LRAE: Large-Region-Aware Safe and Fast Autonomous Exploration of Ground Robots for Uneven Terrains
Qingchen Bi, Xuebo Zhang, Shiyong Zhang, Runhua Wang, Lun Li, Jing Yuan
Abstract
Ground robot autonomous exploration for uneven terrains is still challenging since the rugged terrain structures not only degrade the exploration performance but also threaten the navigation safety of the robot. In this letter, a novel exploration planner is proposed for safe and fast exploration in uneven terrains. To obtain high exploration efficiency, we propose a large-region-aware exploration route optimization strategy that prioritizes exploring large regions while also considering exploring nearby small regions. To safely and completely explore uneven terrains, our planner fully introduces traversability information to extract unknown regions and assess exploration safety levels. The safety levels are then integrated into the design of the exploration strategy to ensure safe robotic exploration. We validate our method in various challenging simulation scenes and real-world wild uneven terrains. The results show that our method can safely explore uneven terrains and improve exploration efficiency by up to 45.3% compared with state-of-the-art methods.