Presents methods and experiments of adaptive posture control for a four legged walking machine. Starting from the analysis of the implemented movement behaviour of BISAM we identify adequate tasks for adaptive control components and present adaptive posture control mechanisms for statically stable and dynamically stable movements. The reflex-based posture control is implemented via fuzzy control and reinforcement learning. The integration of the posture control in the control architecture is also described.

Presents an adaptive control architecture for the four legged walking machine BISAM. This architecture uses coupled neuro-oscillators as representation of periodic behaviours on different control levels such as joint movement, leg control and leg coordination. Coupled neuro-oscillators together with adaptive sensor based reflexes provide a robust and efficient representation for quadrupedal locomotion and support the use of online learning approaches to realize adaptation and optimization of locomotion behaviours.

Online learning of complex control behaviour of autonomous mobile robots is one of the current research topics. In this article a hybrid learning architecture based on self-organizing neural networks for online adaptivity is presented. The hybrid concept integrates different learning methods and task-oriented representations as well as available domain knowledge. The proposed concept is used for reinforcement learning of control strategies on different control levels on a walking machine.

In this paper a concept for a wheeled, multijoint robot able to operate in sewage systems is presented. The robot should work in an autonomous way. This concerns power supply, control and information processing. The cable-free navigation and the multijoint redundant construction of the robot enable higher mobility in sewage systems. In contrast to present systems, the robot should be able to avoid and overcome small obstacles, e.g socket displacements, holes or sediments, and to pass junctions and curves. In the following the results of a feasibility study are described in which the state of the art in sewer inspection robot as well as first experiments for the development of such a system is shown.

As presented in many papers neural networks are adequate for special control tasks because of their real-time processing capability, their fault-tolerance and their high adaptivity. The following paper aims to demonstrate how to build up a neural control architecture, consisting only of control algorithms based on neural networks. A further aspect is how to teach such control algorithms. As a testbed, a six-legged walking machine is selected. Due to the use of neural control architecture, the requirements to the hardware concept of the walking machine is described.>