Probabilistic Roadmap Methods (PRMs) are widely used motion planning methods that sample robot configurations (nodes) and connect them to form a graph (roadmap) containing feasible trajectories. Many PRM variants propose different strategies for each of the steps and choosing among them is problem dependent. Planning in heterogeneous environments and/or on parallel machines necessitates dividing the problem into regions where these choices have to be made for each one. Hand-selecting the best method for each region becomes infeasible. In particular, there are many ways to select connection candidates, and choosing the appropriate strategy is input dependent. In this paper, we present a general connection framework that adaptively selects a neighbor finding strategy from a candidate set of options. Our framework learns which strategy to use by examining their success rates and costs. It frees the user of the burden of selecting the best strategy and allows the selection to change over time. We perform experiments on rigid bodies of varying geometry and articulated linkages up to 37 degrees of freedom. Our results show that strategy performance is indeed problem/region dependent, and our adaptive method harnesses their strengths. Over all problems studied, our method differs the least from manual selection of the best method, and if one were to manually select a single method across all problems, the performance can be quite poor. Our method is able to adapt to changing sampling density and learns different strategies for each region when the problem is partitioned for parallelism. © 2013 IEEE.
|Original language||English (US)|
|Title of host publication||2013 IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||8|
|State||Published - Nov 2013|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research supported in part by NSF awards CNS-0551685,CCF-0833199,CCF-0830753,IIS-0917266,IIS-0916053,EFRI-1240483,RI-1217991,by NSF/DNDO award 2008-DN-077-ARI018-02, by NIH NCI R25 CA090301-11, by DOE awards DE-FC52-08NA28616, DE-AC02-06CH11357, B575363, B575366, by THECB NHARP award 000512-00972009, by Samsung, Chevron, IBM, Intel, Oracle/Sun, by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST),and by Schlumberger Faculty for the Future Fellowship.This research used resources of the National Energy Research Scientific Computing Center,which is supported by the Office of Science of the U.S. Department of Energy under Contract No.DE-AC02-05CH11231.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.