Spatial Generalization of Optimal Control for Robot Manipulators
Zhiwei Luo*, Hideyuki Ando**, Shigeyuki Hosoe***, Keiji Watanabe* and Atsuo Kato**
*Bio-Mimetic Control Research Center, RIKEN, Anagahara, Shimoshidami, Moriyama-ku, Nagoya 463-0003, Japan
**Aichi Institute of Technology, 1247 Yakusa Yakusa-cho, Toyoda 470-0392, Japan
***Faculty of Engineering Nagoya University, Furo-cho Chikusa-ku, Nagoya 464-8601, Japan
Received:April 11, 2001Accepted:August 10, 2001Published:October 20, 2001
Keywords:diffusion-based learning, spatial generalization, optimal control, radial basis function network, robot manipulators
A diffusion-based learning approach is presented to generalize optimal control of a robot manipulator over a bounded workspace. By assuming that, for some sets of initial and desired terminal conditions of the robot's positions, we already have the numerical optimal control inputs (for example, by using some complex numerical calculation techniques), this approach first uses radial basis function (RBF) network to parameterize these control inputs by a set of weight matrixes. Diffusion-based algorithm is then applied to generalize these weight matrixes for different terminal position conditions. This approach greatly reduced calculation cost for the robot to find its optimal control. Diffusion-based algorithm is a parallel distributed learning approach, it only requires local interaction between the nodes of a learning network (a lattice) and can be realized by resent IC hardware technology easily. Computer simulations of a 2 D.O.F. planner robot arm show the effectiveness of this approach.
Cite this article as:Z. Luo, H. Ando, S. Hosoe, K. Watanabe, and A. Kato, “Spatial Generalization of Optimal Control for Robot Manipulators,” J. Robot. Mechatron., Vol.13 No.5, pp. 533-539, 2001.Data files:
Copyright© 2001 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.