Fast Anytime Motion Planning in Point Clouds by Interleaving Sampling and Interior Point Optimization
Fast Anytime Motion Planning in Point Clouds by Interleaving Sampling and Interior Point Optimization
复制标题
通过交错采样和内点优化在点云中进行快速随时运动规划
DOI：
10.1007/978-3-030-28619-4_63
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
R. Alterovitz
中科院分区：
文献类型：
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作者：
A. Kuntz;Chris Bowen;R. Alterovitz
在家庭和办公室等非结构化环境中操作的机器人需要在依赖现实世界传感器（通常会产生点云）的情况下快速规划动作。为了实现直观、交互和反应性的用户界面，运动计划计算应该快速、随时提供高质量的解决方案，这意味着算法可以逐步改进其解决方案，并且可以随时中断并返回有效的解决方案。为了应对这些挑战，我们结合了两种范式：(1)基于渐近最优采样的运动规划，它可以有效地提供任何时间的解决方案，但在高维构型空间中难以快速收敛到高质量的解决方案；(2)优化，它可以快速地局部细化路径。我们建议使用内部点优化，因为它能够随时执行，保证在每次迭代中避免障碍，并且我们提供了一种新的惰性公式，可以有效地直接操作点云数据。该方法在基于随时采样的运动规划和基于随时惰性内点优化之间迭代交替，快速计算出高质量的运动规划，并收敛到全局最优解。
Robotic manipulators operating in unstructured environments such as homes and offices need to plan their motions quickly while relying on real-world sensors, which typically produce point clouds. To enable intuitive, interactive, and reactive user interfaces, the motion plan computation should provide high-quality solutions quickly and in an anytime manner, meaning the algorithm progressively improves its solution and can be interrupted at any time and return a valid solution. To address these challenges, we combine two paradigms: (1) asymptotically-optimal sampling-based motion planning, which is effective at providing anytime solutions but can struggle to quickly converge to high quality solutions in high dimensional configuration spaces, and (2) optimization, which locally refines paths quickly. We propose the use of interior point optimization for its ability to perform in an anytime manner that guarantees obstacle avoidance in each iteration, and we provide a novel lazy formulation that efficiently operates directly on point cloud data. Our method iteratively alternates between anytime sampling-based motion planning and anytime, lazy interior point optimization to compute high quality motion plans quickly, converging to a globally optimal solution.