Paper Info
Title
Adapting RRT growth for heterogeneous environments.
Abstract
Rapidly-exploring Random Trees (RRTs) are effective for a wide range of applications ranging from kinodynamic planning to motion planning under uncertainty. However, RRTs are not as efficient when exploring heterogeneous environments and do not adapt to the space. For example, in difficult areas an expensive RRT growth method might be appropriate, while in open areas inexpensive growth methods should be chosen. In this paper, we present a novel algorithm, Adaptive RRT, that adapts RRT growth to the current exploration area using a two level growth selection mechanism. At the first level, we select groups of expansion methods according to the visibility of the node being expanded. Second, we use a cost-sensitive learning approach to select a sampler from the group of expansion methods chosen. Also, we propose a novel definition of visibility for RRT nodes which can be computed in an online manner and used by Adaptive RRT to select an appropriate expansion method. We present the algorithm and experimental analysis on a broad range of problems showing not only its adaptability, but efficiency gains achieved by adapting exploration methods appropriately.
Year
DOI
Venue
2013
10.1109/IROS.2013.6696589
IROS
Keywords
Field
DocType
learning (artificial intelligence),mobile robots,random processes,trees (mathematics),RRT growth method,RRT node visibility,adaptive RRT,cost-sensitive learning approach,expansion method,exploration method,heterogeneous environments,kinodynamic planning,motion planning,rapidly-exploring random trees,two-level growth selection mechanism
Adaptability,Motion planning,Kinodynamic planning,Visibility,Computer science,Stochastic process,Control engineering,Ranging,Artificial intelligence,Machine learning,Mobile robot
Conference
ISSN
Citations 
PageRank 
2153-0858
8
0.49
References 
Authors
16
4
Name
Order
Citations
PageRank
Jory Denny1828.04
Marco Morales215013.97
Samuel Rodríguez325315.69
Nancy M. Amato42328187.71