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Title: Exponential Convergence of Infeasibility Proofs for Kinematic Motion Planning
Proving motion planning infeasibility is an important part of a complete motion planner. Common approaches for high-dimensional motion planning are only probabilistically complete. Previously, we presented an algorithm to construct infeasibility proofs by applying machine learning to sampled configurations from a bidirectional sampling-based planner. In this work, we prove that the learned manifold converges to an infeasibility proof exponentially. Combining prior approaches for sampling-based planning and our converging infeasibility proofs, we propose the term asymptotic completeness to describe the property of returning a plan or infeasibility proof in the limit. We compare the empirical convergence of different sampling strategies to validate our analysis.  more » « less
Award ID(s):
1849348
NSF-PAR ID:
10338686
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Algorithmic Foundations of Robotics XV
Page Range / eLocation ID:
294-311
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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