Path Planning for Mount Robot Based on Improved Particle Swarm Optimization Algorithm

被引:9
作者
Li, Xudong [1 ]
Tian, Bin [1 ]
Hou, Shuaidong [1 ]
Li, Xinxin [1 ]
Li, Yang [1 ]
Liu, Chong [1 ,2 ]
Li, Jingmin [1 ,2 ,3 ]
机构
[1] Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China
[2] Dalian Univ Technol, Key Lab Micro Nano Technol & Syst Liaoning Prov, Dalian 116024, Peoples R China
[3] Dalian Univ Technol, State Key Lab High Performance Precis Mfg, Dalian 116024, Peoples R China
关键词
mount robot; path planning; particle swarm optimization (PSO); adaptive strategy; PSO;
D O I
10.3390/electronics12153289
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
To address the problem of cooperative work among right-angle coordinate robots in spacecraft structural plate mount tasks, an improved particle swarm optimization (PSO) algorithm was proposed to assign paths to three robots in a surface-mounted technology (SMT) machine. First, the optimization objective of path planning was established by analyzing the working process of the SMT machine. Then, the inertia weight update strategy was designed to overcome the early convergence of the traditional PSO algorithm, and the learning factor of each particle was calculated using fuzzy control to improve the global search capability. To deal with the concentration phenomenon of particles in the iterative process, the genetic algorithm (GA) was introduced when the particles were similar. The particles were divided into elite, high-quality, or low-quality particles according to their performance. New particles were generated through selection and crossover operations to maintain the particle diversity. The performance of the proposed algorithm was verified with the simulation results, which could shorten the planning path and quicken the convergence compared to the traditional PSO or GA. For large and complex maps, the proposed algorithm shortens the path by 7.49% and 11.49% compared to traditional PSO algorithms, and by 3.98% and 4.02% compared to GA.
引用
收藏
页数:17
相关论文
共 37 条
[1]  
Abualigah L., 2022, Integrating MetaHeuristics and Machine Learning for RealWorld Optimization Problems, P481, DOI DOI 10.1007/978-3-030-99079-4_19
[2]   An Enhanced PSO Algorithm for Scheduling Workflow Tasks in Cloud Computing [J].
Anbarkhan, Samar Hussni ;
Rakrouki, Mohamed Ali .
ELECTRONICS, 2023, 12 (12)
[3]   Printed circuit board assembly time minimisation using a novel Bees Algorithm [J].
Castellani, Marco ;
Otri, Sameh ;
Duc Truong Pham .
COMPUTERS & INDUSTRIAL ENGINEERING, 2019, 133 :186-194
[4]   Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators [J].
Das, P. K. ;
Jena, P. K. .
APPLIED SOFT COMPUTING, 2020, 92
[5]  
Duan Q., 2022, MACH TOOL HYDRAUL, V50, P50
[6]   Improved ant colony optimization algorithm for the traveling salesman problems [J].
Gan, Rongwei ;
Guo, Qingshun ;
Chang, Huiyou ;
Yi, Yang .
JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2010, 21 (02) :329-333
[7]   Apple-Picking Robot Picking Path Planning Algorithm Based on Improved PSO [J].
Gao, Ruilong ;
Zhou, Qiaojun ;
Cao, Songxiao ;
Jiang, Qing .
ELECTRONICS, 2023, 12 (08)
[8]   A Centralized Strategy for Multi-Agent Exploration [J].
Gul, Faiza ;
Mir, Adnan ;
Mir, Imran ;
Mir, Suleman ;
Ul Islaam, Tauqeer ;
Abualigah, Laith ;
Forestiero, Agostino .
IEEE ACCESS, 2022, 10 :126871-126884
[9]   A novel placement method for mini-scale passive components in surface mount technology [J].
He, Jingxi ;
Cen, Yuqiao ;
Li, Yuanyuan ;
Alelaumi, Shrouq M. ;
Won, Daehan .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2021, 115 (5-6) :1475-1485
[10]   Synthesis of silica-supported ZnO pigments for thermal control coatings and analysis of their reflection model [J].
Heydari, V. ;
Bahreini, Z. .
JOURNAL OF COATINGS TECHNOLOGY AND RESEARCH, 2018, 15 (01) :223-230