A New Technique to Improve Estimation of Position for Serial Robots

Abstract

This paper presents a new method to improve the estimation of the positions for serial robots using power-activated feed-forward neural network. In the paper, a six-input three-output neural network is created with robot joint angle sine values as inputs and positions in the world frame as outputs. The neuron is activated with an orthogonal polynomial sequence,and the neural weights can be calculated directly without involving iterative and convergent problem. It is found that, the RMS error is less than 0.25 mm for the whole work space. And the absolute and relative errors of this method are smaller than those of built in kinematics model and the traditional back propagation (BP) neural network method. Experimental results show that the proposed method can effectively predict the positioning of the given joint angles.

Analysis of the optimal orientation of robot gripper for an improved capability assembly process

“…Gatti and Danieli [30] proposed an effective and ready-to-apply computational approach to estimate geometric error parameters. A new technique to improve the estimation of position for serial robots is proposed by Wang et al [31]. It was found that the RMS error is less than 0.25 mm for the whole workspace.…”