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...

Full description

Saved in:

Bibliographic Details
Published in:	2015 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 2646 - 2652
Main Authors:	Arslan, Oktay, Tsiotras, Panagiotis
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.09.2015
Subjects:	Approximation algorithms Machine learning algorithms Planning Prediction algorithms Search problems Training Yttrium
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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