A Novel SMMS Teleoperation Control Framework for Multiple Mobile Agents With Obstacles Avoidance by Leader Selection
提出一种单主多从遥操作控制框架，通过领导者选择和虚拟力反馈解决人工势场法的局部极小问题，使多移动智能体在时延、非完整约束等条件下实现避障和编队控制。
Teleoperation of multiple agents has the unique advantage to complete tasks with wide range and is an effective solution to help agents avoid obstacles with human intelligence, especially when encounters the local-minima problem. In this article, a novel nonlinear single-master–multislave (SMMS) teleoperation control framework is proposed for multiple mobile agents to achieve obstacles avoidance under delays, nonlinearities, various uncertainties, and nonholonomic constraints. Namely, the slave trajectory planner is designed to cope with the nonholonomic constraints caused by underactuated characteristics of slave agents, while the slave obstacle avoidance planner is designed to cope with obstacles in the environment, which can avoid the obstacles by artificial potential function (APF)-based obstacle avoidance algorithm. Particularly, considering that the APF usually encounters the local-minima problem, a leader selection algorithm is designed for the slave obstacle avoidance planner and a virtual force feedback is designed for the master subsystem, where the slave agents can get rid of local-minima points while teleoperated by human operator with confident force feedback. The global stability of the overall system can be guaranteed under the proposed radial basis function neural network (RBFNN)-based adaptive sliding mode master controller and slave formation controller under delays, nonlinearities and various uncertainties. The comparative experiment is implemented, and the results show the effectiveness of proposed control framework in the achievement of good performance including position tracking, force feedback, and formation and obstacles avoidance while the stability is guaranteed.