文献类型：期刊文献

英文题名：Double global optimum genetic algorithm-particle swarm optimization-based welding robot path planning

作者：Wang, Xuewu[1];Shi, Yingpan[1];Ding, Dongyan[2];Gu, Xingsheng[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Sch Mat Sci & Engn, Inst Microelect Mat & Technol, Shanghai 200030, Peoples R China

年份：2016

卷号：48

期号：2

起止页码：299

外文期刊名：ENGINEERING OPTIMIZATION

收录：;EI(收录号：20150700506947);WOS:【SCI-EXPANDED(收录号:WOS:000365645900007)】；

基金：The authors appreciate the support of Shanghai Natural Science Foundation [14ZR1409900], National Major Scientific Instruments Equipment Development Project [2012YQ15000105] and Key Program for the Fundamental Research of Shanghai Committee of Science and Technology [12JC1403400].

语种：英文

外文关键词：welding robot; path planning; genetic algorithm; particle swarm optimization; double global optimum

摘要：Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm-particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.