ニューラルネットによるロボットインピーダンスのオンライン学習 On-line Learning of Robot Arm Impedance Using Neural Networks
Impedance control is one of the most effective control methods for a manipulator in contact with its environments. The characteristics of force and motion control, however, are determined by the impedance parameters of the end-effector of the manipulator which must be designed according to the given task. In this paper, we propose a method to regulate impedance parameters of the manipulator's end-effector while identifying the characteristics of the environments using neural networks through on-line learning. Four kinds of neural networks are used: three for the position, velocity and force control of the end-effector, and one for the identification of environments. First, the neural networks for the position and velocity control are trained during free movements. Then, the neural networks for the force control and the identification of environments are trained during contact movements. Computer simulations show that the method can regulate stiffness, viscosity and inertia parameters of the end-effector and identify the unknown property of the environments through on-line learning.
日本ロボット学会誌 17(2), 234-241, 1999-03-15
The Robotics Society of Japan