We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. We focus on collaborative manipulation tasks where the robots have to adopt intelligent collaboration strategies to be successful and effective, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability information for each robot by calling motion-planning algorithms. We then propose a method that uses the collected information to build a graph structure which captures the precedence of the manipulations of different objects and supports the implementation of a mixed-integer program to guide the search for highly effective collaborative task-and-motion plans. The search process for collaborative task-and-motion plans is based on a Monte-Carlo Tree Search (MCTS) exploration strategy to achieve exploration-exploitation balance. We evaluate our framework in two challenging MR-GTAMP domains and show that it outperforms two state-of-the-art baselines with respect to the planning time, the resulting plan length and the number of objects moved. We also show that our framework can be applied to underground mining operations where a robotic arm needs to coordinate with an autonomous roof bolter. We demonstrate plan execution in two roof-bolting scenarios both in simulation and on robots.

翻译：本文研究同步单调场景下的多机器人几何任务与运动规划（MR-GTAMP）问题。MR-GTAMP问题的目标是在存在其他可移动物体的情况下，通过多台机器人将目标物体移动至指定区域。我们聚焦于协同操控任务，要求机器人采用智能协作策略以实现高效执行，即决策由哪台机器人将哪些物体移动至何位置，并执行交接等协同操作。为赋予机器人此类协作能力，我们首先通过调用运动规划算法为每台机器人采集遮挡与可达性信息。进而提出一种方法，利用所采集信息构建捕获不同物体操控优先关系的图结构，并支持实施混合整数规划以引导高效协同任务与运动规划方案的搜索。协同任务与运动规划的搜索过程基于蒙特卡洛树搜索（MCTS）探索策略，以实现探索与利用的平衡。我们在两个具有挑战性的MR-GTAMP领域评估了该框架，结果表明其在规划时间、规划方案长度及移动物体数量方面均优于两种基准方法。我们还展示了该框架可应用于地下采矿作业场景——机械臂需与自主锚杆机协同配合。通过仿真与机器人实物实验，我们在两种锚杆支护场景中验证了规划执行效果。