An exciting frontier in robotic manipulation is the use of multiple arms at once. However, planning concurrent motions is a challenging task using current methods. The high-dimensional composite state space renders many well-known motion planning algorithms intractable. Recently, Multi-Agent Path-Finding (MAPF) algorithms have shown promise in discrete 2D domains, providing rigorous guarantees. However, widely used conflict-based methods in MAPF assume an efficient single-agent motion planner. This poses challenges in adapting them to manipulation cases where this assumption does not hold, due to the high dimensionality of configuration spaces and the computational bottlenecks associated with collision checking. To this end, we propose an approach for accelerating conflict-based search algorithms by leveraging their repetitive and incremental nature -- making them tractable for use in complex scenarios involving multi-arm coordination in obstacle-laden environments. We show that our method preserves completeness and bounded sub-optimality guarantees, and demonstrate its practical efficacy through a set of experiments with up to 10 robotic arms.

翻译：机器人操作领域一个令人兴奋的前沿方向是同时使用多个机械臂。然而，使用现有方法规划并发运动是一项具有挑战性的任务。高维复合状态空间使得许多知名的运动规划算法变得难以处理。近年来，多智能体路径搜索算法在离散二维领域展现了潜力，提供了严格的理论保证。然而，MAPF中广泛使用的基于冲突的方法假设存在高效的单智能体运动规划器。这在将其应用于操作场景时带来了挑战——由于配置空间的高维性和碰撞检测相关的计算瓶颈，该假设在操作场景中并不成立。为此，我们提出了一种加速基于冲突的搜索算法的方法，利用其重复性和增量特性，使其在涉及障碍物环境中多臂协调的复杂场景中变得可行。我们证明了该方法保留了完备性和有界次优性保证，并通过多达10个机械臂的实验证明了其实用效能。