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.

翻译：对于真实世界的导航而言，赋予机器人在包含动态与非凸静态障碍物的复杂环境中安全高效导航的能力至关重要。然而，在没有地图的情况下实现非凸复杂环境中的路径规划，并让多个机器人遵循社会规则实现避障，至今仍是具有挑战性的问题。本文提出一种具有社交意识的无地图机器人导航算法——安全强化学习与最优互惠避障（SRL-ORCA）。该算法是一种多智能体安全强化学习方法，通过将ORCA作为外部知识来提供安全保障。该算法进一步引入人类社会交通规范，通过遵循人类社交习惯提升社交舒适度并实现协同避障。实验结果表明，SRL-ORCA能够学习遵循特定交通规则的策略。相比深度强化学习方法（DRL），在应用相同训练网络的不同复杂场景中，SRL-ORCA在导航成功率上展现出显著提升。该算法能够应对非凸障碍物环境而不陷入局部极小区域，与ORCA相比路径质量（即到达目标的平均时间）提升了14.1%。相关视频可访问https://youtu.be/huhXfCDkGws。