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This paper focuses on improving the ergonomics of industrial workers. It addresses the critical implications of poor ergonomics, which can lead to musculoskeletal disorders over time. A novel methodology for a path-planning algorithm designed for human–robot collaboration was introduced to tackle this challenge. The algorithm's essential contribution lies in determining the most ergonomic path for a robot to guide a human's hand during task execution, facilitating a transition toward an optimized body configuration. The algorithm effectively charts the ergonomic path by adopting a Cartesian path-planning approach and employing the cell decomposition method. The methodology was implemented on a dataset of ten individuals, representing a diverse group of male and female subjects aged between 20 and 35, with one participant being left-handed. The algorithm was applied to three different activities: "stacking an item," "taking an object from a shelf," and "assembling an object by sitting over a table." The results demonstrated a significant improvement in the REBA score (as a measure of ergonomics condition) of the individuals after applying the algorithm. This outcome reinforces the efficacy of the methodology in enhancing the ergonomics of industrial workers. Furthermore, the study compared the performance of A* with three heuristic functions against Dijkstra's algorithm, aiming to identify the most effective approach for achieving optimal ergonomic paths in human–robot collaboration. The findings revealed that A* with a specific heuristic function surpassed Dijkstra's algorithm, underscoring its superiority in this context. The findings highlight the potential for optimizing human–robot collaboration and offer practical implications for designing more efficient industrial work environments. [ABSTRACT FROM AUTHOR]

Occupational exoskeletons are not yet frequently used at work. Previous research has indicated multiple factors (physiological, implementation-related, work-related, policy, and psycho-social) that can explain this lack of adoption. However, there is a lack of specific requirements related to these themes to improve exoskeletons and their adoption. Therefore, the goal of our research is to formulate concrete requirements for the design and implementation of exoskeletons. We used a design ethnography approach (focus groups, an interview, observations, surveys, and group interviews) with multiple stakeholders (users, designers, and potential future users). Our data suggest that potential future users of exoskeletons believe that existing exoskeletons should be improved to be adopted. Exoskeletons should be more comfortable to wear and flexible enough to support a wider range of tasks. We have formulated 49 requirements for occupational exoskeletons, and we validated and extended an existing framework. Our work encourages the improved design of future or existing exoskeletons. [ABSTRACT FROM AUTHOR]

Soft robotics has gained increasing interest recently, but challenges persist, including operating at low temperatures, susceptibility to damage, fatigue-induced deterioration, and the need for proprioceptive sensing for autonomous operation and recovery. Addressing these challenges, this paper introduces a novel conductive ionoelastomer with a dendritic microstructure engineered to resist crack propagation and self-repair even at sub-zero temperature, offering opportunities to construct more adaptable soft robotic grippers which can work in complex environments. Silk ionoelastomers, serving as the matrix material, are complemented by dendritic sodium polyacrylate crystalline fibers. This integration significantly enhances strength and Young's modulus, while maintaining low hysteresis (below 24%), high fracture toughness (35.2 kJ m−2), and a fatigue threshold of 754 J m−2. Furthermore, it exhibits exceptional self-healing capabilities at both room temperature and −20 °C, enabling its use in flexible sensors with elongation capabilities of up to 500%. Utilizing folding techniques and inherent self-healing properties, this material can be tailored into pneumatic fingers with sensing and damage-detecting functionalities, facilitating the grasping of various objects. Even when exposed to various types of external damage, its pneumatic functionality is fully restored through the self-healing process at room temperature or low temperature, underscoring its resilience and adaptability in practical applications. [ABSTRACT FROM AUTHOR]

Despite enhancements in the development of robotic systems, the energy economy of today's robots lags far behind that of biological systems. This is in particular critical for untethered legged robot locomotion. To elucidate the current stage of energy efficiency in legged robotic systems, this paper provides an overview on recent advancements in development of such platforms. The covered different perspectives include actuation, leg structure, control and locomotion principles. We review various robotic actuators exploiting compliance in series and in parallel with the drive-train to permit energy recycling during locomotion. We discuss the importance of limb segmentation under efficiency aspects and with respect to design, dynamics analysis and control of legged robots. This paper also reviews a number of control approaches allowing for energy efficient locomotion of robots by exploiting the natural dynamics of the system, and by utilizing optimal control approaches targeting locomotion expenditure. To this end, a set of locomotion principles elaborating on models for energetics, dynamics, and of the systems is studied.

Brain-computer interfaces (BCI) enable users to control devices through their brain activity. Motor imagery (MI), the neural activity resulting from an individual imagining performing a movement, is a common control paradigm. This study introduces a user-centric evaluation protocol for assessing the performance and user experience of an MI-based BCI control system utilizing augmented reality. Augmented reality is employed to enhance user interaction by displaying environment-aware actions, and guiding users on the necessary imagined movements for specific device commands. One of the major gaps in existing research is the lack of comprehensive evaluation methodologies, particularly in real-world conditions. To address this gap, our protocol combines quantitative and qualitative assessments across three phases. In the initial phase, the BCI prototype's technical robustness is validated. Subsequently, the second phase involves a performance assessment of the control system. The third phase introduces a comparative analysis between the prototype and an alternative approach, incorporating detailed user experience evaluations through questionnaires and comparisons with non-BCI control methods. Participants engage in various tasks, such as object sorting, picking and placing, and playing a board game using the BCI control system. The evaluation procedure is designed for versatility, intending applicability beyond the specific use case presented. Its adaptability enables easy customization to meet the specific user requirements of the investigated BCI control application. This user-centric evaluation protocol offers a comprehensive framework for iterative improvements to the BCI prototype, ensuring technical validation, performance assessment, and user experience evaluation in a systematic and user-focused manner. [ABSTRACT FROM AUTHOR]

This study evaluates an innovative control approach to assistive robotics by integrating brain–computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times ( p < 0.001 ). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use ( p = 0.04 ). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements. [ABSTRACT FROM AUTHOR]