A Behavior-Based Policy for Multirobot Formation Control

Abstract

This paper presents an advanced method with hierarchical architecture for multirobot formation control. The control system consists of fundamental behavior module, supervisor module and velocity tuning module. Formation and obstacle avoidance behaviors are produced by fundamental behavior module, among which the obstacle avoidance behavior is produced by fuzzy logic technique. Then a FNN (Fuzzy Neural Network) is designed to fuse the two fundamental behaviors in supervisor module. The FNN is trained through reinforcement learning. At last, a velocity tuning module is designed to adjust the speed of the robot. Simulation results validate the feasibility of this method．