Neural Network Learning of Robot Arm Impedance in Operational Space

Tsuji Toshio

Ito Koji

Morasso Pietro

IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics,

lmpedance control is one of the most effective controlmethods for the manipulators in contact with their environments.The characteristics of force and motion control, however, isdetermined by a desired impedance parameter of a manipulator'send-effector that should be carefully designed according to agiven task and an environment. The present paper proposesa new method to regulate the impedance parameter of theend-effector through learning of neural networks. Three kindsof the feed-forward networks are prepared corresponding toposition, velocity and force control loops of the end-effector beforelearning. First, the neural networks for position and velocitycontrol are trained using iterative learning of the manipulatorduring free movements. Then, the neural network for forcecontrol is trained for contact movements. During learning ofcontact movements, a virtual trajectory is also modified to reducecontrol error. The method can regulate not only stiffness andviscosity but also inertia and virtual trajectory of the end-effector.Computer simulations show that a smooth transition from freeto contact movements can be realized by regulating impedanceparameters before a contact.

機械工学 [ 530 ]

英語

IEEE

Copyright (c) 1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

[ISSN] 1083-4419

[DOI] http://dx.doi.org/10.1109/3477.485879 ～の異版である