发表机构
Robotics Institute, Carnegie Mellon University; University of Michigan（卡内基梅隆大学机器人研究所; 密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多机器人手臂运动规划这一关键挑战，结合经典规划算法与矢量化碰撞检查技术，引入新运动规划器，基于CPU SIMD指令加速碰撞检查，在运动规划和执行后处理中实现加速，并发布实现降低研发门槛。
AI 中文摘要
多机器人手臂运动规划是部署多个机械手执行制造等工业任务的关键挑战。现有的基于搜索和采样的求解器通常需要大量计算时间来生成适用于安全实际执行的无碰撞、高质量运动。在这项工作中，我们引入了一套新的多机器人手臂运动规划器，能够近乎实时地生成运动，将经典规划算法与先进的矢量化碰撞检查技术相结合。基于CPU SIMD指令，新规划器加速了主要瓶颈——碰撞检查，并在多臂操作任务的运动规划和执行后处理中实现了高达两个数量级的加速。我们还发布了实现，以降低多机器人手臂规划和操作问题的研发门槛。代码可在该https URL获取。
英文摘要
Multi-robot-arm motion planning is a key challenge in deploying multiple manipulators for industrial tasks such as manufacturing. Existing search-based and sampling-based solvers often require significant computation time to produce collision-free, high-quality motions suitable for safe real-world execution. In this work, we introduce a new suite of multi-robot-arm motion planners capable of near real-time motion generation, combining classical planning algorithms with state-of-the-art vectorized collision-checking techniques. Based on CPU SIMD instructions, our new planners accelerate their primary bottleneck, collision checking, and achieve up to two orders of magnitude speedup in both motion planning and execution postprocessing for multi-arm manipulation tasks. We also release our implementation to lower the barrier for research and development of multi-robot-arm planning and manipulation problems. Code is available at https://vamp-mr.github.io/vamp-mr
Comments8 pages, 6 figures, 3 tables. To appear in the Proceedings of IROS 2026
