SRL-ORCA: A Socially Aware Multi-Agent Mapless Navigation Algorithm In Complex Dynamic Scenes

TL;DR

SRL-ORCA is a socially aware, mapless multi-agent navigation algorithm that combines reinforcement learning with ORCA to enable safe, efficient, and socially compliant navigation in complex dynamic environments.

Contribution

It introduces a novel multi-agent safe reinforcement learning algorithm that incorporates social norms and ORCA for improved navigation in complex, non-convex environments without maps.

Findings

Significant improvement in navigation success rate over DRL.

14.1% better path quality compared to ORCA.

Effective obeyance of traffic rules in complex scenarios.

Abstract

For real-world navigation, it is important to endow robots with the capabilities to navigate safely and efficiently in a complex environment with both dynamic and non-convex static obstacles. However, achieving path-finding in non-convex complex environments without maps as well as enabling multiple robots to follow social rules for obstacle avoidance remains challenging problems. In this letter, we propose a socially aware robot mapless navigation algorithm, namely Safe Reinforcement Learning-Optimal Reciprocal Collision Avoidance (SRL-ORCA). This is a multi-agent safe reinforcement learning algorithm by using ORCA as an external knowledge to provide a safety guarantee. This algorithm further introduces traffic norms of human society to improve social comfort and achieve cooperative avoidance by following human social customs. The result of experiments shows that SRL-ORCA learns strategies to obey specific traffic rules. Compared to DRL, SRL-ORCA shows a significant improvement in navigation success rate in different complex scenarios mixed with the application of the same training network. SRL-ORCA is able to cope with non-convex obstacle environments without falling into local minimal regions and has a 14.1\% improvement in path quality (i.e., the average time to target) compared to ORCA. Videos are available at https://youtu.be/huhXfCDkGws.