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

Mengyu Fu1, Alan Kuntz1, Oren Salzman2

  • 1Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

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Summary
This summary is machine-generated.

We developed Incremental Random Inspection-roadmap Search (IRIS) for robot inspection planning. IRIS efficiently finds high-quality inspection paths, significantly outperforming prior methods in speed and plan quality.

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Area of Science:

  • Robotics
  • Motion Planning
  • Computational Geometry

Background:

  • Inspection planning is crucial for industrial, field, and medical robotics.
  • Computational complexity increases exponentially with the number of inspection points.
  • Existing methods struggle with scalability and efficiency for complex tasks.

Purpose of the Study:

  • To introduce a novel and efficient method for robot inspection planning.
  • To address the computational challenges in generating optimal inspection paths.
  • To improve the quality and speed of inspection plan generation.

Main Methods:

  • Proposed Incremental Random Inspection-roadmap Search (IRIS) algorithm.
  • Utilized sampling-based algorithms to incrementally build a motion planning roadmap.
  • Implemented efficient near-optimal graph search on the dynamically generated roadmap.

Main Results:

  • IRIS computes inspection plans that asymptotically converge to optimal solutions.
  • Demonstrated efficacy on simulated planar manipulator and real-world medical endoscopic tasks.
  • Achieved orders-of-magnitude speedup and higher-quality plans compared to state-of-the-art methods.

Conclusions:

  • IRIS offers a significant advancement in robot inspection planning efficiency and quality.
  • The method is effective across diverse robotic applications, including complex medical scenarios.
  • IRIS provides a scalable solution for computationally intensive inspection tasks.