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Development and Implementation of the Underwater Robot Enhanced by AI...
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Development and Implementation of the Underwater Robot Enhanced by AI Methods

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 2203

Special Issue Editors

Dr. Zhan Li

E-Mail Website
Guest Editor
Department of Computer Science, Swansea University, Swansea, UK
Interests: neural networks; robotics; machine learning; human movement understanding/sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Hongliang Guo

E-Mail Website
Guest Editor
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research, Singapore 138632, Singapore
Interests: muli-agent systems; mobile robot; reinforcement learning
Dr. Weibing Li

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
Interests: medical robotics
Dr. Chunxu Li

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Interests: robotics; machine learning; computer vision; human–robot interaction; smart materials for soft robot
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of the global economy and technology, there is an increasing demand for tasks such as underwater operations and exploration. Underwater robots, as an advanced underwater work tool, are undertaking increasingly underwater work tasks and have received rapid development and high attention from countries around the world.

To better complete tasks, underwater robots must consider different types of uncertainties and achieve efficient and robust interaction between the environment and the robot. Underwater robots should be able to make autonomous decisions to reduce the burden on operators. For example, robots can automatically adjust their motion control mode, select suitable sensors for data collection, or perform basic task planning based on environmental conditions and task requirements. In addition, virtual reality technology, posture recognition and other technologies can provide operators with more intuitive and immersive underwater robot operating experience. With the development of artificial intelligence technology and machine learning technology, underwater robots will continue to become intelligent, possessing higher levels of autonomous decision-making, autonomous control, and task planning capabilities. At the same time, they will also have multi-agent collaboration functions, which can achieve cooperation, joint cruising, and task execution among multiple robots.

This research topic is dedicated to collecting the latest development and research findings, addressing theoretical and practical issues related to advanced methods, path planning, adaptive control, and signal processing in underwater robot systems. The aim is to provide a platform for researchers and practitioners to showcase and discuss innovative solutions.

Topics of interest include, but are not limited to:

  • Underwater robot control system based on deep learning or reinforcement learning.
  • Teleoperation control of ROV/AUV/AGV
  • Multimodal data fusion for underwater robot control
  • Path planning for ROV/AUV/AGV
  • Collaborative or semi-autonomous control of multiple robots
  • Posture recognition and localization
  • Intelligent wearable and assistive robot devices

Dr. Zhan Li
Dr. Hongliang Guo
Dr. Weibing Li
Dr. Chunxu Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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Research

13 pages, 3931 KiB  
Article
A Visual–Inertial Pressure Fusion-Based Underwater Simultaneous Localization and Mapping System
by Zhufei Lu, Xing Xu, Yihao Luo, Lianghui Ding, Chao Zhou and Jiarong Wang
Sensors 2024, 24(10), 3207; https://doi.org/10.3390/s24103207 - 18 May 2024
Viewed by 629
Abstract
Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based [...] Read more.
Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based on the ORB-SLAM3-VI framework, we propose ORB-SLAM3-VIP, which integrates a depth sensor, an IMU sensor and an optical sensor. This method integrates the measurements of depth sensors and an IMU sensor into the visual SLAM algorithm through tight coupling, and establishes a multi-sensor fusion SLAM model. Depth constraints are introduced into the process of initialization, scale fine-tuning, tracking and mapping to constrain the position of the sensor in the z-axis and improve the accuracy of pose estimation and map scale estimate. The test on seven sets of underwater multi-sensor sequence data in the AQUALOC dataset shows that, compared with ORB-SLAM3-VI, the ORB-SLAM3-VIP system proposed in this paper reduces the scale error in all sequences by up to 41.2%, and reduces the trajectory error by up to 41.2%. The square root has also been reduced by up to 41.6%. Full article
(This article belongs to the Special Issue Development and Implementation of the Underwater Robot Enhanced by AI Methods)
►▼ Show Figures

Figure 1

Figure 1
<p>Factor graph of visual SLAM.</p> Full article ">Figure 2
<p>Framework of g2o.</p> Full article ">Figure 3
<p>The process of SLAM algorithm based on vision–inertial pressure fusion.</p> Full article ">Figure 4
<p>Factor graph representation for inertial pressure optimization.</p> Full article ">Figure 5
<p>Factor graph representation for Visual-Inertial-Pressure optimization.</p> Full article ">Figure 6
<p>Factor graph representation for scale and gravity optimization.</p> Full article ">Figure 7
<p>Initialization and scale refinement flow diagram.</p> Full article ">Figure 8
<p>Relative scale of harbor_01 during optimization.</p> Full article ">Figure 9
<p>Comparison of scale unaligned estimate trajectory for harbor_01 underwater sequence.</p> Full article ">Figure 10
<p>Prediction of the <span class="html-italic">z</span>-axis position for harbor_01 sequence.</p> Full article ">
15 pages, 7755 KiB  
Article
Three-Dimensional Path Planning Based on Six-Direction Search Scheme
by Kene Li, Liuying Li, Chunyi Tang, Wanning Lu and Xiangsuo Fan
Sensors 2024, 24(4), 1193; https://doi.org/10.3390/s24041193 - 12 Feb 2024
Viewed by 911
Abstract
In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is [...] Read more.
In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is constructed based on a neural network and the path energy is introduced to avoid a too-long path being generated. Based on the weighted total energy of obstacle energy and path energy, a six-direction search scheme is designed here for path planning. To improve the efficiency of the six-direction search algorithm, two optimization methods are employed to reduce the number of iterations and total path search time. The first method involves adjusting the search step length dynamically, which helps to decrease the number of iterations needed for path planning. The second method involves reducing the number of path nodes, which can not only decrease the search time but also avoid premature convergence. By implementing these optimization methods, the performance of the six-direction search algorithm is enhanced in favor of path planning with multiple underwater obstacles reasonably. The simulation results validate the effectiveness and efficiency of the six-direction search scheme. Full article
(This article belongs to the Special Issue Development and Implementation of the Underwater Robot Enhanced by AI Methods)
►▼ Show Figures

Figure 1

Figure 1
<p>The neural network structure for the obstacles’ energy.</p> Full article ">Figure 2
<p>A cuboid obstacle.</p> Full article ">Figure 3
<p>Introduction to the six-direction search method. The green arrows indicate the direction the mobile agent. The blue balls indicate the six directions in space.</p> Full article ">Figure 4
<p>Modeling of individual obstacle energy in space. The red line is a straight line from start to end.</p> Full article ">Figure 5
<p>Path planning by SDS scheme with fixed <math display="inline"><semantics> <mrow> <mi>δ</mi> </mrow> </semantics></math> = 0.1.</p> Full article ">Figure 6
<p>Paths synthesized by SDS with <math display="inline"><semantics> <mrow> <mi>δ</mi> </mrow> </semantics></math> = 0.1. The colored lines indicate the generated transient paths during the move.</p> Full article ">Figure 7
<p>Path planning by SDS scheme with fixed <span class="html-italic">δ</span> = 0.01.</p> Full article ">Figure 8
<p>Paths synthesized by SDS with <span class="html-italic">δ</span> = 0.01.</p> Full article ">Figure 9
<p>Path planning by SDS scheme with the variable step length.</p> Full article ">Figure 10
<p>Paths synthesized by SDS with variable step length.</p> Full article ">Figure 11
<p>Path point reduction.</p> Full article ">Figure 12
<p>Paths synthesized by SDS with reduce path node.</p> Full article ">Figure 13
<p>Energy modeling of multiple obstacles in space.</p> Full article ">Figure 14
<p>Multiple obstacle avoidance path.</p> Full article ">Figure 15
<p>Paths generated by the different algorithms.</p> Full article ">Figure 16
<p>Simulation experiment.</p> Full article ">Figure 17
<p>Main view of the robotic arm performing the path following.</p> Full article ">Figure 18
<p>Side view of the robotic arm performing the path following.</p> Full article ">
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