, fragile objects gripping, human–machine interaction, and locomotion. All these should be done in an accurate and safe manner for them to be useful. However, the design and manufacture of soft robot parts that interact with living tissue or fragile objects is not as straightforward. Given that hyper-elasticity and conductivity are involved, conventional (subtractive) manufacturing can result in wasted materials (which are expensive), incompatible parts due to different physical properties, and high costs. In this work, additive manufacturing (3D printing) is used to produce a conductive, composite flexible sensor. Its electrical response was tested based on various physical conditions. Finite element analysis (FEA) was used to characterise its deformation and stress behaviour for optimisation to achieve functionality and durability. Also, a nonlinear regression model was developed for the sensor’s performance.
Full article
(This article belongs to the Special Issue Soft Robotics: Fusing Function with Structure)
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Show Figures
![](https://pub.mdpi-res.com/robotics/robotics-13-00103/article_deploy/html/images/robotics-13-00103-g001-550.jpg?1720693604)
Figure 1
Figure 1
<p>Design 2, two turns: (<b>a</b>) track width = 1.5 mm; height = 0.8 mm; track spacing = 1 mm; (<b>b</b>) track width = 1.5 mm; height = 1.2 mm; track spacing = 1 mm; (<b>c</b>) track width = 1.5 mm; height = 1.6 mm; track spacing = 1 mm; (<b>d</b>) track width = 1.5 mm; height = 2.0 mm; track spacing = 1 mm.</p> Full article ">Figure 2
<p>(<b>a</b>) The fabrication and printing procedures for a flexible strain sensor; (<b>b</b>) 3D-printed flexible strain sensors with an industrial strain sensor for experiments [<a href="#B2-robotics-13-00103" class="html-bibr">2</a>].</p> Full article ">Figure 3
<p>The 3D models of fixed curvature testing pieces for the experiment.</p> Full article ">Figure 4
<p>(<b>a</b>) Circuit diagram of data acquisition circuit (DAQ) for experiments. (<b>b</b>) Sensor testing setup [<a href="#B22-robotics-13-00103" class="html-bibr">22</a>].</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>) Circuit diagram of data acquisition circuit (DAQ) for experiments. (<b>b</b>) Sensor testing setup [<a href="#B22-robotics-13-00103" class="html-bibr">22</a>].</p> Full article ">Figure 5
<p>Mesh convergence—maximum deformation vs. mesh elements.</p> Full article ">Figure 6
<p>Showing simulation boundary limits: (<b>a</b>) fixed support and (<b>b</b>) applied force.</p> Full article ">Figure 7
<p>All flexible strain sensor resistance measurements for (<b>a</b>) flat positions (<b>b</b>) 20 mm curvature.</p> Full article ">Figure 8
<p>Signal conditioning and processing of time series data for the flexible strain sensors.</p> Full article ">Figure 9
<p>Showing maximum deflections for one turn: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; (<b>d</b>) 2.0 mm.</p> Full article ">Figure 10
<p>Cross-plot of experimental and predicted dimensionless resistance analysis of the data.</p> Full article ">Figure 11
<p>The novel strain sensors with the rehabilitation hand glove.</p> Full article ">Figure 12
<p>Rehabilitation hand glove.</p> Full article ">Figure A1
<p>Showing design 1, one turn: (<b>a</b>) track width = 2.5 mm; height = 0.8 mm; track spacing = 2 mm; (<b>b</b>) track width = 2.5 mm; height = 1.2 mm; track spacing = 2 mm; (<b>c</b>) track width = 2.5 mm; height = 0.8 mm; track spacing = 2 mm; (<b>d</b>) track width = 2.5 mm; height = 0.8 mm; track spacing = 2 mm.</p> Full article ">Figure A2
<p>Showing design 3, three turns: (<b>a</b>) track width = 0.9 mm; height = 0.8 mm; track spacing = 0.9 mm; (<b>b</b>) track width = 0.9 mm; height = 1.2 mm; track spacing = 0.9 mm; (<b>c</b>) track width = 0.9 mm; height = 1.6 mm; track spacing = 0.9 mm; (<b>d</b>) track width = 0.9 mm; height = 2.0 mm; track spacing = 0.9 mm.</p> Full article ">Figure A3
<p>All flexible strain sensors’ resistance measurements for (<b>a</b>) 40 mm curvature; (<b>b</b>) 60 mm curvature; and (<b>c</b>) 80 mm curvature.</p> Full article ">Figure A4
<p>(<b>a</b>) Impacts of thickness, one turn, and curvatures on resistance. (<b>b</b>) Impacts of thickness, two turns, and curvatures on resistance. (<b>c</b>) Impacts of thickness, three turns, and curvatures on resistance.</p> Full article ">Figure A5
<p>Impacts of curvatures on resistance of the industrial sensor.</p> Full article ">Figure A6
<p>(<b>a</b>) SD as a percentage of the mean resistance of flexible strain sensors in turn one. (<b>b</b>) SD as a percentage of the mean resistance of flexible strain sensors in turn two. (<b>c</b>) SD as a percentage of the mean resistance of flexible strain sensors in turn three.</p> Full article ">Figure A7
<p>Showing maximum deflections for two turns: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; and (<b>d</b>) 2.0 mm.</p> Full article ">Figure A8
<p>Showing maximum deformations for three turns: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; and (<b>d</b>) 2.0 mm.</p> Full article ">Figure A9
<p>Showing maximum equivalent stress for one turn: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; and (<b>d</b>) 2.0 mm.</p> Full article ">Figure A10
<p>Showing maximum equivalent stress for two turns: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; and (<b>d</b>) 2.0 mm.</p> Full article ">Figure A11
<p>Showing maximum equivalent stress for three turns: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; and (<b>d</b>) 2.0 mm.</p> Full article ">
<p>Design 2, two turns: (<b>a</b>) track width = 1.5 mm; height = 0.8 mm; track spacing = 1 mm; (<b>b</b>) track width = 1.5 mm; height = 1.2 mm; track spacing = 1 mm; (<b>c</b>) track width = 1.5 mm; height = 1.6 mm; track spacing = 1 mm; (<b>d</b>) track width = 1.5 mm; height = 2.0 mm; track spacing = 1 mm.</p> Full article ">Figure 2
<p>(<b>a</b>) The fabrication and printing procedures for a flexible strain sensor; (<b>b</b>) 3D-printed flexible strain sensors with an industrial strain sensor for experiments [<a href="#B2-robotics-13-00103" class="html-bibr">2</a>].</p> Full article ">Figure 3
<p>The 3D models of fixed curvature testing pieces for the experiment.</p> Full article ">Figure 4
<p>(<b>a</b>) Circuit diagram of data acquisition circuit (DAQ) for experiments. (<b>b</b>) Sensor testing setup [<a href="#B22-robotics-13-00103" class="html-bibr">22</a>].</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>) Circuit diagram of data acquisition circuit (DAQ) for experiments. (<b>b</b>) Sensor testing setup [<a href="#B22-robotics-13-00103" class="html-bibr">22</a>].</p> Full article ">Figure 5
<p>Mesh convergence—maximum deformation vs. mesh elements.</p> Full article ">Figure 6
<p>Showing simulation boundary limits: (<b>a</b>) fixed support and (<b>b</b>) applied force.</p> Full article ">Figure 7
<p>All flexible strain sensor resistance measurements for (<b>a</b>) flat positions (<b>b</b>) 20 mm curvature.</p> Full article ">Figure 8
<p>Signal conditioning and processing of time series data for the flexible strain sensors.</p> Full article ">Figure 9
<p>Showing maximum deflections for one turn: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; (<b>d</b>) 2.0 mm.</p> Full article ">Figure 10
<p>Cross-plot of experimental and predicted dimensionless resistance analysis of the data.</p> Full article ">Figure 11
<p>The novel strain sensors with the rehabilitation hand glove.</p> Full article ">Figure 12
<p>Rehabilitation hand glove.</p> Full article ">Figure A1
<p>Showing design 1, one turn: (<b>a</b>) track width = 2.5 mm; height = 0.8 mm; track spacing = 2 mm; (<b>b</b>) track width = 2.5 mm; height = 1.2 mm; track spacing = 2 mm; (<b>c</b>) track width = 2.5 mm; height = 0.8 mm; track spacing = 2 mm; (<b>d</b>) track width = 2.5 mm; height = 0.8 mm; track spacing = 2 mm.</p> Full article ">Figure A2
<p>Showing design 3, three turns: (<b>a</b>) track width = 0.9 mm; height = 0.8 mm; track spacing = 0.9 mm; (<b>b</b>) track width = 0.9 mm; height = 1.2 mm; track spacing = 0.9 mm; (<b>c</b>) track width = 0.9 mm; height = 1.6 mm; track spacing = 0.9 mm; (<b>d</b>) track width = 0.9 mm; height = 2.0 mm; track spacing = 0.9 mm.</p> Full article ">Figure A3
<p>All flexible strain sensors’ resistance measurements for (<b>a</b>) 40 mm curvature; (<b>b</b>) 60 mm curvature; and (<b>c</b>) 80 mm curvature.</p> Full article ">Figure A4
<p>(<b>a</b>) Impacts of thickness, one turn, and curvatures on resistance. (<b>b</b>) Impacts of thickness, two turns, and curvatures on resistance. (<b>c</b>) Impacts of thickness, three turns, and curvatures on resistance.</p> Full article ">Figure A5
<p>Impacts of curvatures on resistance of the industrial sensor.</p> Full article ">Figure A6
<p>(<b>a</b>) SD as a percentage of the mean resistance of flexible strain sensors in turn one. (<b>b</b>) SD as a percentage of the mean resistance of flexible strain sensors in turn two. (<b>c</b>) SD as a percentage of the mean resistance of flexible strain sensors in turn three.</p> Full article ">Figure A7
<p>Showing maximum deflections for two turns: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; and (<b>d</b>) 2.0 mm.</p> Full article ">Figure A8
<p>Showing maximum deformations for three turns: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; and (<b>d</b>) 2.0 mm.</p> Full article ">Figure A9
<p>Showing maximum equivalent stress for one turn: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; and (<b>d</b>) 2.0 mm.</p> Full article ">Figure A10
<p>Showing maximum equivalent stress for two turns: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; and (<b>d</b>) 2.0 mm.</p> Full article ">Figure A11
<p>Showing maximum equivalent stress for three turns: (<b>a</b>) 0.8 mm; (<b>b</b>) 1.2 mm; (<b>c</b>) 1.6 mm; and (<b>d</b>) 2.0 mm.</p> Full article ">
Open AccessArticle
Semantic 3D Reconstruction for Volumetric Modeling of Defects in Construction Sites
by
Dimitrios Katsatos, Paschalis Charalampous, Patrick Schmidt, Ioannis Kostavelis, Dimitrios Giakoumis, Lazaros Nalpantidis and Dimitrios Tzovaras
Robotics 2024, 13(7), 102; https://doi.org/10.3390/robotics13070102 - 11 Jul 2024
Abstract
The appearance of construction defects in buildings can arise from a variety of factors, ranging from issues during the design and construction phases to problems that develop over time with the lifecycle of a building. These defects require repairs, often in the context
[...] Read more.
The appearance of construction defects in buildings can arise from a variety of factors, ranging from issues during the design and construction phases to problems that develop over time with the lifecycle of a building. These defects require repairs, often in the context of a significant shortage of skilled labor. In addition, such work is often physically demanding and carried out in hazardous environments. Consequently, adopting autonomous robotic systems in the construction industry becomes essential, as they can relieve labor shortages, promote safety, and enhance the quality and efficiency of repair and maintenance tasks. Hereupon, the present study introduces an end-to-end framework towards the automation of shotcreting tasks in cases where construction or repair actions are required. The proposed system can scan a construction scene using a stereo-vision camera mounted on a robotic platform, identify regions of defects, and reconstruct a 3D model of these areas. Furthermore, it automatically calculates the required 3D volumes to be constructed to treat a detected defect. To achieve all of the above-mentioned technological tools, the developed software framework employs semantic segmentation and 3D reconstruction modules based on YOLOv8m-seg, SiamMask, InfiniTAM, and RTAB-Map, respectively. In addition, the segmented 3D regions are processed by the volumetric modeling component, which determines the amount of concrete needed to fill the defects. It generates the exact 3D model that can repair the investigated defect. Finally, the precision and effectiveness of the proposed pipeline are evaluated in actual construction site scenarios, featuring reinforcement bars as defective areas.
Full article
(This article belongs to the Special Issue Localization and 3D Mapping of Intelligent Robotics)
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![](https://pub.mdpi-res.com/robotics/robotics-13-00102/article_deploy/html/images/robotics-13-00102-g001-550.jpg?1720682933)
Figure 1
Figure 1
<p>In (<b>a,c</b>), the construction of tunnels is depicted, while (<b>b</b>) shows the construction of ground support walls. All images feature exposed reinforcement bars and highlight the labor-intensive process of spraying concrete (shotcreting) to fill the surface [<a href="#B9-robotics-13-00102" class="html-bibr">9</a>].</p> Full article ">Figure 2
<p>Workflow of the volumetric modeling approach.</p> Full article ">Figure 3
<p>(<b>a</b>) Main concept of the missing volume computation (<b>b</b>) Typical projected mesh of a defected region and (<b>c</b>) Generated 3D model repairing the defect.</p> Full article ">Figure 4
<p>(<b>a</b>) Theoretical point cloud construction methodology and (<b>b</b>) Typical computed 3D model and its corresponding point cloud representation.</p> Full article ">Figure 5
<p>(<b>a</b>) Cyan-labeled mask area in RGB encoding. (<b>b</b>) Corresponding mask area in binary format. (<b>c</b>) Sensor’s raw depth image. (<b>d</b>) Cropped depth image based on binary mask.</p> Full article ">Figure 6
<p>In the outdoor scene in Greece, the left image shows Testbed 01-a featuring a replica rebar structure, while the right image depicts Testbed 01-b with actual reinforcement bars used in building construction. Both rebars are installed in a custom wooden frame.</p> Full article ">Figure 7
<p>The semi-indoor scene in Denmark serves as Testbed 02, situated within the construction site. Here, wooden frames with exposed reinforcement bars are prepared for shotcreting.</p> Full article ">Figure 8
<p>Testbed 03 is a semi-indoor scene, where the surface presents various flaws requiring proper treatment.</p> Full article ">Figure 9
<p>On the left: Robotnik Summit XL platform equipped with a Roboception RC-Visard 160 stereo camera mounted on the side. On the right: real-time testing of the integrated system during the experimental session in Testbed 01-a.</p> Full article ">Figure 10
<p>Three-dimensional views of semantic 3D reconstruction. <b>Top</b> sequence: results for Testbed 01-a (<b>left</b>) and Testbed 01-b (<b>right</b>). <b>Bottom</b> sequence: results for Testbed 02.</p> Full article ">Figure 11
<p>(<b>a</b>) Projected mesh of the investigated cases and (<b>b</b>) registered 3D generated models on the input scenes.</p> Full article ">Figure 12
<p>(<b>a</b>) Computation of the projected mesh in each defected region and (<b>b</b>) generation of the appropriate 3D model on the scene with multiple defects.</p> Full article ">Figure 13
<p>Registration and alignment of the generated point clouds in the 3D reconstructed scene.</p> Full article ">Figure 14
<p>Comparison between the automatically computed 3D models and the theoretical ones for (<b>a</b>) Testbed 01-b, (<b>b</b>) Testbed 01-a, (<b>c</b>) Testbed 02 (LR) and Testbed 02 (RR), (<b>d</b>) Testbed 03.</p> Full article ">
<p>In (<b>a,c</b>), the construction of tunnels is depicted, while (<b>b</b>) shows the construction of ground support walls. All images feature exposed reinforcement bars and highlight the labor-intensive process of spraying concrete (shotcreting) to fill the surface [<a href="#B9-robotics-13-00102" class="html-bibr">9</a>].</p> Full article ">Figure 2
<p>Workflow of the volumetric modeling approach.</p> Full article ">Figure 3
<p>(<b>a</b>) Main concept of the missing volume computation (<b>b</b>) Typical projected mesh of a defected region and (<b>c</b>) Generated 3D model repairing the defect.</p> Full article ">Figure 4
<p>(<b>a</b>) Theoretical point cloud construction methodology and (<b>b</b>) Typical computed 3D model and its corresponding point cloud representation.</p> Full article ">Figure 5
<p>(<b>a</b>) Cyan-labeled mask area in RGB encoding. (<b>b</b>) Corresponding mask area in binary format. (<b>c</b>) Sensor’s raw depth image. (<b>d</b>) Cropped depth image based on binary mask.</p> Full article ">Figure 6
<p>In the outdoor scene in Greece, the left image shows Testbed 01-a featuring a replica rebar structure, while the right image depicts Testbed 01-b with actual reinforcement bars used in building construction. Both rebars are installed in a custom wooden frame.</p> Full article ">Figure 7
<p>The semi-indoor scene in Denmark serves as Testbed 02, situated within the construction site. Here, wooden frames with exposed reinforcement bars are prepared for shotcreting.</p> Full article ">Figure 8
<p>Testbed 03 is a semi-indoor scene, where the surface presents various flaws requiring proper treatment.</p> Full article ">Figure 9
<p>On the left: Robotnik Summit XL platform equipped with a Roboception RC-Visard 160 stereo camera mounted on the side. On the right: real-time testing of the integrated system during the experimental session in Testbed 01-a.</p> Full article ">Figure 10
<p>Three-dimensional views of semantic 3D reconstruction. <b>Top</b> sequence: results for Testbed 01-a (<b>left</b>) and Testbed 01-b (<b>right</b>). <b>Bottom</b> sequence: results for Testbed 02.</p> Full article ">Figure 11
<p>(<b>a</b>) Projected mesh of the investigated cases and (<b>b</b>) registered 3D generated models on the input scenes.</p> Full article ">Figure 12
<p>(<b>a</b>) Computation of the projected mesh in each defected region and (<b>b</b>) generation of the appropriate 3D model on the scene with multiple defects.</p> Full article ">Figure 13
<p>Registration and alignment of the generated point clouds in the 3D reconstructed scene.</p> Full article ">Figure 14
<p>Comparison between the automatically computed 3D models and the theoretical ones for (<b>a</b>) Testbed 01-b, (<b>b</b>) Testbed 01-a, (<b>c</b>) Testbed 02 (LR) and Testbed 02 (RR), (<b>d</b>) Testbed 03.</p> Full article ">
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