期刊
ENGINEERING OPTIMIZATION
卷 48, 期 2, 页码 299-316出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/0305215X.2015.1005084
关键词
welding robot; path planning; genetic algorithm; particle swarm optimization; double global optimum
资金
- Shanghai Natural Science Foundation [14ZR1409900]
- National Major Scientific Instruments Equipment Development Project [2012YQ15000105]
- Key Program for the Fundamental Research of Shanghai Committee of Science and Technology [12JC1403400]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据