“大区域优先+小区域路由+3D地形通行性导向”，让高效安全自主探索崎岖地形成为可能！

Github开源代码链接：https://github.com/NKU-MobFly-Robotics/LRAE

论文链接：https://ieeexplore.ieee.org/document/10734213

B站视频链接：https://www.bilibili.com/video/BV1g1SVYWEfw/?vd_source=16f56ea8aec66438bd5fb0302a43ae0c

现有的地面机器人探索方法面临一系列挑战：(1) 仅考虑局部“边界点”，未评估全局“区域块”, 优先级排序不合理；(2) 单次决策仅考虑单一目标、未兼顾多目标路由，往复折返多，探索效率低；(3) 仅适用于平坦地形，难以应对复杂崎岖地形。

本文提出“大区域优先、小区域路由”的未知崎岖地形机器人自主探索策略，在优先探索大面积未知区域的同时，不忘兼顾附近小面积未知区域，实现了抓大放小、往复折返少、覆盖效率高的自主探索；此外，策略设计中率先融入了3D地形通行性分析，确保了崎岖地形探索路径安全可行。平坦地形下探索效率比SOTA方法提升30%以上，且率先扩展到3D崎岖地形。

Q. Bi, X. Zhang*, S. Zhang, R. Wang, L. Li and J. Yuan. LRAE: Large-Region-Aware Safe and Fast Autonomous Exploration of Ground Robots for Uneven Terrains. IEEE Robotics and Automation Letters, doi: 10.1109/LRA.2024.3486229.

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.