Artificial neural networks for autonomous robot control: reflective navigation and adaptive sensor calibration

The authors 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 take a pragmatic point of view as the minimal condition imposed on the developed algorithms: 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 mini-robot Khepera is utilized as an experimentational platform, which is (due to its small size), a versatile tool for scientific investigation