Concurrent-Learning-Based Adaptive Critic Formation for Multirobots Under Safety Constraints
Y. T. Cheng, Xingling Shao, Jian Li, Jun Liu, Qingzhen Zhang
Abstract
This article presents a concurrent learning-based adaptive critic formation for multirobots under safety constraints, which comprises of an initial formation consensus item and a collision-free adaptive critic policy. First, based on directed graph communication, an initial formation consensus item is designed to maintain the velocity agreement under a leader-follower setting. Particularly, a collision-free adaptive critic policy is developed that enables robots to preserve formation configuration with the minimum cost while excluding collisions caused by inter-robots and static/moving obstacles, wherein safety constraints encoded by an elegantly devised penalty function are enforced by converting constrained optimal control into unconstrained optimal control issue. Furthermore, by revisiting real-time and historical information, a concurrent weight learning rule is elaborated under a critic-only adaptive dynamic programming, improving the weight convergence without demanding the persistence excitation conditions. The remarkable benefits outperforming existing outcomes are safety-critical coordination with energy-saving performances is assured under a computationally efficient optimal learning paradigm. Involved errors are theoretically proved to be convergent. Finally, the values and superiorities are verified through extensive simulations on 2-D and 3-D multirobots.