Learning Algorithm Predicts Passive Joint Positioning for 3R Under-actuated Robot

Abstract

This paper devoted for the implementation of a learning algorithm, utilized Artificial Neural Network (ANN), to predict the passive joint angular positioning for 3R under-actuated serial robot. Under-actuated system has less number of actuators than the degrees of freedom; therefore, the estimating or modelling of its behaviour is difficult with many uncertainties. Thus, to overcome the disadvantages of several methods reported in literatures. A specific ANN model has been designed and trained to learn a desired set of joint angular positions for the passive joint from a given set of input torques and angular positions for the active joints over a certain period of time. ANN proposes from being free model technique. Consequently, data from sensors fixed on each joints were collected experimentally and provided for the developed ANN model. The learning algorithm can directly determine the position of its passive joint, and can, therefore, completely eliminate the need for any system modelling. Hence, this method could be generalized for the prediction of under-actuated systems behaviour. Results show a successful implementation of the learning algorithm in predicting the behaviour for 3R underactuated robot.