Asymptotically optimal inspection planning via efficient near-optimal search on sampled roadmaps

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Creator
  • Fu, Mengyu
    • Affiliation: College of Arts and Sciences, Department of Computer Science
  • Kuntz, Alan
  • Salzman, Oren
    • Other Affiliation: Technion - Israel Institute of Technology
  • Alterovitz, Ron
    • Affiliation: College of Arts and Sciences, Department of Computer Science
Abstract
  • Inspection planning, the task of planning motions for a robot that enable it 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 a sampling-based algorithm and performs efficient near-optimal graph search over the resulting roadmap as it is generated. We prove the resulting algorithm is asymptotically optimal under very general assumptions about the robot and the environment. We demonstrate IRIS’s efficacy on a simulated inspection task with a planar five DOF manipulator, on a simulated bridge inspection task with an Unmanned Aerial Vehicle (UAV), and on a medical endoscopic inspection task for a continuum parallel surgical robot in cluttered human anatomy. In all these systems IRIS computes higher-quality inspection plans orders of magnitudes faster than a prior state-of-the-art method.
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Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • SAGE Publications
License
  • Attribution-NonCommercial 4.0 International
Journal title
  • The International Journal of Robotics Research
Journal volume
  • 42
Journal issue
  • 4-5
Page start
  • 150
Page end
  • 175
Funder
  • Ministry of Science, Technology and Space
  • National Science Foundation
  • United States-Israel Binational Science Foundation
ISSN
  • 0278-3649
  • 1741-3176
Copyright date
  • 2023
Publisher
  • SAGE Publications

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