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Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning

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  • Jae-Han Park
  • Tae-Woong Yoon

Abstract

Automated motion-planning technologies for industrial robots are critical for their application to Industry 4.0. Various sampling-based methods have been studied to generate the collision-free motion of articulated industrial robots. Such sampling-based methods provide efficient solutions to complex planning problems, but their limitations hinder the attainment of optimal results. This paper considers a method to obtain the optimal results in the roadmap algorithm that is representative of the sampling-based method. We define the coverage of a graph as a performance index of its optimality as constructed by a sampling-based algorithm and propose an optimization algorithm that can maximize graph coverage in the configuration space. The proposed method was applied to the model of an industrial robot, and the results of the simulation confirm that the roadmap graph obtained by the proposed algorithm can generate results of satisfactory quality in path-finding tests under various conditions.

Suggested Citation

  • Jae-Han Park & Tae-Woong Yoon, 2018. "Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning," Complexity, Hindawi, vol. 2018, pages 1-23, November.
    • Handle: RePEc:hin:complx:9104720
    DOI: 10.1155/2018/9104720
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    References listed on IDEAS

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    1. Kurt Marti, 2008. "Stochastic Optimization Methods," Springer Books, Springer, number 978-3-540-79458-5, January.
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