用骨架图规划四足机器人自动扫描未知环境路径。
Autonomous Navigation of Quadrupeds Using Coverage Path Planning with Morphological Skeleton Map
- 基于SLAM生成的2D地图骨架点生成兴趣点序列。
- 五次实验中86.5%的路径点可达，处理速度达1.7ms/pixel。
- 适合复杂非凸室内环境的自主导航与扫描任务。
本文提出一种新型覆盖路径规划方法，用于在未结构化环境中自主扫描。该方法利用SLAM构建的2D导航地图的形态学骨架，生成一系列兴趣点（POIs），并根据机器人当前位置对这些点排序，生成最优路径。通过有限状态机控制高层行为，在导航至兴趣点（使用Nav2）和局部扫描模式间切换。在平坦、无障碍、非凸的室内环境中进行了五次测试，验证了时间效率与可达性。地图读取器与路径规划器对尺寸为[196,225]像素宽、[185,231]像素高的地图的处理速度分别为2.52毫秒/像素和1.7毫秒/像素，计算时间随像素增长分别增加22.0纳秒/像素和8.17微秒/像素。五次运行中，机器人成功到达86.5%的路径点。该方法存在2D导航地图漂移问题。
原文摘要 · Abstract (English)
This paper proposes a novel method of coverage path planning for the purpose of scanning an unstructured environment autonomously. The method uses the morphological skeleton of the prior 2D navigation map via SLAM to generate a sequence of points of interest (POIs). This sequence is then ordered to create an optimal path given the robot's current position. To control the high-level operation, a finite state machine is used to switch between two modes: navigating towards a POI using Nav2, and scanning the local surrounding. We validate the method in a leveled indoor obstacle-free non-convex environment on time efficiency and reachability over five trials. The map reader and the path planner can quickly process maps of width and height ranging between [196,225] pixels and [185,231] pixels in 2.52 ms/pixel and 1.7 ms/pixel, respectively, where their computation time increases with 22.0 ns/pixel and 8.17 $μ$s/pixel, respectively. The robot managed to reach 86.5% of all waypoints over all five runs. The proposed method suffers from drift occurring in the 2D navigation map.
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