Control of roving robot using chaotic dynamics in a quasi-layered recurrent neural network for sensing and driving

We propose a quasi-layered recurrent neural network consisting of sensing neurons (upper layer) and driving neurons (lower layer). In both layers, chaotic dynamics are used where, in sensing neurons, sensitive response to external input is utilized, whereas in driving neurons, complex dynamics is utilized to generate complex motions. These two properties are applied to solving two-dimensional mazes by computer simulations and hardware implementation into a roving robot is shown.