Latest documents http://publikationen.ub.uni-frankfurt.de/index/index/ Wed, 15 Sep 2010 11:24:26 +0200 Wed, 15 Sep 2010 11:24:26 +0200 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/8006 A new programming paradigm for the control of a robot manipulator by learning the mapping between the Cartesian space and the joint space (inverse Kinematic) is discussed. It is based on a Neural Network model of optimal mapping between two high-dimensional spaces by Kohonen. This paper describes the approach and presents the optimal mapping, based on the principle of maximal information gain. It is shown that Kohonens mapping in the 2-dimensional case is optimal in this sense. Furthermore, the principal control error made by the learned mapping is evaluated for the example of the commonly used PUMA robot, the trade-off between storage resources and positional error is discussed and an optimal position encoding resolution is proposed. Rüdiger W. Brause report http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/8006 Wed, 15 Sep 2010 11:24:26 +0200