A reliable traversability learning method based on human-demonstrated risk cost mapping for mobile robots over uneven terrain

The paper proposed a traversability learning method based on the human demonstration for generating risk cost maps. These maps aid mobile robots in identifying safe areas for reliable autonomous navigation over uneven terrain. Firstly, a maximum causal entropy-based inverse reinforcement learning method is employed to generate a reward function by considering human-demonstrated trajectories, robot poses, and feature vectors extracted from elevation data. This reward function is intended to accurately capture the behavioral preferences identified in human-demonstrated trajectories, specifically focusing on low-risk areas of the environment. Secondly, the reward function is combined with terrain feature data to generate a cost map and least-cost trajectory. Utilizing a wheeled mobile robot traversing uneven terrain, this paper verifies the adaptability enhancement of the proposed method for autonomous navigation over outdoor uneven terrain. The experimental results show an increase of 4%-10% in the success rate, a decrease of 13.6%-32.1% in the cumulative slope and gradient, and a decrease of 20.8%-27.4% in the Hausdorff distance of the robot's trajectories compared with traditional inverse reinforcement learning-based navigation methods.