Learning-Based Multi-Robot Formation Control With Obstacle Avoidance

Abstract

Multi-robot formation control has been intensively studied in recent years. In practical applications, the multi-robot system's ability to independently change the formation to avoid collision among the robots or with obstacles is critical. In this study, a multi-robot adaptive formation control framework based on deep reinforcement learning is proposed. The framework consists of two layers, namely the execution layer and the decision-making layer. The execution layer enables the robot to approach its target position and avoid collision with other robots and obstacles through a deep network trained by a reinforcement learning method. The decision-making layer organizes all robots into a formation through a new leader-follower configuration and provides target positions to the leader and followers. The leader's target position is kept unchanged, while the follower's target position is changed according to the situation it encounters. In addition, to operate more effectively in environments with different levels of complexity, a hybrid switching control strategy is proposed. The simulation results demonstrate that our proposed formation control framework enables the robots to adjust formation independently to pass through obstacle areas and can be generalized to different scenarios with unknown obstacles and varying number of robots.