苏俊, 熊瑞平, 温记明, 李云秋, 谭平. 基于改进粒子群算法的六自由度工业机器人轨迹规划[J]. 制造技术与机床, 2022, (10): 38-45. DOI: 10.19287/j.mtmt.1005-2402.2022.10.005
引用本文: 苏俊, 熊瑞平, 温记明, 李云秋, 谭平. 基于改进粒子群算法的六自由度工业机器人轨迹规划[J]. 制造技术与机床, 2022, (10): 38-45. DOI: 10.19287/j.mtmt.1005-2402.2022.10.005
SU Jun, XIONG Ruiping, WEN Jiming, LI Yunqiu, TAN Ping. Trajectory planning of 6-DOF industrial robot based on improved particle swarm optimization algorithm[J]. Manufacturing Technology & Machine Tool, 2022, (10): 38-45. DOI: 10.19287/j.mtmt.1005-2402.2022.10.005
Citation: SU Jun, XIONG Ruiping, WEN Jiming, LI Yunqiu, TAN Ping. Trajectory planning of 6-DOF industrial robot based on improved particle swarm optimization algorithm[J]. Manufacturing Technology & Machine Tool, 2022, (10): 38-45. DOI: 10.19287/j.mtmt.1005-2402.2022.10.005

基于改进粒子群算法的六自由度工业机器人轨迹规划

Trajectory planning of 6-DOF industrial robot based on improved particle swarm optimization algorithm

  • 摘要: 针对工件打磨工况,设计建立用于打磨的机器人运动学模型,规划机器人运动学轨迹,通过改进粒子群算法优化轨迹曲线,减少机器人运行时间,提高机器人工作效率。首先建立打磨工业机器人空间运动学模型,计算目标点从笛卡尔空间转换到关节空间的逆解,在关节空间中利用“三次-五次-三次”三段多项式曲线对所求逆解进行轨迹规划,以轨迹运动时间最短作为优化目标。利用融合免疫操作的改进粒子群算法对轨迹曲线进行优化,将改进算法的优化结果与传统粒子群算法进行对比;改进后的新算法改善了粒子群算法易陷入局部最优的问题,适应度结果更好,算法效果更佳。

     

    Abstract: For dealing with grinding, the robot kinematics model is designed and established, the robot trajectory is planned, and the trajectory curve is optimized by an improving particle swarm optimization algorithm to reduce the running time and improve the working efficiency of the robot. Firstly, this paper establishes the spatial kinematics model of the industrial robot for polishing, calculates the joint space inverse solution position corresponding to the target pose points in Cartesian space, uses the 3-5-3 three segment polynomial curve to plan the trajectory of the inverse solution points in joint space, and takes the shortest trajectory motion time as the optimization goal. By using the improved particle swarm optimization algorithm with immune operation to optimize the trajectory curve, the optimization results of the improved algorithm are compared with the traditional particle swarm optimization algorithm, and it is found that the improved new algorithm can better avoid falling into local optimization, the fitness result is better, and the algorithm effect is better.

     

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