Recent advances in sampling-based motion planning algorithms for high DOF arms leverage GPUs to provide SOTA performance. These algorithms can be used to control multiple arms jointly, but this approach scales poorly. To address this, we extend STORM, a sampling-based model-predictive-control (MPC) motion planning algorithm, to handle multiple robots in a distributed fashion. First, we modify STORM to handle dynamic obstacles. Then, we let each arm compute its own motion plan prefix, which it shares with the other arms, which treat it as a dynamic obstacle. Finally, we add a dynamic priority scheme. The new algorithm, MR-STORM, demonstrates clear empirical advantages over SOTA algorithms when operating with both static and dynamic obstacles.

翻译：近年来，针对高自由度机械臂的基于采样的运动规划算法利用GPU实现了最先进的性能。这些算法可用于联合控制多个机械臂，但该方法扩展性较差。为解决此问题，我们扩展了STORM——一种基于采样的模型预测控制（MPC）运动规划算法，使其能够以分布式方式处理多个机器人。首先，我们修改STORM以处理动态障碍物。随后，让每个机械臂计算自身的运动规划前缀，并将其共享给其他机械臂，其他机械臂将其视为动态障碍物。最后，我们引入了动态优先级机制。新算法MR-STORM在静态与动态障碍物共存的操作环境下，相较于最先进算法展现出明显的实证优势。