A Region-Based Strategy for Collaborative Roadmap Construction

Jory Denny, Read Sandström, Nicole Julian, Nancy M. Amato

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Scopus citations


© Springer International Publishing Switzerland 2015. Motion planning has seen much attention over the past two decades. A great deal of progress has been made in sampling-based planning, whereby a planner builds an approximate representation of the planning space. While these planners have demonstrated success inmany scenarios, there are still difficult problems where they lack robustness or efficiency, e.g., certain types of narrow spaces. Conversely, human intuition can often determine an approximate solution to these problems quite effectively, but humans lack the speed and precision necessary to perform the corresponding low-level tasks (such as collision checking) in a timely manner. In this work, we introduce a novel strategy called Region Steering in which the user and a PRM planner work cooperatively to map the space while maintaining the probabilistic completeness property of the PRMplanner. Region Steering utilizes two-way communication to integrate the strengths of both the user and the planner, thereby overcoming the weaknesses inherent to relying on either one alone. In one communication direction, a user can input regions, or bounding volumes in the workspace, to bias sampling towards or away from these areas. In the other direction, the planner displays its progress to the user and colors the regions based on their perceived usefulness.We demonstrate that Region Steering provides roadmap customizability, reduced mapping time, and smaller roadmap sizes compared with fully automated PRMs, e.g., Gaussian PRM.
Original languageEnglish (US)
Title of host publicationAlgorithmic Foundations of Robotics XI
PublisherSpringer Nature
Number of pages17
ISBN (Print)9783319165943
StatePublished - Apr 30 2015
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-0916053, IIS-0917266, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11, by Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King AbdullahUniversity of Science and Technology (KAUST). J. Denny supported in part by an NSF GraduateResearch Fellowship
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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