Choosing measurement poses for robot calibration with the local convergence method and tabu search
Choosing measurement poses for robot calibration with the local convergence method and tabu search
复制标题
DOI：
10.1177/0278364905053185
复制
发表时间：
2005-06-01
影响因子：
9.2
通讯作者：
Madeline, B
中科院分区：
文献类型：
--
作者：
Daney, D;Papegay, Y;Madeline, B
机器人标定对传感器噪声的鲁棒性对采集测量数据的机械臂姿态敏感。在本文中，我们提出了一种基于约束优化方法的算法，它允许我们选择一组测量配置。它的工作原理是在工作空间内迭代地选择一个又一个姿势。经过几个步骤，得到了一组构型，该构型使与识别雅可比矩阵相关的可观测性指数最大化。在以前的工作中，该算法对局部极小值很敏感。这就是为什么我们在这里提出元启发式方法来降低我们算法的这种敏感性。最后，通过校准经验的仿真验证表明，通过将与结果相关的噪声除以10-15，使用选定的配置可显着改善运动参数识别。此外，我们还介绍了一种基于视觉的测量装置在并联机器人标定中的应用。
The robustness of robot calibration with respect to sensor noise is sensitive to the manipulator poses used to collect measurement data. In this paper we propose an algorithm based on a constrained optimization method, which allows us to choose a set of measurement configurations. It works by selecting iteratively one pose after another inside the workspace. After a few steps, a set of configurations is obtained, which maximizes an index of observability associated with the identification Jacobian. This algorithm has been shown, in a former work, to be sensitive to local minima. This is why we propose here meta-heuristic methods to decrease this sensibility of our algorithm. Finally, a validation through the simulation of a calibration experience shows that using selected configurations significantly improve the kinematic parameter identification by dividing by 10-15 the noise associated with the results. Also, we present an application to the calibration of a parallel robot with a vision-based measurement device.