Hierarchical Reinforcement Learning for Safe Mapless Navigation with Congestion Estimation

• URL: http://arxiv.org/abs/2503.12036v1
• Date: Sat, 15 Mar 2025 08:03:50 GMT
• Title: Hierarchical Reinforcement Learning for Safe Mapless Navigation with Congestion Estimation
• Authors: Jianqi Gao, Xizheng Pang, Qi Liu, Yanjie Li,
• Abstract summary: This paper introduces a safe mapless navigation framework utilizing hierarchical reinforcement learning (HRL) to enhance navigation through such areas.<n>The findings demonstrate that our HRL-based navigation framework excels in both static and dynamic scenarios.<n>We implement the HRL-based navigation framework on a TurtleBot3 robot for physical validation experiments.
• Score: 7.339743259039457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning-based mapless navigation holds significant potential. However, it faces challenges in indoor environments with local minima area. This paper introduces a safe mapless navigation framework utilizing hierarchical reinforcement learning (HRL) to enhance navigation through such areas. The high-level policy creates a sub-goal to direct the navigation process. Notably, we have developed a sub-goal update mechanism that considers environment congestion, efficiently avoiding the entrapment of the robot in local minimum areas. The low-level motion planning policy, trained through safe reinforcement learning, outputs real-time control instructions based on acquired sub-goal. Specifically, to enhance the robot's environmental perception, we introduce a new obstacle encoding method that evaluates the impact of obstacles on the robot's motion planning. To validate the performance of our HRL-based navigation framework, we conduct simulations in office, home, and restaurant environments. The findings demonstrate that our HRL-based navigation framework excels in both static and dynamic scenarios. Finally, we implement the HRL-based navigation framework on a TurtleBot3 robot for physical validation experiments, which exhibits its strong generalization capabilities.

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