Adaptive Approaches to Basic Mobile Robot Tasks

摘要

The present thesis addresses the research field of adaptive behaviour concerning mobile robots. The world as “seen” by the robot is previously unknown and has to be explored by manoeuvring according to certain optimization criteria. This assumption enhances the fitness of a mobile robot for a range of applications beyond rigid installations, demanding normally significant effort, and offering limited ability to adapt to changes in the environment. A central concept emphasized in this thesis is the achieving of competence and fitness through continuous interaction with the robot's world. Lifelong learning is considered, even after achieving a temporally sufficient degree of adaptation and running in parallel to the actual robot's application. The levels of competence are generated bottom up, i.e. upper levels are based on the current robot's experience modelled in lower levels. The terms (the skills are formulated with) employed on higher levels are generated through real world interactions on lower levels. The robotics problems discussed are limited to some basic tasks, which are found to be relevant for most mobile robot applications. These are exploration of unknown environments, stable self-localization with respect to the current world and its internal representation, as well as navigation, target extraction, and target recognition. In order to cope with problems resulting from a lack of proper a-priori knowledge and defined and reliably detectable symbols in unknown and dynamic environments, connectionist 1 methods are employed to a great extend. Realtime constraints are considered at all levels of competence, with the natural exception of global planning. The research field of target extraction and identification with respect to mobile robot constraints leads especially to the discussion of visual search (steering), extraction of geometric primitives even at system startup time, and to the generation of symbols out of subsymbolic processing. These symbols can be reliably recognized and should be suitable for a following symbolic planning level, outside the focus of the present thesis. The presented approach ensures a large degree of adaptability on all levels, not discussed before to this wide extent, or even investigated for the first time regarding some components (e.g. visual search with highly focused devices). The exploration, self-localization, and navigation tasks are attacked by an integral approach allowing the parallel processing of these tasks in a dynamic environment. The stability and reliability of the discussed techniques are proven on the base of realtime and real world experiments with a mobile platform. The high error tolerance and low demands concerning the used sensor devices, as well as the small computation power required, are (currently) unique features of the presented method. 1. also denoted as “artificial neural networks” or even more general as “subsymbolic methods” Special thanks: Jorg Bruske, Thomas Edlinger, Cornelia Fischer, Walter J. Freeman, Bernd Fritzke, Hsin-Chia Fu, Wolfgang Grund, Nikola K. Kasabov, Geoffrey Kennedy, Edmund Klapper, Herman Keuchel, Pascal Lefevre, Andras Lorincz, Ralf Moller, Ulrich Nehmzow, Klaus-Jurgen Nissen, Franz-Josef Nolke, Rolf Peukert, Andreas Prus, Ewald von Puttkamer, Michael M. Richter, Philip J. Sallis, Matthias Schafer, Manfred Sigmann, Rainer Smidt, Martin Stecher, Stefan Stranz, Jun Tani, Richard Tebuckhorst, Jorg Trundt, Martin Wagner, Gerhard Weis, Christopher Wetzler. Very special thanks to my wife Sabine Friedrich, making this thesis possible.