Search Results (1,302)

When a robot performs tasks such as assembly or human–robot interaction, it is inevitable for it to collide with the unknown environment, resulting in potential safety hazards. In order to improve the compliance of robots to cope with unknown environments and enhance their intelligence in contact force-sensitive tasks, this paper proposes an improved admittance force control method, which combines classical adaptive control and machine learning methods to make them use their respective advantages in different stages of training and, ultimately, achieve better performance. In addition, this paper proposes an improved Deep Deterministic Policy Gradient (DDPG)-based optimizer, which is combined with the Gaussian process (GP) model to optimize the admittance parameters. In order to verify the feasibility of the algorithm, simulations and experiments are carried out in MATLAB and on a UR10e robot, respectively. The experimental results show that the algorithm improves the convergence speed by 33% in comparison to the general model-free learning method, and has better control performance and robustness. Finally, the adjustment time required by the algorithm is 44% shorter than that of classical adaptive admittance control. Full article

In the Industry 4.0 scenario, human–robot collaboration (HRC) plays a key role in factories to reduce costs, increase production, and help aged and/or sick workers maintain their job. The approaches of the ISO 11228 series commonly used for biomechanical risk assessments cannot be applied in Industry 4.0, as they do not involve interactions between workers and HRC technologies. The use of wearable sensor networks and software for biomechanical risk assessments could help us develop a more reliable idea about the effectiveness of collaborative robots (coBots) in reducing the biomechanical load for workers. The aim of the present study was to investigate some biomechanical parameters with the 3D Static Strength Prediction Program (3DSSPP) software v.7.1.3, on workers executing a practical manual material-handling task, by comparing a dual-arm coBot-assisted scenario with a no-coBot scenario. In this study, we calculated the mean and the standard deviation (SD) values from eleven participants for some 3DSSPP parameters. We considered the following parameters: the percentage of maximum voluntary contraction (%MVC), the maximum allowed static exertion time (MaxST), the low-back spine compression forces at the L4/L5 level (L4Ort), and the strength percent capable value (SPC). The advantages of introducing the coBot, according to our statistics, concerned trunk flexion (SPC from 85.8% without coBot to 95.2%; %MVC from 63.5% without coBot to 43.4%; MaxST from 33.9 s without coBot to 86.2 s), left shoulder abdo-adduction (%MVC from 46.1% without coBot to 32.6%; MaxST from 32.7 s without coBot to 65 s), and right shoulder abdo-adduction (%MVC from 43.9% without coBot to 30.0%; MaxST from 37.2 s without coBot to 70.7 s) in Phase 1, and right shoulder humeral rotation (%MVC from 68.4% without coBot to 7.4%; MaxST from 873.0 s without coBot to 125.2 s), right shoulder abdo-adduction (%MVC from 31.0% without coBot to 18.3%; MaxST from 60.3 s without coBot to 183.6 s), and right wrist flexion/extension rotation (%MVC from 50.2% without coBot to 3.0%; MaxST from 58.8 s without coBot to 1200.0 s) in Phase 2. Moreover, Phase 3, which consisted of another manual handling task, would be removed by using a coBot. In summary, using a coBot in this industrial scenario would reduce the biomechanical risk for workers, particularly for the trunk, both shoulders, and the right wrist. Finally, the 3DSSPP software could be an easy, fast, and costless tool for biomechanical risk assessments in an Industry 4.0 scenario where ISO 11228 series cannot be applied; it could be used by occupational medicine physicians and health and safety technicians, and could also help employers to justify a long-term investment. Full article

Interaction errors are hard to avoid in the process of human–robot interaction (HRI). User emotions toward interaction errors could further affect the user’s attitudes to robots and experiences of HRI and so on. In this regard, the present study explores the effects of different factors on user emotions when interaction errors occur in HRI. There is sparse research directly studying this perspective. In so doing, three factors, including robot feedback, passive and active contexts, and previous user emotions, were considered. Two stages of online surveys with 465 participants were implemented to explore attitudes to robots and the self-reporting of emotions in active and passive HRI. Then, a Yanshee robot was selected as the experimental platform, and 61 participants were recruited for a real human–robot empirical study based on the two surveys. According to the results of statistical analysis, we conclude some design guides can cope with scenarios of interaction errors. For example, feedback and previous emotions have impacts on user emotions after encountering interaction errors, but contexts do not. There are no interactive effects between the three factors. The approach to reduce negative emotions in the cases of interaction errors in HRI, such as providing irrelevant feedback and so on, is also illustrated in the contributions. Full article

The integration of robots into social environments necessitates their ability to interpret human intentions and anticipate potential outcomes accurately. This capability is particularly crucial for social robots designed for human care, as they may encounter situations that pose significant risks to individuals, such as undetected obstacles in their path. These hazards must be identified and mitigated promptly to ensure human safety. This paper delves into the artificial theory of mind (ATM) approach to inferring and interpreting human intentions within human–robot interaction. We propose a novel algorithm that detects potentially hazardous situations for humans and selects appropriate robotic actions to eliminate these dangers in real time. Our methodology employs a simulation-based approach to ATM, incorporating a “like-me” policy to assign intentions and actions to human subjects. This strategy enables the robot to detect risks and act with a high success rate, even under time-constrained circumstances. The algorithm was seamlessly integrated into an existing robotics cognitive architecture, enhancing its social interaction and risk mitigation capabilities. To evaluate the robustness, precision, and real-time responsiveness of our implementation, we conducted a series of three experiments: (i) A fully simulated scenario to assess the algorithm’s performance in a controlled environment; (ii) A human-in-the-loop hybrid configuration to test the system’s adaptability to real-time human input; and (iii) A real-world scenario to validate the algorithm’s effectiveness in practical applications. These experiments provided comprehensive insights into the algorithm’s performance across various conditions, demonstrating its potential for improving the safety and efficacy of social robots in human care settings. Our findings contribute to the growing research on social robotics and artificial intelligence, offering a promising approach to enhancing human–robot interaction in potentially hazardous environments. Future work may explore the scalability of this algorithm to more complex scenarios and its integration with other advanced robotic systems. Full article

This paper presents the architecture of a multimodal human–robot interaction control platform that leverages the advanced language capabilities of ChatGPT to facilitate more natural and engaging conversations between humans and robots. Implemented on the Pepper humanoid robot, the platform aims to enhance communication by providing a richer and more intuitive interface. The motivation behind this study is to enhance robot performance in human interaction through cutting-edge natural language processing technology, thereby improving public attitudes toward robots, fostering the development and application of robotic technology, and reducing the negative attitudes often associated with human–robot interactions. To validate the system, we conducted experiments measuring negative attitude robot scale and their robot anxiety scale scores before and after interacting with the robot. Statistical analysis of the data revealed a significant improvement in the participants’ attitudes and a notable reduction in anxiety following the interaction, indicating that the system holds promise for fostering more positive human–robot relationships. Full article

This article presents the design and implementation of an innovative human–machine interface (HMI) in mixed reality for a robot model operating within Robot Operating System 2 (ROS 2). The interface is specifically developed for compatibility with Microsoft HoloLens 2 hardware and leverages the Unity game engine alongside the Mixed Reality Toolkit (MRTK) to create an immersive mixed reality application. The project uses the Turtlebot 3 Burger model robot, simulated within the Gazebo virtual environment, as a representative mechatronic system for demonstration purposes. Communication between the mixed reality application and ROS 2 is facilitated through a publish–subscribe mechanism, utilizing ROS TCP Connector for message serialization between nodes. This interface not only enhances the user experience by allowing for the real-time monitoring and control of the robotic system but also aligns with the principles of Industry 5.0, emphasizing human-centric and inclusive technological advancements. The practical outcomes of this research include a fully functional mixed reality application that integrates seamlessly with ROS 2, showcasing the potential of mixed reality technologies in advancing the field of industrial automation and human–machine interaction. Full article

Traditional myoelectric controls of trans-humeral prostheses fail to provide intuitive coordination of the necessary degrees of freedom. We previously showed that by using artificial neural network predictions to reconstruct distal joints, based on the shoulder posture and movement goals (i.e., position and orientation of the targeted object), participants were able to position and orient an avatar hand to grasp objects with natural arm performances. However, this control involved rapid and unintended prosthesis movements at each modification of the movement goal, impractical for real-life scenarios. Here, we eliminate this abrupt change using novel methods based on an angular trajectory, determined from the speed of stump movement and the gap between the current and the ‘goal’ distal configurations. These new controls are tested offline and online (i.e., involving participants-in-the-loop) and compared to performances obtained with a natural control. Despite a slight increase in movement time, the new controls allowed twelve valid participants and six participants with trans-humeral limb loss to reach objects at various positions and orientations without prior training. Furthermore, no usability or workload degradation was perceived by participants with upper limb disabilities. The good performances achieved highlight the potential acceptability and effectiveness of those controls for our target population. Full article

Arm and hand function play a critical role in the successful completion of everyday tasks. Lost function due to neurological impairment impacts millions of lives worldwide. Despite improvements in the ability to assess and rehabilitate arm deficits, knowledge about underlying sources of impairment and related sequela remains limited. The comprehensive assessment of function requires the measurement of both biomechanics and neuromuscular contributors to performance during the completion of tasks that often use multiple joints and span three-dimensional workspaces. To our knowledge, the complexity of movement and diversity of measures required are beyond the capabilities of existing assessment systems. To bridge current gaps in assessment capability, a new exoskeleton instrument is developed with comprehensive bilateral assessment in mind. The development of the BiLateral Upper-limb Exoskeleton for Simultaneous Assessment of Biomechanical and Neuromuscular Output (BLUE SABINO) expands on prior iterations toward full-arm assessment during reach-and-grasp tasks through the development of a dual-arm and dual-hand system, with 9 active degrees of freedom per arm and 12 degrees of freedom (six active, six passive) per hand. Joints are powered by electric motors driven by a real-time control system with input from force and force/torque sensors located at all attachment points between the user and exoskeleton. Biosignals from electromyography and electroencephalography can be simultaneously measured to provide insight into neurological performance during unimanual or bimanual tasks involving arm reach and grasp. Design trade-offs achieve near-human performance in exoskeleton speed and strength, with positional measurement at the wrist having an error of less than 2 mm and supporting a range of motion approximately equivalent to the 50th-percentile human. The system adjustability in seat height, shoulder width, arm length, and orthosis width accommodate subjects from approximately the 5th-percentile female to the 95th-percentile male. Integration between precision actuation, human–robot-interaction force-torque sensing, and biosignal acquisition systems successfully provide the simultaneous measurement of human movement and neurological function. The bilateral design enables use with left- or right-side impairments as well as intra-subject performance comparisons. With the resulting instrument, the authors plan to investigate underlying neural and physiological correlates of arm function, impairment, learning, and recovery. Full article

This paper describes the design and characterisation of a novel hybrid pneumatic rotational actuator that aims to overcome the limitations of both rigid and soft actuators while combining their advantages; indeed, the designed actuator consists of a soft air chamber having an auxetic structure constrained between two rigid frames connected by a soft hinge joint inspired by the musculoskeletal structure of a lobster leg. The main goal is to integrate the advantages of soft actuation, such as inherent compliance and safe human–robot interaction, with those of rigid components, i.e., the robustness and structural stability limiting the ineffective expansion of the soft counterpart of the actuator. The air chamber and its auxetic structure are capable of leveraging the hyper-elastic properties of the soft fabrication material, thereby optimising the response and extending the operational range of the rotational actuator. Each component of the hybrid actuator is fabricated using a 3D-printing method based on Fused Deposition Modeling technology; the soft components are made of thermoplastic polyurethane, and the rigid components are made of polylactic acid. The design phases were followed by some experimental tests to characterise the hybrid actuation by reproducing the typical operating conditions of the actuator itself. In particular, the actuator response in unconstrained expansion and isometric and isobaric conditions has been evaluated. The experimental results show linearity, good repeatability, and sensitivity of the actuator response vs. pneumatic pressure input, other than a small percentage hysteresis, which is ten times less than that observed in commercial soft pneumatic actuators. Full article

Our study presents a novel design for a cable-driven robotic arm, emphasizing low cost, low inertia movement, and long-term cable durability. The robotic arm shares similar specifications with the UR5 robotic arm, featuring a total of six degrees of freedom (DOF) distributed in a 1:1:1:3 ratio at the arm base, shoulder, elbow, and wrist, respectively. The three DOF at the wrist joints are driven by a cable system, with heavy motors relocated from the end-effector to the shoulder base. This repositioning results in a lighter cable-actuated wrist (weighing 0.8 kg), which enhances safety during human interaction and reduces the torque requirements for the elbow and shoulder motors. Consequently, the overall cost and weight of the robotic arm are reduced, achieving a payload-to-body weight ratio of 5:8.4 kg. To ensure good positional repeatability, the shoulder and elbow joints, which influence longer moment arms, are designed with a direct-drive structure. To evaluate the design’s performance, tests were conducted on loading capability, cable durability, position repeatability, and manipulation. The tests demonstrated that the arm could manipulate a 5 kg payload with a positional repeatability error of less than 0.1 mm. Additionally, a novel cable tightener design was introduced, which served dual functions: conveniently tightening the cable and reducing the high-stress concentration near the cable locking end to minimize cable loosening. When subjected to an initial cable tension of 100 kg, this design retained approximately 80% of the load after 10 years at a room temperature of 24 °C. Full article

The paradigm of Industry 5.0 pushes the transition from the traditional to a novel, smart, digital, and connected industry, where well-being is key to enhance productivity, optimize man–machine interaction and guarantee workers’ safety. This work aims to conduct a systematic review of current methodologies for monitoring and analyzing physical and cognitive ergonomics. Three research questions are addressed: (1) which technologies are used to assess the physical and cognitive well-being of workers in the workplace, (2) how the acquired data are processed, and (3) what purpose this well-being is evaluated for. This way, individual factors within the holistic assessment of worker well-being are highlighted, and information is provided synthetically. The analysis was conducted following the PRISMA 2020 statement guidelines. From the sixty-five articles collected, the most adopted (1) technological solutions, (2) parameters, and (3) data analysis and processing were identified. Wearable inertial measurement units and RGB-D cameras are the most prevalent devices used for physical monitoring; in the cognitive ergonomics, and cardiac activity is the most adopted physiological parameter. Furthermore, insights on practical issues and future developments are provided. Future research should focus on developing multi-modal systems that combine these aspects with particular emphasis on their practical application in real industrial settings. Full article

With the development of large language model technologies, the capability of social robots to interact emotionally with users has been steadily increasing. However, the existing research insufficiently examines the influence of robot stance attribution design cues on the construction of users’ mental models and their effects on human–robot interaction (HRI). This study innovatively combines mental models with the associative–propositional evaluation (APE) model, unveiling the impact of the stance attribution explanations of this design cue on the construction of user mental models and the interaction between the two types of mental models through EEG experiments and survey investigations. The results found that under the influence of intentional stance explanations (compared to design stance explanations), participants displayed higher error rates, higher θ- and β-band Event-Related Spectral Perturbations (ERSPs), and phase-locking value (PLV). Intentional stance explanations trigger a primarily associatively based mental model of users towards robots, which conflicts with the propositionally based mental models of individuals. Users might adjust or “correct” their immediate reactions caused by stance attribution explanations after logical analysis. This study reveals that stance attribution interpretation can significantly affect users’ mental model construction of robots, which provides a new theoretical framework for exploring human interaction with non-human agents and provides theoretical support for the sustainable development of human–robot relations. It also provides new ideas for designing robots that are more humane and can better interact with human users. Full article

Human–machine interactions (HMIs) have penetrated into various academic and industrial fields, such as robotics, virtual reality, and wearable electronics. However, the practical application of most human–machine interfaces faces notable obstacles due to their complex structure and materials, high power consumption, limited effective skin adhesion, and high cost. Herein, we report a self-powered, skin adhesive, and flexible human–machine interface based on a triboelectric nanogenerator (SSFHMI). Characterized by its simple structure and low cost, the SSFHMI can easily convert touch stimuli into a stable electrical signal at the trigger pressure from a finger touch, without requiring an external power supply. A skeleton spacer has been specially designed in order to increase the stability and homogeneity of the output signals of each TENG unit and prevent crosstalk between them. Moreover, we constructed a hydrogel adhesive interface with skin-adhesive properties to adapt to easy wear on complex human body surfaces. By integrating the SSFHMI with a microcontroller, a programmable touch operation platform has been constructed that is capable of multiple interactions. These include medical calling, music media playback, security unlocking, and electronic piano playing. This self-powered, cost-effective SSFHMI holds potential relevance for the next generation of highly integrated and sustainable portable smart electronic products and applications. Full article

Using lower limb exoskeletons provides potential advantages in terms of productivity and safety associated with reduced stress. However, complex issues in human–robot interactions are still open, such as the physiological effects of exoskeletons and the impact on the user’s subjective experience. In this work, an innovative exoskeleton, the Wearable Walker, is assessed using the EXPERIENCE benchmarking protocol from the EUROBENCH project. The Wearable Walker is a lower-limb exoskeleton that enhances human abilities, such as carrying loads. The device uses a unique control approach called Blend Control that provides smooth assistance torques. It operates two models simultaneously, one in the case in which the left foot is grounded and another for the grounded right foot. These models generate assistive torques combined to provide continuous and smooth overall assistance, preventing any abrupt changes in torque due to model switching. The EXPERIENCE protocol consists of walking on flat ground while gathering physiological signals, such as heart rate, its variability, respiration rate, and galvanic skin response, and completing a questionnaire. The test was performed with five healthy subjects. The scope of the present study is twofold: to evaluate the specific exoskeleton and its current control system to gain insight into possible improvements and to present a case study for a formal and replicable benchmarking of wearable robots. Full article

In recent decades, the potential of robots’ understanding, perception, learning, and action has been widely expanded due to the integration of artificial intelligence (AI) into almost every system. Cooperation between AI and human beings will be responsible for the bright future of AI technology. Moreover, for a perfect manually or automatically controlled machine or device, the device must perform together with a human through multiple levels of automation and assistance. Humans and robots cooperate or interact in various ways. With the enhancement of robot efficiencies, they can perform more work through an automatic method; therefore, we need to think about cooperation between humans and robots, the required software architectures, and information about the designs of user interfaces. This paper describes the most important strategies of human–robot interactions and the relationships between several control techniques and cooperation techniques using sensor fusion and machine learning (ML). Based on the behavior and thinking of humans, a human–robot interaction (HRI) framework is studied and explored in this article to make attractive, safe, and efficient systems. Additionally, research on intention recognition, compliance control, and perception of the environment by elderly assistive robots for the optimization of HRI is investigated in this paper. Furthermore, we describe the theory of HRI and explain the different kinds of interactions and required details for both humans and robots to perform different kinds of interactions, including the circumstances-based evaluation technique, which is the most important criterion for assistive robots. Full article