基于粒子群算法的机器人运动学参数标定研究

Research on Robot Kinematics Parameter Calibration Based on Particle Swarm Optimization

为了提高机器人末端绝对定位精度,提出了一种基于粒子群算法的机器人运动学几何参数标定方法,对工业机器人的参数误差进行辨识和补偿。首先,为了避免机械臂相邻两个轴平行时会出现奇异性的情况,采用了MDH参数法建立误差模型;其次,为了把测量数据定义在同一坐标轴上,使用结合模型精度补偿的机器人方位与手眼关系同步标定的坐标转换方法;然后用粒子群算法辨识出机器人的模型几何参数误差。通过对ABB工业机器人的仿真,实验计算和标定,机器人的平均绝对定位精度提高了66.9%。结果表明,文章中的标定算法可以有效地辨识出机器人的模型参数误差,对机器人的模型参数进行补偿后能有效提高绝对定位精度。

In order to improve the absolute positioning accuracy of robot end,a calibration method of robot kinematic geometric parameters based on particle swarm optimization(PSO)was proposed to identify and compensate the parameter errors of industrial robots.First,in order to avoid the singularity when two adjacent axes of the manipulator are parallel,MDH parameter method was adopted to establish the error model.Secondly,in order to define the measurement data on the same coordinate axis,the coordinate conversion method of synchronous calibration of robot orientation and hand-eye relationship combined with model precision compensation was used.Then particle swarm optimization(PSO)algorithm was used to identify the model geometric parameter error.Through the simulation,experimental calculation and calibration of ABB industrial robots,the average absolute positioning accuracy of robots was improved by 66.9%.The results showed that the calibration algorithm can effectively identify the model parameter errors of the robot and improve the absolute positioning accuracy after compensating the model parameters of the robot.