2 research outputs found
Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search
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.Comment: RSS 201