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

• URL: http://arxiv.org/abs/2609.08512v1
• Date: Tue, 08 Sep 2026 09:58:32 GMT
• Title: TASG-Explore: Traversability-Aware Sector-Guided Exploration for Ground Robot on Uneven Terrain
• Abstract summary: TASG-Explore is a traversability-aware sector-guided exploration framework for ground robots.<n>It first performs hierarchical traversability analysis using variable-voxel ground fitting and adaptive 8-bit obstacle encoding.<n>A sector-guided planner selects region targets and inserts local viewpoints to generate efficient exploration routes.
• Score: 20.259150956128746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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