An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning
Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA)...
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Format: | Article |
Language: | English |
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Wiley
2021-01-01
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Series: | Journal of Advanced Transportation |
Online Access: | http://dx.doi.org/10.1155/2021/3606895 |
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author | Ruo-Bin Wang Wei-Feng Wang Lin Xu Jeng-Shyang Pan Shu-Chuan Chu |
author_facet | Ruo-Bin Wang Wei-Feng Wang Lin Xu Jeng-Shyang Pan Shu-Chuan Chu |
author_sort | Ruo-Bin Wang |
collection | DOAJ |
description | Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning. |
format | Article |
id | doaj-art-9e89e3d45dba4abcaaf79847eae045ac |
institution | Kabale University |
issn | 0197-6729 2042-3195 |
language | English |
publishDate | 2021-01-01 |
publisher | Wiley |
record_format | Article |
series | Journal of Advanced Transportation |
spelling | doaj-art-9e89e3d45dba4abcaaf79847eae045ac2025-02-03T01:27:05ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/36068953606895An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path PlanningRuo-Bin Wang0Wei-Feng Wang1Lin Xu2Jeng-Shyang Pan3Shu-Chuan Chu4School of Information Science and Technology, North China University of Technology, Beijing 100043, ChinaSchool of Information Science and Technology, North China University of Technology, Beijing 100043, ChinaSTEM, University of South Australia, Adelaide 5095, AustraliaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaPath planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.http://dx.doi.org/10.1155/2021/3606895 |
spellingShingle | Ruo-Bin Wang Wei-Feng Wang Lin Xu Jeng-Shyang Pan Shu-Chuan Chu An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning Journal of Advanced Transportation |
title | An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning |
title_full | An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning |
title_fullStr | An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning |
title_full_unstemmed | An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning |
title_short | An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning |
title_sort | adaptive parallel arithmetic optimization algorithm for robot path planning |
url | http://dx.doi.org/10.1155/2021/3606895 |
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