发表机构
Shenyang Institute of Automation, Chinese Academy of Sciences; University of Chinese Academy of Sciences; Hohai University（中国科学院沈阳自动化研究所; 中国科学院大学; 河海大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TASG-Explore提出可通行性感知的扇区引导探索框架，通过分层地形分析和动态拓扑规划，在崎岖地形上实现高效安全探索，效率提升51%，覆盖率最高提升2.95倍。
AI 中文摘要
在不平坦地形上的自主探索要求地面机器人在探索效率、覆盖完整性和地形安全性之间取得平衡。详细的地形推理可提高局部可靠性，但可能减慢大规模探索的速度，而粗略的区域引导在开阔区域扩展迅速，但可能遗漏狭窄通道和不规则的可通行边界。为应对这一挑战，本文提出了TASG-Explore，一种面向地面机器人的可通行性感知扇区引导探索框架。该框架首先使用可变体素地面拟合和自适应8位障碍物编码执行分层可通行性分析。随后将代价地图分割为扇区，增量更新扇区聚类，提取地形耦合的前沿视点，并维护具有未知拓扑假设的动态拓扑路线图。最后，扇区引导规划器选择区域目标并插入局部视点以生成高效的探索路线。在包括洞穴、森林和崎岖丘陵在内的多种挑战性环境中的基准实验表明，TASG-Explore在六个代表性最先进规划器中取得了最佳整体性能。所提出的可通行性分析将处理效率提高了6.3倍，同时保持高精度，探索规划器将探索效率提高了51%，并在崎岖丘陵场景中将覆盖率提高了最多2.95倍。大规模真实世界实验进一步证明了所提出方法的实用价值。
英文摘要
Autonomous exploration on uneven terrain requires ground robots to balance exploration efficiency, coverage completeness, and terrain safety. Detailed tsrrain reasoning improves local reliability but can slow large-scale exploration, whereas coarse region guidance expands quickly in open areas but can miss narrow passages and irregular traversable boundaries. To address this challenge, this paper presents TASG-Explore, a traversability-aware sector-guided exploration framework for ground robots. The framework first performs hierarchical traversability analysis using variable-voxel ground fitting and adaptive 8-bit obstacle encoding. It then splitting cost map into sectors, incrementally updates sector clusters, extracts terrain-coupled frontier viewpoints, and maintains a dynamic topological roadmap with unknown topological hypotheses. Finally, a sector-guided planner selects region targets and inserts local viewpoints to generate efficient exploration routes. Benchmark experiments in diverse challenging environments, including caves, forests, and rugged hills, show that TASG-Explore achieves the best overall performance among six representative state-of-the-art planners. The proposed traversability analysis improves processing efficiency by 6.3 times while maintaining high accuracy, and the exploration planner improves exploration efficiency by 51% and increases coverage by up to 2.95 times in rugged hill scene. Large-scale real-world experiments further demonstrate the practical value of the proposed method.
Comments20 pages, 17 figures
