Machine learning guided exploration for sampling-based motion planning algorithms

We propose a machine learning (ML)-inspired approach to estimate the relevant region of a motion planning problem during the exploration phase of sampling-based path-planners. The algorithm guides the exploration so that it draws more samples from the relevant region as the number of iterations incr...

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Vydáno v:	2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS) s. 2646 - 2652
Hlavní autoři:	Arslan, Oktay, Tsiotras, Panagiotis
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.09.2015
Témata:	Approximation algorithms Machine learning algorithms Planning Prediction algorithms Search problems Training Yttrium
On-line přístup:	Získat plný text
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Popis
Shrnutí:	We propose a machine learning (ML)-inspired approach to estimate the relevant region of a motion planning problem during the exploration phase of sampling-based path-planners. The algorithm guides the exploration so that it draws more samples from the relevant region as the number of iterations increases. The approach works in two steps: first, it predicts if a given sample is collision-free (classification phase) without calling the collision-checker, and it then estimates if it is a promising sample, i.e., if it has the potential to improve the current best solution (regression phase), without solving the local steering problem. The proposed exploration strategy is integrated to the RRT # algorithm. Numerical simulations demonstrate the efficiency of the proposed approach.
DOI:	10.1109/IROS.2015.7353738