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Design Space Exploration for Sampling-Based Motion Planning Programs with Combinatory Logic Synthesis

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Algorithmic Foundations of Robotics XV (WAFR 2022)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 25))

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Abstract

Motion Planning is widely acknowledged as a fundamental problem of robotics. Due to the continuous efforts of the scientific community, various algorithmic families emerged that have different strengths and weaknesses. Finding a suitable motion planning program is often not trivial for real-world problems, as various domain-specific factors must be considered. An obvious example is a potential trade-off between path length, computation time, and resource constraints. We propose a technique to systematically explore the space of suitable programs, aiming to find Pareto optimal algorithm configurations. Our approach makes use of Combinatory Logic Synthesis to perform component-based software composition. Software components are injected with domain-knowledge, effectively restricting the solution space of synthesizable programs. We synthesize sample-based global planning programs that make use of the Open Motion Planning Library (OMPL) and evaluate the produced programs to yield numeric result vectors. These steps are encapsulated in a black-box function which is used with a multi-objective optimization tool (Hypermapper) to yield an automatic, learning-based search procedure for a given feature space. We validate our approach with a series of experiments that demonstrate the extensibility and transferability of our methodology regarding different robotic systems and planning instances.

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Notes

  1. 1.

    http://omplapp.kavrakilab.org/.

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Authors and Affiliations

  1. TU Dortmund University, 44227, Dortmund, Germany

    Tristan Schäfer, Jan Bessai, Constantin Chaumet, Jakob Rehof & Christian Riest

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  1. Tristan Schäfer
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  2. Jan Bessai
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  3. Constantin Chaumet
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  4. Jakob Rehof
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  5. Christian Riest
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Corresponding author

Correspondence to Tristan Schäfer .

Editor information

Editors and Affiliations

  1. Center for Ubiquitous Computing, University of Oulu, Oulu, Finland

    Steven M. LaValle

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

    Jason M. O’Kane

  3. Department of Aerospace Engineering, University of Maryland, College Park, MD, USA

    Michael Otte

  4. Gates Computer Science, Stanford University, Stanford, CA, USA

    Dorsa Sadigh

  5. Department of Computer Science, University of Maryland, College Park, MD, USA

    Pratap Tokekar

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Schäfer, T., Bessai, J., Chaumet, C., Rehof, J., Riest, C. (2023). Design Space Exploration for Sampling-Based Motion Planning Programs with Combinatory Logic Synthesis. In: LaValle, S.M., O’Kane, J.M., Otte, M., Sadigh, D., Tokekar, P. (eds) Algorithmic Foundations of Robotics XV. WAFR 2022. Springer Proceedings in Advanced Robotics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-031-21090-7_3

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