KAUST Grant NumberKUS-C1-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/597450
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AbstractRapidly-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. © 2013 IEEE.
CitationDenny J, Morales M, Rodriguez S, Amato NM (2013) Adapting RRT growth for heterogeneous environments. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Available: http://dx.doi.org/10.1109/IROS.2013.6696589.
SponsorsThis research supported in part by NSF awards CNS-0551685,CCF-0833199,CCF-0830753,IIS-0917266,IIS-0916053,EFRI-1240483,RI-1217991,by NIH NCI R25 CA090301-11, by THECB NHARP award 000512-0097-2009,by Chevron, IBM, Intel, Oracle/Sun, by Award KUS-C1-016-04,made by King Abdullah University of Science and Technology (KAUST), and by Instituto Tecnologico Autonomo de Mexico and Academia Mexicana de Cultura A.C.