Methods for Visually Guided Robotic Systems Matching, Tracking and Servoing

Detta är en avhandling från Linköping : Linköping University Electronic Press

Sammanfattning: This thesis deals with three topics; Bayesian tracking, shape matching and visual servoing. These topics are bound together by the goal of visual control of robotic systems. The work leading to this thesis was conducted within two European projects, COSPAL and DIPLECS, both with the stated goal of developing artificial cognitive systems. Thus, the ultimate goal of my research is to contribute to the development of artificial cognitive systems.The contribution to the field of Bayesian tracking is in the form of a framework called Channel Based Tracking (CBT). CBT has been proven to perform competitively with particle filter based approaches but with the added advantage of not having to specify the observation or system models. CBT uses channel representation and correspondence free learning in order to acquire the observation and system models from unordered sets of observations and states. We demonstrate how this has been used for tracking cars in the presence of clutter and noise.The shape matching part of this thesis presents a new way to match Fourier Descriptors (FDs). We show that it is possible to take rotation and index shift into account while matching FDs without explicitly de-rotate the contours or neglecting the phase. We also propose to use FDs for matching locally extracted shapes in contrast to the traditional way of using FDs to match the global outline of an object. We have in this context evaluated our matching scheme against the popular Affine Invariant FDs and shown that our method is clearly superior.In the visual servoing part we present a visual servoing method that is based on an action precedes perception approach. By applying random action with a system, e.g. a robotic arm, it is possible to learn a mapping between action space and percept space. In experiments we show that it is possible to achieve high precision positioning of a robotic arm without knowing beforehand how the robotic arm looks like or how it is controlled.