Risk Perception-Based Safe Reinforcement Learning Navigation for Mobile Robots in Multi-Dynamic Obstacles Environments

In this paper, a novel deep reinforcement learning based navigation policy for mobile robot is proposed to address the challenges in multi-dynamic obstacles environments. Firstly, this method designs a risk perception function to evaluate the collision probability (CP) between robot and dynamic obstacles. Then, the observation space with risk perception is designed for the robot with a sense of the danger level between dynamic obstacles. In order to guide the robot to actively avoid such high-risk obstacles, the velocity obstacles(VO)-based reward is used to find desired direction angle only considering these critical obstacles. In addition, a novel safety constraint is formulated based on control barrier function (CBF) theory, it could adaptively adjust the safety distance according to the risk level of dynamic obstacles. The policy is then optimized within a CBF-guided training framework to enhance safety and adaptability in multi-dynamic obstacles environments. A series of simulation and real-world experiments demonstrate that the proposed policy achieves superior performance in both safety and efficiency compared with state-of-the-art methods, and it is well-suited for safety-critical navigation tasks in dense, dynamic environments such as indoor service and warehouse logistics scenarios.