Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning

Jae Han Park, Tae Woong Yoon

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Article number9104720
JournalComplexity
Volume2018
DOIs
Publication statusPublished - 2018 Jan 1

Fingerprint

Motion planning
Industrial robots
Sampling
Planning
Industry

ASJC Scopus subject areas

  • General

Cite this

Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning. / Park, Jae Han; Yoon, Tae Woong.

In: Complexity, Vol. 2018, 9104720, 01.01.2018.

Research output: Contribution to journalArticle

@article{3eb21f657b7c46c485d1ac366798f002,
title = "Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning",
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.",
author = "Park, {Jae Han} and Yoon, {Tae Woong}",
year = "2018",
month = "1",
day = "1",
doi = "10.1155/2018/9104720",
language = "English",
volume = "2018",
journal = "Complexity",
issn = "1076-2787",
publisher = "John Wiley and Sons Inc.",

}

TY - JOUR

T1 - Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning

AU - Park, Jae Han

AU - Yoon, Tae Woong

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85057099459&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85057099459&partnerID=8YFLogxK

U2 - 10.1155/2018/9104720

DO - 10.1155/2018/9104720

M3 - Article

AN - SCOPUS:85057099459

VL - 2018

JO - Complexity

JF - Complexity

SN - 1076-2787

M1 - 9104720

ER -