论文标题
使用路径碎片打碎片在货运状态空间中解决重排拼图
Solving Rearrangement Puzzles using Path Defragmentation in Factored State Spaces
论文作者
论文摘要
重排难题是重排问题的变化，其中问题的元素在逻辑上可能会链接在一起。为了有效地解决此类难题，我们基于逻辑上的新状态空间开发了一种运动计划方法，通过同时操纵对象的关节的因素来整合机器人的功能。基于这个偏向的状态空间，我们提出了较少的行动RRT（LA-RRT），该计划者可以优化较少的动作来解决难题。我们方法的核心是一种新的路径碎片化方法，该方法重新安排和优化连续的边缘以最大程度地降低动作成本。我们使用fetch机器人解决了六个重排方案，涉及平面桌子难题和逃生室情景。 LA-RRT的表现明显优于下一个最佳渐近计划者的最终动作成本提高4.01倍至6.58倍。
Rearrangement puzzles are variations of rearrangement problems in which the elements of a problem are potentially logically linked together. To efficiently solve such puzzles, we develop a motion planning approach based on a new state space that is logically factored, integrating the capabilities of the robot through factors of simultaneously manipulatable joints of an object. Based on this factored state space, we propose less-actions RRT (LA-RRT), a planner which optimizes for a low number of actions to solve a puzzle. At the core of our approach lies a new path defragmentation method, which rearranges and optimizes consecutive edges to minimize action cost. We solve six rearrangement scenarios with a Fetch robot, involving planar table puzzles and an escape room scenario. LA-RRT significantly outperforms the next best asymptotically-optimal planner by 4.01 to 6.58 times improvement in final action cost.
