多机械臂协同运动规划，效率更高更稳定。
Decoupled MPPI-Based Multi-Arm Motion Planning
- 各机械臂自主规划前段路径并共享，视为动态障碍物
- 引入动态优先级机制，提升多臂协作稳定性
- 在静态与动态障碍下均优于现有最优算法
高自由度机械臂的采样式运动规划算法近年借助GPU实现了顶尖性能。这些算法可联合控制多臂，但扩展性差。为此，我们扩展了基于模型预测控制的采样算法STORM，使其以分布式方式处理多机器人。首先，改进STORM以应对动态障碍；其次，让每臂独立计算自身运动规划前缀，并共享给其他臂，后者将其视为动态障碍；最后，加入动态优先级策略。新算法MR-STORM在存在静态和动态障碍时，均展现出显著的实证优势，优于当前最优算法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。