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The International Symposium of Robotics Research

ISRR 2022: Robotics Research pp 555–571Cite as

Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort

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Part of the Springer Proceedings in Advanced Robotics book series (SPAR,volume 27)

Abstract

Multiquery planning algorithms find paths between various different starts and goals in a single search space. They are designed to do so efficiently by reusing information across planning queries. This information may be computed before or during the search and often includes knowledge of valid paths.

Using known valid paths to solve an individual planning query takes less computational effort than finding a completely new solution. This allows multiquery algorithms, such as PRM*, to outperform single-query algorithms, such as RRT*, on many problems but their relative performance depends on how much information is reused. Despite this, few multiquery planners explicitly seek to maximize path reuse and, as a result, many do not consistently outperform single-query alternatives.

This paper presents Effort Informed Roadmaps (EIRM*), an almost-surely asymptotically optimal multiquery planning algorithm that explicitly prioritizes reusing computational effort. EIRM* uses an asymmetric bidirectional search to identify existing paths that may help solve an individual planning query and then uses this information to order its search and reduce computational effort. This allows it to find initial solutions up to an order-of-magnitude faster than state-of-the-art planning algorithms on the tested abstract and robotic multiquery planning problems.

Keywords

  • Sampling-based path planning
  • Optimal path planning
  • Multiquery path planning

M. P. Strub—Work performed while at the University of Oxford.

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  • DOI: 10.1007/978-3-031-25555-7_37
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Notes

  1. 1.

    All experiments were run using OMPL 1.5, on a laptop with an Intel i7-4720HQ CPU @ 2.60GHz processor with 16GB RAM.

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Acknowledgement

This research has been supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2120/1 - 390831618 and UK Research and Innovation and EPSRC through ACE-OPS: From Autonomy to Cognitive assistance in Emergency OPerationS [EP/S030832/1].

Author information

Authors and Affiliations

  1. Learning and Intelligent Systems Group, TU Berlin, Berlin, Germany

    Valentin N. Hartmann & Marc Toussaint

  2. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA

    Marlin P. Strub

  3. Estimation, Search, and Planning (ESP) Group of the Oxford Robotics Institute (ORI), University of Oxford, Oxford, UK

    Jonathan D. Gammell

Authors
  1. Valentin N. Hartmann
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  2. Marlin P. Strub
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  3. Marc Toussaint
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  4. Jonathan D. Gammell
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Correspondence to Valentin N. Hartmann .

Editor information

Editors and Affiliations

  1. EPFL STI SMT-GE, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, Switzerland

    Aude Billard

  2. Institute for Anthropomatics and Robotic, KIT, Karlsruhe, Baden-Württemberg, Germany

    Tamim Asfour

  3. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, Stanford, CA, USA

    Oussama Khatib

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Hartmann, V.N., Strub, M.P., Toussaint, M., Gammell, J.D. (2023). Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort. In: Billard, A., Asfour, T., Khatib, O. (eds) Robotics Research. ISRR 2022. Springer Proceedings in Advanced Robotics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-031-25555-7_37

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