发表机构
Purdue University; Massachusetts Institute of Technology（普渡大学; 麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出ReVAMP，一种基于解析逆运动学重参数化规划空间的向量化运动规划方法，通过暴露并行性解决现有低效问题，在高达20维复杂约束系统中实现比最先进技术快10倍的微秒级规划，并促进顺序操作流程的重组。
AI 中文摘要
机器人在执行现实世界任务时，其运动规划通常需要满足一个或多个约束条件。当这些约束将有效构型空间缩减为零测度子集时，基于采样的规划算法需要修改才能生成可行样本。对于许多常见的末端执行器约束，基于逆运动学（IK）构建的参数化方法提供了一种替代公式，在该公式中约束通过构造得以满足，从而可以直接对可行集进行采样。尽管这些参数化规划器方法优雅，但其速度仍慢于基于投影方法的向量加速实现，其性能上限仍是一个悬而未决的问题。我们探索了一种新的向量化维度，该维度基于通过解析逆运动学对规划空间进行重参数化。这种方法解决了向量化投影规划器中现有的低效问题，并揭示了规划器内部并行化的新机遇。我们展示了该规划器能够为高维系统（高达20维）在微秒到毫秒级别内合成规划，对于复杂约束，其速度比当前最先进技术快达10倍。此外，我们还演示了这种规划速度如何为重组顺序操作流程开辟了新途径。
英文摘要
Robots often must satisfy one or more constraints during motion planning for real-world tasks. When such constraints reduce the valid configuration space to a measure-zero subset, sampling based planning algorithms require modifications to draw feasible samples. For many common end-effector constraints, parameterizations built on inverse kinematics (IK) provide an alternate formulation where the constraints are satisfied by construction, allowing directly sampling the feasible set. Despite their elegant approach, parameterized planners have remained slower than vector-accelerated implementations of projection-based approaches, leaving their performance ceiling an open question. We explore a new axis of vectorization built upon reparameterizing the planning space through analytic IK. This approach addresses existing inefficiencies in vectorized projection-based planners and exposes new opportunities for parallelism within the planner. We show that the planner can synthesize plans in microseconds to milliseconds for high dimensional systems (up to 20 dimensions), with complex constraints, up to 10x faster than the current state-of-the-art. Furthermore, we demonstrate how such planning speeds open up avenues for restructuring sequential manipulation pipelines.
