用向量化技术加速多机械臂运动规划，实现近实时生成安全路径。
VAMP-MR: Vector-Accelerated Motion Planning and Execution for Multi-Robot-Arms
- 结合经典算法与向量化碰撞检测，提升规划效率。
- 在多机械臂任务中实现比传统方法快100倍的运动规划速度。
- 适合工业自动化中需要快速响应的多臂协同场景。
多机械臂运动规划是工业制造等场景中部署多个操作臂的关键挑战。现有基于搜索和采样的求解器通常需大量计算时间才能生成无碰撞、高质量且适合安全执行的运动轨迹。本文提出一套新的多机械臂运动规划方法，结合经典规划算法与最先进的向量化碰撞检测技术。基于CPU SIMD指令，新方法显著加速了核心瓶颈——碰撞检测，在多臂操作任务中实现了运动规划与执行后处理速度提升高达两个数量级。同时，我们开源了实现代码，降低多机械臂规划与操作研究的门槛。代码见 https://vamp-mr.github.io/vamp-mr。
原文摘要 · Abstract (English)
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
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