Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search
This addresses the problem of efficient inspection planning for robots in domains like industrial and medical robotics, representing an incremental improvement over existing methods.
The paper tackles the computationally challenging problem of inspection planning for robots by proposing IRIS, a method that incrementally densifies a motion planning roadmap and performs efficient near-optimal graph search, resulting in higher-quality inspection paths computed orders of magnitude faster than prior state-of-the-art methods.
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 that inspect the points of interest grows exponentially with the number of inspected points. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of 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 anatomy segmented from patient CT data. We show that IRIS computes higher-quality inspection paths orders of magnitudes faster than a prior state-of-the-art method.