An artificial neural network strategy for the forward kinematics of robot control. (4th December 2006) - Hasan, Ali T.; Hamouda, A.M.S.; Ismail, N.; Al-Assadi, H.M.A.A.
This paper is devoted to the development and implementation of the neural network technique to solve the forward kinematics problems of robot control, which are mainly singularities and non-linearities. In this paper, a network has been trained to learn the set of end effecter positions X, Y and Z from a given set of joint angle positions for a 6 D.O.F industrial robot. Training data sets were uniformly distributed over a particular region of the robot's working area so that the network can make good generalisation for the intermediate points. Experimental results have shown a good mapping over the working area for the robot. The proposed control technique does not require any prior knowledge of the kinematics model of the system to be controlled; the basic idea of this concept is to use a neural network to learn the characteristics of the robot system rather than having to specify explicit robot system model, which is a significant advantage of using neural network technology. Any modifications in the physical set-up of the system would involve only training the robot in a new path without the need for any major software modification.