Paper available:Inverse Kinematics via Network Inversions

[Bao-Liang Lu]

The following paper, to appear in Proc. of IEEE ICNN'95, Perth, Australia, 
is available via FTP.

FTP-host:ftp.bmc.riken.go.jp

FTP-file:pub/publish/Lu/lu-ieee-icnn95.ps.Z

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TITLE: Regularization of Inverse Kinematics for Redundant Manipulators
       Using Neural Network Inversions

AUTHORS: 
       Bao-Liang Lu (1)
       Koji Ito (1,2)

ORGANISATIONS:
       (1) The Institute of Physical and Chemical Research
       (2) Toyohashi University of Technology

ABSTRACT:
This paper presents a new approach to regularizing the inverse 
kinematics problem for redundant manipulators using neural network 
inversions. This approach is a four-phase procedure. In the first 
phase, the configuration space and associated workspace are 
partitioned into a set of regions.  In the second phase, a set of 
modular neural networks is trained on associated training data sets 
sampled over these regions to learn the forward kinematic function. 
In the third phase, the multiple inverse kinematic solutions 
for a desired end-effector position are obtained by inverting the 
corresponding modular neural networks. In the fourth phase, an 
``optimal" inverse kinematic solution is selected from the multiple 
solutions according to a given criterion. This approach has an 
important feature in comparison with existing methods, that is,  
both the inverse kinematic solutions located in the multiple solution 
branches and the ones that belong to the same solution branch can be 
found. As a result, better control of the manipulator using the optimum 
solution than that using an ordinary solution can be achieved. 
This approach is illustrated with a three-joint planar arm.

(6 pages. No hard copies available.)

Bao-Liang Lu
---------------------------------------------
Bio-Mimetic Control Research Center,
The Institute of Physical and Chemical Research (RIKEN)
3-8-31 Rokuban, Atsuta-ku, Nagoya 456, Japan
Phone: +81-52-654-9137
Fax: +81-52-654-9138
Email: lbl at nagoya.bmc.riken.go.jp