针对点焊机器人在C空间中的轨迹规划问题,提出一种基于蚁群算法的智能轨迹规划参数自适应蚁群算法(pa-rameter adaptive ant colony algorithm,PAACA),以期改进蚁群算法易早熟、收敛速度慢的问题,在PAACA中构建一种复合线性适应度函数,此函数可以智能控制算法中信息素的作用强度,从而提高寻优能力.通过MATLAB进行仿真测试证明了PAACA的优越性,并将智能轨迹规划应用在工业机器人实体中,用KEBA控制器进行3D建模仿真示教和机器人本体运行,验证了C空间中的智能轨迹规划PAACA具有很强的实际应用价值.
An Intelligent Trajectory Planning Algorithm for Spot Welding Robots in C-space
This paper mainly focuses on the trajectory planning problem of spot welding robots in C-space, and proposes an intelligent trajectory planning parameter adaptive ant colony algorithm (PAACA) based on ant colony algorithm so as to improve the problem of premature convergence and slow convergence speed of ant colony algorithm. A composite linear fitness function is constructed in PAACA, which can intelligently control the intensity of pheromones in the algorithm, thereby improving optimization ability. The superiority of PAACA was demonstrated through simulation testing using MATLAB, and intelligent trajectory planning was applied to industrial robot entities. The KEBA controller was used for 3D modeling simulation teaching and robot operation, verifying that PAACA has strong practical application value in C-space.
spot welding robotant colony algorithmintelligent trajectory planningrobot control system
万方数据
维普
