An Environmental-Complexity-Based Navigation Method Based on Hierarchical Deep Reinforcement Learning

Abstract

Navigation methods based on deep reinforcement learning (RL) have recently exhibited superior performance, particularly for navigation in dynamic environments. However, most existing methods solely rely on deep neural network feature encoders to extract features from raw LiDAR data, lacking an explicit representation of environmental structure. This limitation hinders effective environmental representation and interpretability, constraining navigation performance improvement. To solve this problem, we propose two quantitative metrics based on laser scans, which explicitly represent environmental complexity and show great interpretability. Furthermore, we propose an environmental-complexity-based navigation method based on hierarchical deep RL with the proposed metrics. Experimental results show that the proposed method achieves better navigation performance than baselines, especially in challenging scenarios with corners and dynamic obstacles.