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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Main Authors: Ruo-Bin Wang, Wei-Feng Wang, Lin Xu, Jeng-Shyang Pan, Shu-Chuan Chu
Format: Article
Language:English
Published: Wiley 2021-01-01
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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