Quotient-Space Motion Planning
Quotient-Space Motion Planning
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DOI：
10.1109/iros.2018.8593554
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
A. Orthey;Adrien Escande;E. Yoshida
中科院分区：
文献类型：
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作者：
A. Orthey;Adrien Escande;E. Yoshida
运动规划算法通过计算穿过机器人的配置空间的路径来计算机器人的运动。为了提高运动规划算法的运行时间，我们建议嵌套机器人在彼此之间，创建一个嵌套的配置空间分解的约束空间。基于这种分解，我们定义了一种新的基于路线图的运动规划算法，称为商空间路线图规划器（QMP）。该算法开始增长的最低维商空间上的图形，切换到下一个商空间，一旦一个有效的路径已被发现，并不断更新的图形在每个商空间同时，直到一个有效的路径在配置空间中已经found.We表明，该算法是概率完全的，并优于一组国家的最先进的算法在开放式运动规划库（OMPL）中实现。
A motion planning algorithm computes the motion of a robot by computing a path through its configuration space. To improve the runtime of motion planning algorithms, we propose to nest robots in each other, creating a nested quotient-space decomposition of the configuration space. Based on this decomposition we define a new roadmap-based motion planning algorithm called the Quotient-space roadMap Planner (QMP). The algorithm starts growing a graph on the lowest dimensional quotient space, switches to the next quotient space once a valid path has been found, and keeps updating the graphs on each quotient space simultaneously until a valid path in the configuration space has been found. We show that this algorithm is probabilistically complete and outperforms a set of state-of-the-art algorithms implemented in the open motion planning library (OMPL).