用并行计算加速机器人约束运动规划，实现实时控制。
Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body Control
- 将约束投影转化为可并行处理结构，利用CPU SIMD加速
- 对人形机器人实现100至1000倍速度提升，达实时规划
- 适合需要实时全身运动规划的动态场景应用
许多机器人规划任务要求在整个轨迹中满足一个或多个约束。针对几何约束，流形约束运动规划算法可在任务指定的约束子流形上规划无碰撞路径。现有最先进方法对复杂系统（如人形机器人）求解需数十秒，难以在实际动态环境中使用。受硬件加速运动规划进展启发，我们提出一种基于CPU SIMD加速的流形约束运动规划器，重新审视基于投影的约束满足机制，通过将相关组件转换为可并行结构，利用SIMD并行性生成满足约束的解。该方法相较现有最优方案提速达100至1000倍，首次使实时约束运动规划成为可能。我们在真实人形机器人上验证了该规划器，实现了实时全身准静态路径生成。
原文摘要 · Abstract (English)
Many robot planning tasks require satisfaction of one or more constraints throughout the entire trajectory. For geometric constraints, manifold-constrained motion planning algorithms are capable of planning collision-free path between start and goal configurations on the constraint submanifolds specified by task. Current state-of-the-art methods can take tens of seconds to solve these tasks for complex systems such as humanoid robots, making real-world use impractical, especially in dynamic settings. Inspired by recent advances in hardware accelerated motion planning, we present a CPU SIMD-accelerated manifold-constrained motion planner that revisits projection-based constraint satisfaction through the lens of parallelization. By transforming relevant components into parallelizable structures, we use SIMD parallelism to plan constraint satisfying solutions. Our approach achieves up to 100-1000x speed-ups over the state-of-the-art, making real-time constrained motion planning feasible for the first time. We demonstrate our planner on a real humanoid robot and show real-time whole-body quasi-static plan generation. Our work is available at https://commalab.org/papers/mcvamp/.
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