Socially Aware Hybrid Robot Navigation via Deep Reinforcement Learning
Zhen Feng, Ming Gao, Bingxin Xue, Chaoqun Wang, Fengyu Zhou · 2023
Service robots working in public environments require the capacity to navigate among humans and other obstacles safely and socially compliantly. This paper presents a hybrid navigation approach combining rule-based trajectory generators into deep reinforcement learning for motion planning in populated and cluttered environments. An intention-based action space is proposed in the reinforcement learning framework to achieve a tight coupling of the two methods. By fusing static information and dynamic objects, our network can learn motion patterns adapted to real-world scenarios. The rule-based trajectory generator guarantees the safety and dynamic feasibility of the motion primitives. The robot is trained to understand real-time human-robot interactions through deep reinforcement learning. Experiment results demonstrate that our policy can efficiently perceive human interactions and navigate the robot safely in crowded environments with static obstacles.