We introduce Lazy-DaSH, an improvement over the recent state of the art multi-robot task and motion planning method DaSH, which scales to more than double the number of robots and objects compared to the original method and achieves an order of magnitude faster planning time when applied to a multi-manipulator object rearrangement problem. We achieve this improvement through a hierarchical approach, where a high-level task planning layer identifies planning spaces required for task completion, and motion feasibility is validated lazily only within these spaces. In contrast, DaSH precomputes the motion feasibility of all possible actions, resulting in higher costs for constructing state space representations. Lazy-DaSH maintains efficient query performance by utilizing a constraint feedback mechanism within its hierarchical structure, ensuring that motion feasibility is effectively conveyed to the query process. By maintaining smaller state space representations, our method significantly reduces both representation construction time and query time. We evaluate Lazy-DaSH in four distinct scenarios, demonstrating its scalability to increasing numbers of robots and objects, as well as its adaptability in resolving conflicts through the constraint feedback mechanism.

翻译：我们提出了Lazy-DaSH，这是对当前最先进的多机器人任务与运动规划方法DaSH的一项改进。与原方法相比，Lazy-DaSH可扩展至两倍以上的机器人和物体数量，并且在应用于多机械臂物体重排问题时，规划时间缩短了一个数量级。我们通过一种分层方法实现了这一改进：高层任务规划层识别完成任务所需的规划空间，而运动可行性仅在上述空间内以惰性方式进行验证。相比之下，DaSH会预先计算所有可能动作的运动可行性，导致构建状态空间表示的成本更高。Lazy-DaSH通过在其分层结构中利用约束反馈机制，将运动可行性有效地传递给查询过程，从而保持了高效的查询性能。通过维持更小的状态空间表示，我们的方法显著减少了表示构建时间和查询时间。我们在四种不同的场景中评估了Lazy-DaSH，证明了其能够扩展到不断增加的机器人和物体数量，并且能够通过约束反馈机制有效解决冲突，展现了良好的适应性。