摘要
点焊机器人在汽车白车身焊接中的应用大大提高了企业的生产效率,本文从焊接路径长度和能量两方面进行焊接机器人多目标路径规划.为了很好地解决这个问题,本文对一种新型多目标粒子群算法(三态协调搜索多目标粒子群优化算法)进行改进,得到适合于求解离散多目标优化问题的离散化三态协调搜索多目标粒子群算法(DTC-MOPSO).通过和两个经典的优化算法比较,DTC-MOPSO算法在分散性和收敛性方面都有很好的优化性能.最后运用Matlab机器人工具箱对机器人的运动学、逆运动学以及逆动力学进行分析以求解机器人的路径长度和能耗,并将改进的算法应用于焊接机器人路径规划中,结果显示规划后的路径明显优于另外两种算法.
The application of spot welding robots to automobile body-in-white welding has greatly improved the production efficiency of automobiles. Multi-objective welding robot path planning focusing on path length and energyoptimization is solved. To solve the problem, after a new multi-objective particle swarm optimization algorithm （multi- objective partical swarm optimization algorithm based on three status coordinating searching, TC-MOPSO） is improved, a discrete multi-objective particle swarm optimization algorithm based on three status coordinating searching （DTC-MOPSO） is presented to solve the discrete multi-objective optimization problem. Compared with two classical multi-objeetive optimization algorithms, high competition in terms of convergence and diversity metrics of the DTC-MOPSO algorithm is proved. In addition, MATLAB toolbox robotics is used to analyze a robot＇s kinematics, inverse kinematics, and inverse dynamics to obtain the path length and energy consumption. The improved algorithm is used to optimize welding robot path planning, and the result is obviously superior to the other algorithms.
出处
《信息与控制》
CSCD
北大核心
2016年第6期713-721,共9页
Information and Control
基金
上海市自然科学基金资助项目(14ZR1409900)
上海市科委基础研究重点资助项目(12JC1403400)
关键词
路径规划
焊接机器人
多目标
粒子群优化算法
path planning
welding robot
multi-objective
particle swarm optimization （PSO） algorithm
