Neural Network Schemes in Cartesian Space Control of Robot Manipulators

In this paper we are studying the Cartesian space robot manipulator control problem by using Neural Networks (NN). Although NN compensation for model uncertainties has been traditionally carried out by modifying the joint torque/force of the robot, it is also possible to achieve the same objective by using the NN to modify other quantities of the controller. We present and evaluate four different NN controller designs to achieve disturbance rejection for an uncertain system. The design perspectives are dependent on the compensated position by NN. There are four quantities that can be compensated: torque , force F, control input U and the input trajectory Xd. By defining a unified training signal all NN control schemes have the same goal of minimizing the same objective functions. We compare the four schemes in respect to their control performance and the efficiency of the NN designs, which is demonstrated via simulations

Topological and Geometrical Reconstruction of Complex Objects on Irregular Isothetic Grids

Abstract. In this paper, we address the problem of vectorization of binary images on irregular isothetic grids. The representation of graphical elements by lines is common in document analysis, where images are digitized on (sometimes very-large scale) regular grids. Regardless of final application, we propose to first describe the topology of an irregular two-dimensional object with its associated Reeb graph, and we recode it with simple irregular discrete arcs. The second phase of our algorithm consists of a polygonal reconstruction of this object, with discrete lines through the elementary arcs computed in the previous stage. We also illustrate the robustness of our method, and discuss applications and improvements.