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.

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Safety is of critical concern in human-robot collaboration. A human-in-the-loop experiment was conducted to assess the performance of the customized robot control scheme in a smart warehouse setting, where the human participants performed order picking and assembly tasks, and the mobile robot performed simulated pallet moving tasks. Three modes of human-robot interaction were tested (no robot involved, mobile robot with the empty payload and mobile robot with the full payload). Participants’ pupillary response, subjective workload as well as task performance (completion time and errors) were recorded and compared between different modes. Preliminary results indicated a slight decrease in human productivity (completion time increased from 191.4 to 204.1 seconds, p = 0.041) was well compensated by the relatively large gain from the robotics outputs. Additionally, human mental workload was negatively impacted (pupil diameter increased from 4.0 to 4.2 mm, p = 0.012) by introducing the robot into the shared workplace.

In order to navigate safely and effectively with humans in close proximity, robots must be capable of predicting the future motions of humans. This study first consolidates human studies in motion, intention, and preference into a discretized human model that can readily be used in robotics decision making algorithms. Cooperative Markov Decision Process (Co-MDP), a novel framework that improves upon Multiagent MDPs, is then proposed for enabling socially aware robot obstacle avoidance. Utilizing the consolidated and discretized human model, Co-MDP allows the system to (1) approximate rational human behavior and intention, (2) generate socially-aware robotic obstacle avoidance behavior, and (3) remain robust to the uncertainty of human intention and motion variance. Simulations of a human-robot co-populated environment verify Co-MDP as a feasible obstacle avoidance algorithm. In addition, the anthropomorphic behavior of Co-MDP was assessed and confirmed with a human-in-the-loop experiment. Results reveal that participants can not directly differentiate agents that were controlled by human operators from Co-MDP, and the reported confidences of their choices indicates that the predictions from participants were backed by behavioral evidence rather than random guesses. Thus the main contributions for this paper are: consolidating past human studies of rational human behavior and intention into a simple, discretized model; the development of Co-MDP: a robotic decision framework that can utilize this human model and maximize the joint utility between the human and robot; and an experimental design for evaluation of the human acceptance of obstacle avoidance algorithms.

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