Ground robot explores rough terrain faster and safer with new planning method
TASG-Explore: Traversability-Aware Sector-Guided Exploration for Ground Robot on Uneven Terrain
Robotics
Summary
Exploring uneven and tricky landscapes is hard for ground robots because they need to balance being fast, thorough, and safe. The authors created a system called TASG-Explore that helps robots understand the terrain at different levels and chooses the best places to explore next. This system organizes the area into sectors, updates what it knows as it goes, and plans routes that avoid risky spots while covering more ground. Tests showed their method works better and faster than several existing approaches, especially in tough environments like caves and hills. This approach could help robots explore real-world places more effectively.
autonomous explorationground robotuneven terraintraversability analysissector-guided planningfrontier viewpointstopological roadmapcost mapcoverage efficiencyrobot navigation
Authors
Shaocong Wang, Shiliang Shao, Ting Wang, Guangjie Han, Lianqing Liu
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.