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Algorithmic Foundations of Robotics XI pp 125–141Cite as

A Region-Based Strategy for Collaborative Roadmap Construction

Part of the Springer Tracts in Advanced Robotics book series (STAR,volume 107)

Abstract

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 in many 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 PRM planner. 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.

Keywords

  • Construction Guidelines
  • Region Steering
  • Probabilistic Roadmap (PRM)
  • Avoid Region
  • Standard Template Adaptive Parallel Library (STAPL)

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgments

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 Abdullah University of Science and Technology (KAUST). J. Denny supported in part by an NSF Graduate Research Fellowship.

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Authors and Affiliations

  1. Parasol Lab, Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

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

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  1. Jory Denny
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  2. Read Sandström
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  3. Nicole Julian
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  4. Nancy M. Amato
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Correspondence to Jory Denny .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Bogazici University, Istanbul, Turkey

    H. Levent Akin

  2. Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA

    Nancy M. Amato

  3. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA

    Volkan Isler

  4. Department of Information and Computing Sciences, Utrecht University, Utrecht, Utrecht, The Netherlands

    A. Frank van der Stappen

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Denny, J., Sandström, R., Julian, N., Amato, N.M. (2015). A Region-Based Strategy for Collaborative Roadmap Construction. In: Akin, H., Amato, N., Isler, V., van der Stappen, A. (eds) Algorithmic Foundations of Robotics XI. Springer Tracts in Advanced Robotics, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-319-16595-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-16595-0_8

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