Lazy Toggle PRM: A single-query approach to motion planning

Jory Denny, Kensen Shi, Nancy M. Amato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations


Probabilistic RoadMaps (PRMs) are quite suc-cessful in solving complex and high-dimensional motion plan-ning problems. While particularly suited for multiple-query scenarios and expansive spaces, they lack efficiency in both solving single-query scenarios and mapping narrow spaces. Two PRM variants separately tackle these gaps. Lazy PRM reduces the computational cost of roadmap construction for single-query scenarios by delaying roadmap validation until query time. Toggle PRM is well suited for mapping narrow spaces by mapping both Cfree and Cobst, which gives certain theoretical benefits. However, fully validating the two resulting roadmaps can be costly. We present a strategy, Lazy Toggle PRM, for integrating these two approaches into a method which is both suited for narrow passages and efficient single-query calculations. This simultaneously addresses two challenges of PRMs. Like Lazy PRM, Lazy Toggle PRM delays validation of roadmaps until query time, but if no path is found, the algorithm augments the roadmap using the Toggle PRM methodology. We demonstrate the effectiveness of Lazy Toggle PRM in a wide range of scenarios, including those with narrow passages and high descriptive complexity (e.g., those described by many triangles), concluding that it is more effective than existing methods in solving difficult queries. © 2013 IEEE.
Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)9781467356435
StatePublished - May 2013
Externally publishedYes

Bibliographical note

KAUST 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 NIH NCI R25 CA090301-11, by THECB NHARP award000512-0097-2009, by Chevron, IBM, Intel, Oracle/Sun and by AwardKUS-C1-016-04, made by King Abdullah University of Science and Technology(KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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