In the paper hardware implemented artificial neural network used for trajectory control of a mobile robot is presented. As a controller RBF (Radial Basis Function) type hardware implemented artificial neural network has been used. There are presented the structure of the neural network implemented on an FPGA system, the main modules of the system, the on chip training.As a robot, a mobile robot with three wheels has been chosen. The left and right side wheels are driven by DC motors and the third one is a free-wheel. The robot position is determined from the image sequence captured by a camera. Finally, measurement results based on the robot model and on the real system will be presented.
This paper demonstrates, that input patterns can be encoded in the synaptic weights by local Hebbian delay-learning of spiking neurons (SN), where, after learning, the firing time of an output neuron reflects the distance of the evaluated pattern to its learned input pattern thus realizing a kind of RBF behavior. Furthermore, the paper shows, that temporal spike-time coding and Hebbian learning is a viable means for unsupervised computation in a network of SNs, as the network is capable of clustering realistic data. Then, two versions — with and without embedded micro-controllers — of a SNN are implemented for the aforementioned task.