论文标题
基于大术的多机器人任务和运动计划
Hypergraph-based Multi-Robot Task and Motion Planning
论文作者
论文摘要
我们提出了一种多机器人任务和运动计划方法，该方法将操纵器应用于对象的重排时，可以使解决方案时间比现有方法快三个数量级，并成功地计划了最多二十个对象的问题，超过三倍，超过三倍以上的对象。我们通过分解计划空间来考虑单独的操纵器，对象和持有物体的操纵器来实现这一改进。我们用超图表示这种分解，其中顶点是计划空间的分解元素，而hyperarcs是元素之间的过渡。现有方法使用基于图的表示，其中顶点是完整的复合空间，而边缘是它们之间的过渡。使用超图可降低计划空间的表示大小，包括多操作器对象重排，超图顶点的数量与机器人或对象的数量线性缩放，而HyperArcs的数量则与机器人数量二次缩放，并与对象的数量进行线性缩放。相反，基于图的表示的顶点和边缘的数量在机器人和对象的数量中成倍缩放。我们表明，对于其他多机器人任务和运动计划问题，可以实现类似的收益。
We present a multi-robot task and motion planning method that, when applied to the rearrangement of objects by manipulators, results in solution times up to three orders of magnitude faster than existing methods and successfully plans for problems with up to twenty objects, more than three times as many objects as comparable methods. We achieve this improvement by decomposing the planning space to consider manipulators alone, objects, and manipulators holding objects. We represent this decomposition with a hypergraph where vertices are decomposed elements of the planning spaces and hyperarcs are transitions between elements. Existing methods use graph-based representations where vertices are full composite spaces and edges are transitions between these. Using the hypergraph reduces the representation size of the planning space-for multi-manipulator object rearrangement, the number of hypergraph vertices scales linearly with the number of either robots or objects, while the number of hyperarcs scales quadratically with the number of robots and linearly with the number of objects. In contrast, the number of vertices and edges in graph-based representations scales exponentially in the number of robots and objects. We show that similar gains can be achieved for other multi-robot task and motion planning problems.
