  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.
