SEB-Naver: 不整地における車両型ロボットのためのSE(2)ベース局所ナビゲーションフレームワーク
SEB-Naver: A SE(2)-based Local Navigation Framework for Car-like Robots on Uneven Terrain
不整地を走行する車両型ロボット向けに、GPU並列計算による走行可能性評価と地形を考慮した軌道最適化を統合したSE(2)ベースの局所ナビゲーション手法を提案し、実機実験で有効性を示した。
著者: Xiaoying Li, Long Xu, Xiaolin Huang, Donglai Xue, Zhihao Zhang, Zhichao Han, Chao Xu, Yanjun Cao, Fei Gao
分類: cs.RO
原文アブストラクト
Autonomous navigation of car-like robots on uneven terrain poses unique challenges compared to flat terrain, particularly in traversability assessment and terrain-associated kinematic modelling for motion planning. This paper introduces SEB-Naver, a novel SE(2)-based local navigation framework designed to overcome these challenges. First, we propose an efficient traversability assessment method for SE(2) grids, leveraging GPU parallel computing to enable real-time updates and maintenance of local maps. Second, inspired by differential flatness, we present an optimization-based trajectory planning method that integrates terrain-associated kinematic models, significantly improving both planning efficiency and trajectory quality. Finally, we unify these components into SEB-Naver, achieving real-time terrain assessment and trajectory optimization. Extensive simulations and real-world experiments demonstrate the effectiveness and efficiency of our approach. The code is at https://github.com/ZJU-FAST-Lab/seb_naver.
