This paper introduces a new method for robot motion planning and navigation in uneven environments through a surfel representation of underlying point clouds. The proposed method addresses the shortcomings of state-of-the-art navigation methods by incorporating both kinematic and physical constraints of a robot with standard motion planning algorithms (e.g., those from the Open Motion Planning Library), thus enabling efficient sampling-based planners for challenging uneven terrain navigation on raw point cloud maps. Unlike techniques based on Digital Elevation Maps (DEMs), our novel surfel-based state-space formulation and implementation are based on raw point cloud maps, allowing for the modeling of overlapping surfaces such as bridges, piers, and tunnels. Experimental results demonstrate the robustness of the proposed method for robot navigation in real and simulated unstructured environments. The proposed approach also optimizes planners' performances by boosting their success rates up to 5x for challenging unstructured terrain planning and navigation, thanks to our surfel-based approach's robot constraint-aware sampling strategy. Finally, we provide an open-source implementation of the proposed method to benefit the robotics community.
翻译:本文介绍了在不均匀的环境中进行机器人运动规划和导航的新方法,方法是通过表面显示深点云层,处理最新导航方法的缺点,将一个具有标准动作规划算法的机器人(例如开放运动规划图书馆的机器人)的动态和物理限制纳入其中,从而使高效的取样规划者能够在原始点云图上进行挑战性不均匀的地形导航。与基于数字升幅地图的技术不同,我们基于冲浪的新颖的州空间设计和实施以原始点云图为基础,从而可以模拟桥梁、码头和隧道等重叠的表面。实验结果显示,拟议机器人在真实和模拟无结构环境中导航的方法非常健全。拟议的方法还优化了规划者的业绩,将他们的成功率提高到5x,以挑战无结构的地形规划和导航,这归功于我们基于冲浪的方法的机器人约束度测测取样战略。最后,我们提供了拟议的方法的开源实施,使机器人界受益。