This paper studies the tracking control problem for an uncertain ${n}$ -link robot with full-state constraints. The rigid robotic manipulator is described as a multiinput and multioutput system. Adaptive neural network (NN) control for the robotic system with full-state constraints is designed. In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances. The Moore-Penrose inverse term is employed in order to prevent the violation of the full-state constraints. A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. Simulation studies are performed to illustrate the effectiveness of the proposed control.

Disassembly is an essential step for remanufacturing end-of-life (EOL) products. Optimization of disassembly sequences and the utilization of robotic technology could alleviate the labor-intensive nature of dismantling operations. This study proposes an optimization framework for disassembly sequence planning under uncertainty considering human–robot collaboration. The proposed framework combines three attributes: disassembly cost, safety, and complexity of disassembly, namely disassembleability, to identify the optimal disassembly path and allocate operations between human and robot. A multi-attribute utility function is used to address uncertainty and make a tradeoff among multiple attributes. The disassembly time reflects the cost of disassembly which is assumed to be an uncertain parameter with a Beta distribution; the disassembleability evaluates the feasibility of conducting operations by robot; finally, the safety index ensures the protection of human workers in the work environment. An example of dismantling a desktop computer is used to show the application. The model identifies the optimal disassembly sequence with less disassembly cost, high disassembleability, and increased safety index while allocating disassembly operations among human and robot. A sensitivity analysis is conducted to show the model's performance when changing the disassembly cost for the robot.

As technology advances, Human-Robot Interaction (HRI) is boosting overall system efficiency and productivity. However, allowing robots to be present closely with humans will inevitably put higher demands on precise human motion tracking and prediction. Datasets that contain both humans and robots operating in the shared space are receiving growing attention as they may facilitate a variety of robotics and human-systems research. Datasets that track HRI with rich information other than video images during daily activities are rarely seen. In this paper, we introduce a novel dataset that focuses on social navigation between humans and robots in a future-oriented Wholesale and Retail Trade (WRT) environment (https://uf-retail-cobot-dataset.github.io/). Eight participants performed the tasks that are commonly undertaken by consumers and retail workers. More than 260 minutes of data were collected, including robot and human trajectories, human full-body motion capture, eye gaze directions, and other contextual information. Comprehensive descriptions of each category of data stream, as well as potential use cases are included. Furthermore, analysis with multiple data sources and future directions are discussed.

A rapid rise in the recycling and remanufacturing of end-of-use electronic waste (e-waste) has been observed due to multiple factors including our increased dependence on electronic products and the lack of resources to meet the demand. E-waste disassembly, which is the operation of extracting valuable components for recycling purposes, has received ever increasing attention as it can serve both the economy and the environment. Traditionally, e-waste disassembly is labor intensive with significant occupational hazards. To reduce labor costs and enhance working efficiency, collaborative robots (cobots) might be a viable option and the feasibility of deploying cobots in high-risk or low value-added e-waste disassembly operations is of tremendous significance to be investigated. Therefore, the major objective of this study was to evaluate the effects of working with a cobot during e-waste disassembly processes on human workload and ergonomics through a human subject experiment. Statistical results revealed that using a cobot to assist participants with the desktop disassembly task reduced the sum of the NASA-TLX scores significantly compared to disassembling by themselves (p = 0.001). With regard to ergonomics, a significant reduction was observed in participants’ mean L5/S1 flexion angle as well as mean shoulder flexion angle on both sides when working with the cobot (p < 0.001). However, participants took a significantly longer time to accomplish the disassembly task when working with the cobot (p < 0.001), indicating a trade-off of deploying cobot in the e-waste disassembly process. Results from this study could advance the knowledge of how human workers would behave and react during human-robot collaborative e-waste disassembly tasks and shed light on the design of better HRC for this specific context.

Ensuring human safety has been one of the most critical considerations within the field of human–robot interaction. To explore the effects of working with autonomous robots on human coworkers’ perceived workload and job performance, two experiments were conducted in this study. Eight participants were recruited in the first experiment. Results revealed an increase in both subjective and objective workload measurements: compared to the baseline “no robot” to “empty payload” condition, the sum of NASA Task Load Index (NASA-TLX) scores increased from 129.3 (58.8) to 147.6 (53.7) ( $p$ -value = 0.041) and the pupil diameter increased from 4.07 (0.64) mm to 4.11 (0.67) mm; while working with a full payload robot, the sum of NASA-TLX scores increased to 151.7 (55.9) ( $p$ -value = 0.010) and the pupil diameter increased to 4.12 (0.66) mm. Increased task completion time (3.1% for empty payload condition and 6.6% for full payload condition) showed a decrease in human productivity. However, this slight human output reduction was compensated by the substantial gain from the robot. Similar effects of autonomous mobile robots on participants’ NASA-TLX scores and task completion time were observed in the second experiment, where eight participants performed the order picking and sorting tasks in a high-fidelity grocery store setting. Results from the study suggested the feasibility of applying fully autonomous mobile robots in Wholesale and Retail Trade settings to improve human–robot team productivity while prioritizing physical safety and reasonable increases in mental workload.