TASG-Explore: Traversability-Aware Sector-Guided Exploration for Ground Robot on Uneven Terrain

2026-09-08

AI Summary

本文提出TASG-Explore框架，通过层级可穿越性分析和基于扇区的规划方法，提高地面机器人在不平地形上的探索效率与安全性。

Abstract

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.

Problem

• autonomous exploration
• uneven terrain
• traversability analysis
• exploration efficiency
• coverage completeness

Innovation

• traversability-aware
• sector-guided exploration
• hierarchical traversability analysis
• variable-voxel ground fitting
• adaptive 8-bit obstacle encoding