LAUSR

Autonomous exploration is a fundamental task for mobile robots. However, a major limitation is that in practical applications, robots usually face dynamic obstacles such as pedestrian crowds. These dynamic objects create significant challenges for both collision-free navigation and accurate localization/mapping, which can compromise the safety and exploration performance of the robot. In this work, a hierarchical approach is proposed for both effective exploration and collision-free navigation in crowded environments. The central idea of our approach is to combine local and global information to ensure the safety and efficiency of the exploration planner. Besides, our planning method utilizes a reinforcement learning (RL)-based obstacle avoidance algorithm that allows the robot to safely follow the exploration planner's path through the pedestrian crowd. The proposed system is thoroughly evaluated in simulation environments, and the results show that it outperforms existing methods in terms of not only the exploration efficiency but also the localization and mapping accuracy.