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The dual-user training system is essential for fostering motor skill learning, particularly in complex operations. However, the challenge lies in the optimal tradeoff between trainee ability and engagement level. To address this problem, we propose an intelligent agent that coordinates trainees’ control authority during real task engagement to ensure task safety during training. Our approach avoids the need for manually set control authority by expert supervision. At the same time, it does not rely on pre-modeling the trainee’s skill development. The intelligent agent uses a deep reinforcement learning (DRL) algorithm based on trainee performance to adjust adaptive engagement during the training process. Our investigation aims to provide reasonable engagement for trainees to improve their skills while ensuring task safety. Our results demonstrate that this system can seek the policy to maximize trainee participation while guaranteeing task safety.
The potential use of onboard vision sensors (e.g., cameras) has long been recognized for the Sense and Avoid (SAA) of unmanned aerial vehicles (UAVs), especially for micro UAVs with limited payload capacity. However, vision-based SAA for UAVs is extremely challenging because vision sensors usually have limitations on accurate distance information measuring. In this paper, we propose a monocular vision-based UAV SAA approach. Within the approach, the host UAV can accurately and efficiently avoid a noncooperative intruder only through angle measurements and perform maneuvers for optimal tradeoff among target motion estimation, intruder avoidance, and trajectory tracking. We realize this feature by explicitly integrating a target tracking filter into a nonlinear model predictive controller. The effectiveness of the proposed approach is verified through extensive simulations.
This paper presents a novel scheme for achieving attitude control of a tumbling combination system in the post-capture phase of a tethered space robot (TSR). Given the combination rotation characteristics, tether force is applied to provide greater control torques for stabilising the attitude. The proposed control scheme involves two attitude controllers, which coordinate the controller of the tether force and thruster force and the controller of single thruster force. The numerical simulations include a comparison between this coordinated control and the traditional thruster control and a sensitivity analysis on initial values of parameters. Simulation results validate the feasibility of the attitude control scheme for a tumbling combination system, and fuel consumption of the attitude control is efficiently reduced using the coordinated control strategies.
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