鲁棒的视觉机械臂联合建模优化方法

Robust joint modeling and optimization method for visual manipulators

针对视觉机械臂在复杂系统环境下整体精度不高、不易部署、校准成本高的问题,提出一种鲁棒的视觉机械臂联合建模优化方法。首先,对视觉机械臂的各个子系统模型进行集成,在机械臂的工作空间随机采集伺服电机转角、机械臂末端坐标等数据。其次,提出一种具有分层优化机制的自适应多精英引导复合差分进化算法(AMECoDEs-LO),使用参数辨识的方法同时优化联合系统参数。AMECoDEs-LO对种群中阶段性的数据进行主成分分析(PCA),以参数降维的思想实现对收敛精度和速度的隐式引导。实验结果表明,在AMECoDEs-LO和联合系统模型的作用下,视觉机械臂在校准过程中不需要额外的仪器,部署速度快,最终精度相较于传统方法提高60%;在机械臂连杆受损、伺服电机精度降低、相机定位噪声增大的情况下,系统仍然保持较高精度,验证了所提方法的鲁棒性。

To address the problems of low accuracy,difficult deployment and high calibration cost of visual manipulator in complex system environments,a robust joint modelling and optimization method for visual manipulators was proposed.Firstly,the subsystem models of the visual manipulator were integrated together,and the sample data such as servo motor rotation angles and manipulator end-effector coordinates were collected randomly in the workspace of the manipulator.Then,an Adaptive Multiple-Elites-guided Composite Differential Evolution algorithm with shift mechanism and Layered Optimization mechanism(AMECoDEs-LO)was proposed.Simultaneous optimization of the joint system parameters was completed by using the method of parameter identification.Principal Component Analysis(PCA)was performed by AMECoDEs-LO on stage data in the population,and with the idea of parameter dimensionality reduction,an implicit guidance for convergence accuracy and speed was realized.Experimental results show that under the cooperation of AMECoDEs-LO and the joint system model,the visual manipulator does not require additional instruments during calibration,achieving fast deployment and a 60%improvement in average accuracy compared to the conventional method.In the cases of broken manipulator linkages,reduced servo motor accuracy and increased camera positioning noise,the system still maintains high accuracy,which verifies the robustness of the proposed method.