Decentralized Multi-robot Navigation Policy with Enhanced Security Using Graph GRU Policy Network

Abstract

Formulating a multi-robot obstacle avoidance policy is essential for enabling safe and efficient navigation in multi-robot environments, forming a critical component of the effective operation of multi-robot systems. Recently, reinforcement learning has been applied to improve the performance of decentralized, policy-driven robots in task execution. However, ensuring the safety of these agents during movement remains a significant challenge due to the inherent risks associated with the reinforcement learning process, such as frequent collisions. To address this issue and enhance the safety of policy-guided multi-robot navigation, we propose a novel policy based on imitation learning. This framework introduces a novel policy neural network that integrates a graph attention mechanism with the GRU network structure. The key innovation lies in utilizing the interactions between neighboring robots to enhance the safety of their movements. In a multi-robot simulation environment, robot behaviors are directed by the proposed policy. A comparative analysis was conducted between our approach and RL-RVO, one of the advanced methods in the field. The results demonstrate that our approach outperforms RL-RVO, achieving a higher success rate and significantly improving safety performance.