A Differential Game Approach to Collision Avoidance in Multi-Agent Systems

This paper gives a novel differential game scheme to solve the collision avoidance problem for multi-agent systems. Based on the concept of artificial potential field (APF), we combine obstacle avoidance objectives with trajectory optimization targets as the performance index. The feedback strategies are based on the solutions of coupled Riccati equations. Furthermore, it is proved that the feedback strategies will converge to a Nash equilibrium (NE). Finally, simulation results are provided to show the advantages, which make agents arrive at the targeted position collision-free with a reduced time.

1 Introduction

With the development of artificial intelligence technology and computer technology, the coordinated control of multi-agent systems (MASs) has attracted extensive attention [1]. When facing the dynamic and complex working environment and task, a agent needs the coordination among multi-agent to complete the overall task requirements [2]. Sometimes, the task execution of MASs require agents to move continuously. Thus, it is necessary to plan a collision-free trajectory [3].