Irwersp-kinematir:s compv : talion 1J.sing an artificial neural network that learns the inverse kinematics of a robot arm has been employed by many resea7'Chers. However, the inverse kinemahcs system of typical robot arms with joint limits is 11 multicvalued and dis continuous function. Since it is difficult f01' a well known multi-layer neural network to approximate such a junction, a correct inverse kinematics model mnnot be obtained by usmg a smgle neural network. In order to overcome the discontinuity of the inverse kinematics function, we proposed a novel modular neural network system that consists of a numbe.r of expert neural net works. Each expert approximates the continuous part of the inverse kinematics fv,nction. The proposed sys tem uses the forward kinematics model for selection of experts. Vl/hen the number of the experts increases, the computation time for ca.lculating the inverse kinemat ics solution also increases without using the parallel computing system. In order to reduce the computation time; we propose a novel expert selection by using the pe rformance prediction networks which directly calcu late the performances of the eJ:perts. The task of calculating all of the joint angles that would result in a specific pm:ition/orientation of an end-effector of a robot arm is called the inverse kine matics problem. An inverse kinematics solver using an [10] . However, the method is a purely off line learning method and is not applicable for on-line learning, i.e. simultaneous or alternate execution of the robot control and the inverse model learning. Fur thermore, the method is not goal-directed, There is no direct way to find an joint angle vector that corre-1006
A new expert neural network based approach has been developed, where each of the experts approximates the continuous part of inverse kinematics function REF .
Inverse kinematics learning by modular architecture neural networks with performance prediction networks
Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164)
Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164)
Computer Science