A Two-Stage Reinforcement Learning Approach for Robot Navigation in Long-range Indoor Dense Crowd Environments

Safe and efficient mobility is vital for mobile robots navigating long-range indoor crowd environments, such as supermarkets, restaurants, and railway stations. Traditional path planning methods are challenged because of the high dynamics of pedestrians and constrained feasible regions. Existing long-range deep reinforcement learning (DRL) path planning methods often exhibit low success rates and driving speeds in long-range navigation tasks under crowded conditions. To overcome these issues, we propose a new two-stage DRL method, known as TSDRL, where the long-range navigation task is divided into subgoal generation (SG) and planning refinement (PR) stages. In the SG stage, the agent is trained to learn a decision-making policy to generate subgoals at each decision time to avoid dense crowds. In the PR stage, the agent learns a safer and more efficient planning policy based on each subgoal generated in the SG stage to improve the robot’s movement safety and speed. Simulated experiments show that our method outperforms traditional and long-range DRL path planning methods in terms of safety, efficiency, generalization, and robustness. Furthermore, we evaluate our approach using the Turtlebot2 platform in a real-world setting, demonstrating that the robot can navigate safely and efficiently while avoiding dense crowds.