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For real-world navigation, it is important to endow robots with thecapabilities to navigate safely and efficiently in a complex environment withboth dynamic and non-convex static obstacles. However, achieving path-findingin non-convex complex environments without maps as well as enabling multiplerobots to follow social rules for obstacle avoidance remains challengingproblems. In this letter, we propose a socially aware robot mapless navigationalgorithm, namely Safe Reinforcement Learning-Optimal Reciprocal CollisionAvoidance (SRL-ORCA). This is a multi-agent safe reinforcement learningalgorithm by using ORCA as an external knowledge to provide a safety guarantee.This algorithm further introduces traffic norms of human society to improvesocial comfort and achieve cooperative avoidance by following human socialcustoms. The result of experiments shows that SRL-ORCA learns strategies toobey specific traffic rules. Compared to DRL, SRL-ORCA shows a significantimprovement in navigation success rate in different complex scenarios mixedwith the application of the same training network. SRL-ORCA is able to copewith non-convex obstacle environments without falling into local minimalregions and has a 14.1\% improvement in path quality (i.e., the average time totarget) compared to ORCA. Videos are available at https://youtu.be/huhXfCDkGws.

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