Jürgen Klahold

Continuous sonar sensing for mobile mini-robots

Ultrasonic sensors enable mobile autonomous systems to obtain information about obstacles in large environments. In the presented work, a 5 cm broad array of three piezo-ceramic ultrasonic transducers is employed for getting two-dimensional impressions of the surroundings. Deviating from the pulse echo measurement techniques used so far the time-continuous transmitting and receiving from modulated pseudo-random sequences are considered. Thus the narrow bandwidth of a piezo-ceramic transducer can be compensated by an increased measuring period. An advanced analysis of the correlated signals allows the rejection of phantoms caused by multiple reflections. Furthermore, a classification of objects such as wall, corner, log or cylinder is possible.

The Dynamical Nightwatch's Problem Solved by the Autonomous Micro-Robot Khepera

In this paper, we present the implementation, both in a simulator and in a real-robot version, of an efficient solution to the so-called dynamical nightwatchs problem on the micro-robot Khepera. The problem consists mainly in exploring a previously unknown environment while detecting, registering and recognizing light sources which may dynamically be turned on and off. At the end of each round a report is requested from the robot. Therein we made use of an agent-based approach and applied a self-organizing feature map in order to refine some of the behaviour generating control-modules.

A Visualization Tool for the Mini-Robot Khepera: Behavior Analysis and Optimization

The design of behavior generating control structures for real robots acting autonomously in a real and changing environment is a complex task. This is in particular true with respect to the debugging process, the documentation of the encountered behavior, its quantitative analysis and the final evaluation. To successfully implement such a behavior, it is vital to couple the synthesis on a simulator and the experiment on a real robot with a thorough analysis. The available simulator tools in general only allow behavioral snapshots and do not provide the option of online interference. In order to cure these shortcomings, a visualization tool for aposteriori graphical analysis of recorded data sets which gives access to all relevant internal states and parameters of the system is presented. The mini-robot Khepera has been chosen as experimentatory platform.

Implementing neural soft- and hardware on the autonomous mini-robot Khepera

The applicability of neural networks to generate complex behaviour on autonomous systems is demonstrated both at soft- and hardware-level. In particular, the emergence of simple behaviors based on the Braitenberg approach, adaptive sensor calibration by self-organizing maps with a comparison between off- and online learning and a visualisation tool for a posteriori analysis are shown. It is also envisaged to present the working of embedded neural hardware as associative memory and self-organizing maps. In this connection, the mini-robot Khepera serves as an exemplary platform.