Paper Info
Title
Controlling a four degree of freedom arm in 3D using the XCSF learning classifier system
Abstract
This paper shows for the first time that a Learning Classifier System, namely XCSF, can learn to control a realistic arm model with four degrees of freedom in a three-dimensional workspace. XCSF learns a locally linear approximation of the Jacobian of the arm kinematics, that is, it learns linear predictions of hand location changes given joint angle changes, where the predictions are conditioned on current joint angles. To control the arm, the linear mappings are inverted--deriving appropriate motor commands given desired hand movement directions. Due to the locally linear model, the inversely desired joint angle changes can be easily derived, while effectively resolving kinematic redundancies on the fly. Adaptive PD controllers are used to finally translate the desired joint angle changes into appropriate motor commands. This paper shows that XCSF scales to three dimensional workspaces. It reliably learns to control a four degree of freedom arm in a three dimensional work space accurately and effectively while flexibly incorporating additional task constraints.
Year
DOI
Venue
2009
10.1007/978-3-642-04617-9_25
KI
Keywords
Field
DocType
arm kinematics,current joint angle,linear prediction,linear approximation,freedom arm,joint angle change,classifier system,realistic arm model,linear model,linear mapping,xcsf scale,degree of freedom,dynamic systems,dynamic system,three dimensional,learning classifier system
Linear approximation,Degrees of freedom (statistics),Kinematics,Jacobian matrix and determinant,Control theory,Linear model,Workspace,Engineering,Dynamical system,Learning classifier system
Conference
Volume
ISSN
ISBN
5803
0302-9743
3-642-04616-9
Citations 
PageRank 
References 
5
0.42
13
Authors
3
Name
Order
Citations
PageRank
Patrick O. Stalph1745.95
Martin V. Butz2106585.21
Gerulf K. M. Pedersen381.28