Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning

TL;DR

This paper introduces a distributed multi-agent reinforcement learning approach for formation control and obstacle avoidance, enabling agents to operate with local information and adaptively reorganize, outperforming existing methods in simulations and real-world tests.

Contribution

It presents a novel distributed MARL method that uses only local information, allowing agents to quickly reorganize and improve formation and obstacle avoidance performance.

Findings

Better formation error and convergence rate

Comparable obstacle avoidance success rate

Effective in both simulation and hardware tests

Abstract

Multi-agent formation as well as obstacle avoidance is one of the most actively studied topics in the field of multi-agent systems. Although some classic controllers like model predictive control (MPC) and fuzzy control achieve a certain measure of success, most of them require precise global information which is not accessible in harsh environments. On the other hand, some reinforcement learning (RL) based approaches adopt the leader-follower structure to organize different agents' behaviors, which sacrifices the collaboration between agents thus suffering from bottlenecks in maneuverability and robustness. In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL). Agents in our system only utilize local and relative information to make decisions and control themselves distributively. Agent in the multi-agent system will reorganize themselves into a new topology quickly in case that any of them is disconnected. Our method achieves better performance regarding formation error, formation convergence rate and on-par success rate of obstacle avoidance compared with baselines (both classic control methods and another RL-based method). The feasibility of our method is verified by both simulation and hardware implementation with Ackermann-steering vehicles.