改进灰狼算法在焊接机器人路径规划的应用

Application of Improved Gray Wolf Algorithm in Path Planning of Welding Robot

为优化点焊机器人的焊接路径,提高工作效率,提出一种改进的灰狼优化算法(LOGWO)。在灰狼算法(GWO)原理基础上,引入一种可调节的对数变化收敛因子,实行保留有效个体的余弦变异策略,提高算法的寻优能力。通过对4种TSP算例的计算,体现LOGWO的综合性能。设计LOGWO的焊接路径规划流程,基于Matlab软件实现LOGWO对某车门焊点的焊接路径规划仿真,验证LOGWO在焊接机器人路径规划中的可行性。将LOGWO与GWO的寻优过程进行对比,试验结果表明,LOGWO的收敛速度更快、寻优精度更高、稳定性更强,显著缩短了焊接距离,提高了生产效率。

In order to optimize the welding path of spot welding robot and improve work efficiency,an improved gray wolf optimization algorithm(LOGWO)was proposed.On the basis of the principle of Grey Wolf Algorithm(GWO),an adjustable logarithmic change convergence factor and a cosine mutation strategy with valid individuals retained are introduced to improve the optimization ability of the algorithm.Through the calculation of four TSP examples,the comprehensive performance of LOGWO is verified.The welding path planning process of LOGWO is designed.Based on Matlab software,the welding path planning simulation of LOGWO for a door welding point is realized,which verifies the feasibility of LOGWO in the path planning of welding robot.Comparing the optimization process of LOGWO with that of GWO,the test results show that LOGWO has faster convergence speed,higher optimization accuracy and stronger stability,which significantly shortens the welding distance and improves the production efficiency.