Concurrent Learning-Based Adaptive Critic Formation for Multi-robots under Safety Constraints

This article presents a concurrent learning-based adaptive critic formation for multi-robots under safety constraints, which comprises of an initial formation consensus item and a collision-free adaptive critic policy. Firstly, 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 poli-cy is developed that enables robots to preserve formation config-uration with the minimum cost while excluding collisions caused by inter-robots and static/moving obstacles, wherein safety con-straints encoded by an elegantly devised penalty function are enforced by converting constrained optimal control into uncon-strained optimal control issue. Furthermore, by revisiting real-time and historical information, a concurrent weight learning rule is elaborated under a critic-only adaptive dynamic pro-gramming, improving the weight convergence without demand-ing the persistence excitation conditions. The remarkable benefits outperforming existing outcomes are safety-critical coordination with energy-saving performances is assured under a computa-tionally efficient optimal learning paradigm. Involved errors are theoretically proved to be convergent. Finally, the values and superiorities are verified through extensive simulations on two-dimensional and three-dimensional multi-robots.