A new approach to improve the parameter estimation accuracy in robotic manipulators using a multi-objective output error identification technique
A new approach to improve the parameter estimation accuracy in robotic manipulators using a multi-objective output error identification technique
复制标题
使用多目标输出误差识别技术提高机器人操纵器参数估计精度的新方法
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
C. J. Taylor
中科院分区：
文献类型：
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作者：
C. West;A. Montazeri;S. Monk;Dobromil Duda;C. J. Taylor
本文背后的研究主要涉及用于核退役的移动机器人的开发。正在研究的机器人平台具有双重、七功能、液压驱动的机械手，作者正在为其开发基于视觉的辅助遥操作界面，用于常见的退役任务，如管道切割。然而，为了提高安全性、任务执行速度和操作员训练时间，对机械臂的非线性动力学控制提出了更高的要求。因此，本文的重点是一个相关的动态模型，并解决高度非凸和非线性系统的参数估计这一具有挑战性的一般任务。基于多对象化的思想，提出了一种新的机械手基本参数估计方法。这里，将单目标输出误差辨识问题转化为多目标优化问题。使用具有非支配排序的多目标遗传算法来解决这一问题。以核退役机器人为例的数值和实验结果表明，该方法在输出误差指标和参数估计精度方面均优于已有研究的单目标辨识问题。
The research behind this article primarily concerns the development of mobile robots for nuclear decommissioning. The robotic platform under study has dual, seven-function, hydraulically actuated manipulators, for which the authors are developing a vision based, assisted teleoperation interface for common decommissioning tasks such as pipe cutting. However, to improve safety, task execution speed and operator training-time, high performance control of the nonlinear manipulator dynamics is required. Hence, the present article focuses on an associated dynamic model, and addresses the challenging generic task of parameter estimation for a highly non-convex and nonlinear system. A novel approach for estimation of the fundamental parameters of the manipulator, based on the idea of multi-objectivization, is proposed. Here, a single objective output error identification problem is converted into a multi-objective optimization problem. This is solved using a multi-objective genetic algorithm with non-dominated sorting. Numerical and experimental results using the nuclear decommissioning robot, show that the performance of the proposed approach, in terms of both the output error index and the accuracy of the estimated parameters, is superior to the previously studied single-objective identification problem.