Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search.
Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search.
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DOI：
10.15607/rss.2019.xv.057
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发表时间：
2019-06-01
期刊：
影响因子：
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通讯作者：
Alterovitz, Ron
中科院分区：
文献类型：
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作者：
Fu, Mengyu;Kuntz, Alan;Alterovitz, Ron
检查规划，允许机器人检查一组感兴趣的点的规划运动的任务，在诸如工业、现场和医疗机器人等领域中具有应用。检查规划可能在计算上具有挑战性，因为运动规划上的搜索空间随着要检查的兴趣点的数量呈指数增长。我们提出了一种新的方法，增量随机检查路线图搜索（IRIS），计算检查计划的长度和一组成功的检查点渐近收敛到最佳的检查计划。IRIS使用基于采样的算法逐步加密运动规划路线图，并在生成路线图时对其执行高效的接近最优的图搜索。我们证明了IRIS的功效，在一个模拟的平面5自由度机械手检查任务和医疗内窥镜检查任务的连续并行手术机器人在混乱的解剖结构分割从病人的CT数据。我们表明，IRIS计算更高质量的检查计划的数量级快于现有的最先进的方法。
Inspection planning, the task of planning motions that allow a robot to inspect a set of points of interest, has applications in domains such as industrial, field, and medical robotics. Inspection planning can be computationally challenging, as the search space over motion plans grows exponentially with the number of points of interest to inspect. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of successfully inspected points asymptotically converge to those of an optimal inspection plan. IRIS incrementally densifies a motion planning roadmap using sampling-based algorithms, and performs efficient near-optimal graph search over the resulting roadmap as it is generated. We demonstrate IRIS's efficacy on a simulated planar 5DOF manipulator inspection task and on a medical endoscopic inspection task for a continuum parallel surgical robot in cluttered anatomy segmented from patient CT data. We show that IRIS computes higher-quality inspection plans orders of magnitudes faster than a prior state-of-the-art method.