SEB-Naver：面向非平坦地形上类车机器人的基于SE(2)的局部导航框架
SEB-Naver: A SE(2)-based Local Navigation Framework for Car-like Robots on Uneven Terrain
AI总结：
本文提出SEB-Naver框架，通过GPU并行的SE(2)网格可通行性评估与基于微分平坦性的地形相关轨迹优化，解决类车机器人在非平坦地形上的局部导航难题。
摘要：
与平坦地形相比，类车机器人在非平坦地形上的自主导航面临独特挑战，尤其是在运动规划的可通行性评估和与地形相关的运动学建模方面。本文提出了SEB-Naver，一种新颖的基于SE(2)的局部导航框架，旨在克服这些挑战。首先，我们提出了一种针对SE(2)网格的高效可通行性评估方法，利用GPU并行计算实现局部地图的实时更新与维护。其次，受微分平坦性启发，我们提出了一种基于优化的轨迹规划方法，该方法集成了与地形相关的运动学模型，显著提升了规划效率和轨迹质量。最后，我们将这些组件统一到SEB-Naver中，实现了实时地形评估与轨迹优化。大量仿真和真实世界实验证明了本方法的有效性和高效性。代码位于https://github.com/ZJU-FAST-Lab/seb_naver。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
