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International Workshop on the Algorithmic Foundations of Robotics

WAFR 2018: Algorithmic Foundations of Robotics XIII pp 36–51Cite as

Fast Collision Detection for Motion Planning Using Shape Primitive Skeletons

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Part of the Springer Proceedings in Advanced Robotics book series (SPAR,volume 14)

Abstract

In many robotics applications, the environment (robots and obstacles) often have very complex geometries. These result in expensive primitive computations such as collision detection which in turn, affect the overall performance of these applications. Approximating the geometry is a common approach to optimize computation. Unlike other applications of geometric approximation where it is applied to one space (usually obstacle space), we approximate both obstacle and free workspace with a set of geometric shape primitives that are completely contained within the space and represent its topology (skeleton). We use these “shape primitive skeletons” to improve collision detection performance in motion planning algorithms. Our results show that the use of shape primitive skeletons improves the performance of standard collision detection methods in motion planning problems by 20–70% in our 2D and 3D test environments regardless of motion planning strategy. We also show how the same shape primitive skeletons can be used with robots of different sizes to improve the performance of collision detection operation.

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Authors and Affiliations

  1. Parasol Laboratory, Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA

    Mukulika Ghosh, Shawna Thomas & Nancy M. Amato

Authors
  1. Mukulika Ghosh
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  2. Shawna Thomas
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  3. Nancy M. Amato
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Corresponding author

Correspondence to Mukulika Ghosh .

Editor information

Editors and Affiliations

  1. Departamento de Sistemas Digitales, Instituto Tecnológico Autónomo de México, México, Mexico

    Assist. Prof. Marco Morales

  2. Department of Computer Science, University of New Mexico, Albuquerque, NM, USA

    Prof. Dr. Lydia Tapia

  3. Universidad Politécnica de Yucatán, Yucatán, Mexico

    Gildardo Sánchez-Ante

  4. Georgia Tech, Atlanta, GA, USA

    Prof. Seth Hutchinson

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Ghosh, M., Thomas, S., Amato, N.M. (2020). Fast Collision Detection for Motion Planning Using Shape Primitive Skeletons. In: Morales, M., Tapia, L., Sánchez-Ante, G., Hutchinson, S. (eds) Algorithmic Foundations of Robotics XIII. WAFR 2018. Springer Proceedings in Advanced Robotics, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-44051-0_3

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