PUB - Publikationen an der Universität Bielefeld

In this paper, we present the application of artificial neural networks to the control of a mobile, autonomous robot, which is acting in a totally unknown and - most importantly - dynamically changing environment. In particular, the employment of interacting 'simple', i.e. hand-designed, neural networks for navigation purposes is investigated as well as a variation of self-organizing maps for adaptive sensor calibration. We insofar take a pragmatic point of view as the minimal condition imposed on the developed algorithms is that they do well on a real system acting in a real environment. Hence, the design of all of the implemented neural networks is clearly motivated by their applicability. In this context, special considerations are dedicated to ensure robustness, real-time capability and memory resourcefulness. In order to practically demonstrate the obtained results, the minirobot Khepera is utilized as an experimentatory platform, which is - due to its small size - a versatile tool for scientific investigation.