Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search

被引:0
作者
Fu, Mengyu [1 ]
Kuntz, Alan [1 ]
Salzman, Oren [2 ]
Alterovitz, Ron [1 ]
机构
[1] Univ N Carolina, Dept Comp Sci, Chapel Hill, NC 27599 USA
[2] Carnegie Mellon Univ, Robot Inst, Pittsburgh, PA 15213 USA
来源
ROBOTICS: SCIENCE AND SYSTEMS XV | 2019年
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
SHORTEST PATHS; OPTIMIZATION; ALGORITHMS;
D O I
暂无
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
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.
引用
收藏
页数:10
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