Motion planning of a robotic arm using an adaptive linear interpolation crossover and variable-length move sequence genome

The ability to perform robotic arm motion planning is a necessity in the design of autonomous and intelligent robotic systems. Motion planning allows the autonomous robotic arm to maneuver its end-effector in an unstructured environment whilst avoiding obstacles on the workspace. This ability is par...

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Main Authors: Ligutan, Dino Dominic F., Espanola, Jason L., Abad, Alexander C., Bandala, Argel A., Dadios, Elmer Jose P.
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Published: Animo Repository 2019
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/1387
https://animorepository.dlsu.edu.ph/context/faculty_research/article/2386/type/native/viewcontent/HNICEM48295.2019.9073378
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-23862024-06-10T07:13:31Z Motion planning of a robotic arm using an adaptive linear interpolation crossover and variable-length move sequence genome Ligutan, Dino Dominic F. Espanola, Jason L. Abad, Alexander C. Bandala, Argel A. Dadios, Elmer Jose P. The ability to perform robotic arm motion planning is a necessity in the design of autonomous and intelligent robotic systems. Motion planning allows the autonomous robotic arm to maneuver its end-effector in an unstructured environment whilst avoiding obstacles on the workspace. This ability is particularly important in processes with pick-and-place operations and varying object positions. In this study, a genetic algorithm-based motion planning for a 4-DOF robotic arm was developed. The developed genetic algorithm operates on a variable-length genome that consists of changes in joint angles. These changes in joint angles represent the end-effector's move sequence. The results show that adaptive linear interpolation crossover (ALIX) improves the convergence of the motion path towards minimization of end-effector error and path length. On average, the end-effector error is 1.4 mm with a maximum path length deviation from a straight line of about 50.4 mm tested on extreme target points. Testing with obstacles present in the workspace shows the ability of the algorithm to generate solution paths to avoid them as well. © 2019 IEEE. 2019-01-01T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/1387 info:doi/10.1109/HNICEM48295.2019.9073378 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2386/type/native/viewcontent/HNICEM48295.2019.9073378 Faculty Research Work Animo Repository Robots—Motion Genetic algorithms Electrical and Computer Engineering Electrical and Electronics Systems and Communications
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Robots—Motion
Genetic algorithms
Electrical and Computer Engineering
Electrical and Electronics
Systems and Communications
spellingShingle Robots—Motion
Genetic algorithms
Electrical and Computer Engineering
Electrical and Electronics
Systems and Communications
Ligutan, Dino Dominic F.
Espanola, Jason L.
Abad, Alexander C.
Bandala, Argel A.
Dadios, Elmer Jose P.
Motion planning of a robotic arm using an adaptive linear interpolation crossover and variable-length move sequence genome
description The ability to perform robotic arm motion planning is a necessity in the design of autonomous and intelligent robotic systems. Motion planning allows the autonomous robotic arm to maneuver its end-effector in an unstructured environment whilst avoiding obstacles on the workspace. This ability is particularly important in processes with pick-and-place operations and varying object positions. In this study, a genetic algorithm-based motion planning for a 4-DOF robotic arm was developed. The developed genetic algorithm operates on a variable-length genome that consists of changes in joint angles. These changes in joint angles represent the end-effector's move sequence. The results show that adaptive linear interpolation crossover (ALIX) improves the convergence of the motion path towards minimization of end-effector error and path length. On average, the end-effector error is 1.4 mm with a maximum path length deviation from a straight line of about 50.4 mm tested on extreme target points. Testing with obstacles present in the workspace shows the ability of the algorithm to generate solution paths to avoid them as well. © 2019 IEEE.
format text
author Ligutan, Dino Dominic F.
Espanola, Jason L.
Abad, Alexander C.
Bandala, Argel A.
Dadios, Elmer Jose P.
author_facet Ligutan, Dino Dominic F.
Espanola, Jason L.
Abad, Alexander C.
Bandala, Argel A.
Dadios, Elmer Jose P.
author_sort Ligutan, Dino Dominic F.
title Motion planning of a robotic arm using an adaptive linear interpolation crossover and variable-length move sequence genome
title_short Motion planning of a robotic arm using an adaptive linear interpolation crossover and variable-length move sequence genome
title_full Motion planning of a robotic arm using an adaptive linear interpolation crossover and variable-length move sequence genome
title_fullStr Motion planning of a robotic arm using an adaptive linear interpolation crossover and variable-length move sequence genome
title_full_unstemmed Motion planning of a robotic arm using an adaptive linear interpolation crossover and variable-length move sequence genome
title_sort motion planning of a robotic arm using an adaptive linear interpolation crossover and variable-length move sequence genome
publisher Animo Repository
publishDate 2019
url https://animorepository.dlsu.edu.ph/faculty_research/1387
https://animorepository.dlsu.edu.ph/context/faculty_research/article/2386/type/native/viewcontent/HNICEM48295.2019.9073378
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