改进A*算法，让机器人更快更准地整理桌面上的物品。
Planning for Tabletop Object Rearrangement
- 用更优状态表示和增量目标尝试提升搜索效率
- 在20个物体时成功率超orla*，且耗时减少40%以上
- 适合移动机器人与固定机械臂的桌面重排任务
桌面上物体重排规划是一个具有挑战性的问题。相较于确定目标布局，重排规划因物体间依赖关系和缓冲区容量限制而更加困难。尽管OrLa*提出了基于A*的懒惰评估方法以获得高质量解，但其可扩展性差，随着物体数量增加成功率显著下降。为此，我们提出一种增强型A*算法，通过改进状态表示，并在每次迭代中采用增量目标尝试与懒惰评估策略，旨在提升可扩展性的同时保持解的质量。实验表明，该算法在静态与移动机器人场景下均能比OrLa*更快获得更优解。
原文摘要 · Abstract (English)
Finding an high-quality solution for the tabletop object rearrangement planning is a challenging problem. Compared to determining a goal arrangement, rearrangement planning is challenging due to the dependencies between objects and the buffer capacity available to hold objects. Although orla* has proposed an A* based searching strategy with lazy evaluation for the high-quality solution, it is not scalable, with the success rate decreasing as the number of objects increases. To overcome this limitation, we propose an enhanced A*-based algorithm that improves state representation and employs incremental goal attempts with lazy evaluation at each iteration. This approach aims to enhance scalability while maintaining solution quality. Our evaluation demonstrates that our algorithm can provide superior solutions compared to orla*, in a shorter time, for both stationary and mobile robots.
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