A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator

Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an impro...

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Autores principales:	Xiaoman Cao, Hansheng Yan, Zhengyan Huang, Si Ai, Yongjun Xu, Renxuan Fu, Xiangjun Zou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fruit picking trajectory planning particle swarm optimization multi-objective optimization Agriculture S
Acceso en línea:	https://doaj.org/article/539539287c9341dc940a869563ddd74e
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spelling	oai:doaj.org-article:539539287c9341dc940a869563ddd74e2021-11-25T16:10:21ZA Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator10.3390/agronomy111122862073-4395https://doaj.org/article/539539287c9341dc940a869563ddd74e2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4395/11/11/2286https://doaj.org/toc/2073-4395Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking.Xiaoman CaoHansheng YanZhengyan HuangSi AiYongjun XuRenxuan FuXiangjun ZouMDPI AGarticlefruit pickingtrajectory planningparticle swarm optimizationmulti-objective optimizationAgricultureSENAgronomy, Vol 11, Iss 2286, p 2286 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fruit picking trajectory planning particle swarm optimization multi-objective optimization Agriculture S
spellingShingle	fruit picking trajectory planning particle swarm optimization multi-objective optimization Agriculture S Xiaoman Cao Hansheng Yan Zhengyan Huang Si Ai Yongjun Xu Renxuan Fu Xiangjun Zou A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator
description	Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking.
format	article
author	Xiaoman Cao Hansheng Yan Zhengyan Huang Si Ai Yongjun Xu Renxuan Fu Xiangjun Zou
author_facet	Xiaoman Cao Hansheng Yan Zhengyan Huang Si Ai Yongjun Xu Renxuan Fu Xiangjun Zou
author_sort	Xiaoman Cao
title	A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator
title_short	A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator
title_full	A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator
title_fullStr	A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator
title_full_unstemmed	A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator
title_sort	multi-objective particle swarm optimization for trajectory planning of fruit picking manipulator
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/539539287c9341dc940a869563ddd74e
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A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator

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