Self-Adapting Neural Networks for Mobile Robots
Ralf Salomon · Studies in fuzziness and soft computing · 2003
In the context of research on intelligence, autonomous agents and in particular mobile robots are to behave on their own without any human control. Unfortunately, the real world exhibits plenty of noise, uncertainties, sudden changes, etc, which all imposes significant challenges on the design of appropriate control architectures. This chapter starts off with an existing controller, known as the distributed adaptive control architecture and shows how significant improvements can be achieved by incorporating biological mechanisms, such as proprioception. The resulting controller requires much less preprogrammed design knowledge, exhibits more flexible adaptation capabilities, and is more fault tolerant with respect to environmental changes and sensor failures as its predecessors.