Using higher order synapses and nodes to improve sensing capabilities of mobile robots.
Richard José Duro, José Santos Reyes, Jose Antonio Becerra, Francisco Bellas, José L. Crespo · 2000
In this paper we present three types of higher order artificial neural networks that may be included in heterogeneous ANN architectures to improve the perceptual performance of mobile robots. Two of the networks are based on synaptic processing, with the advantage that this type of processing works with the raw data and not an average, as is the case of nodes. The first one of the structures is designed for handling temporal relations using synaptic delays. The second one, through gaussian functions in the synapses, endows the networks with the capacity of recognizing particular objects in images independently of the background. By integrating these gaussian synapse networks in a global visual architecture, this detection becomes independent of position, orientation and scale. Finally, the third network presented is based on the use of persistence by means of the implementation of habituation neurons as input nodes of networks.