A spiking network for object and ego-motion detection in roving robots

This paper proposes a neural-based model for ego-motion detection and compensation in applications related to bionic antennae. Touch or near range sensors are used on moving platforms to detect the presence of surrounding objects: additional feature could also be extracted, like distance, material characteristics and the shape of objects. The processing and control architecture is based on spiking neurons and in particular a series of resonate and fire neurons has been used to extract important features from the sensory data. The use of spiking resonant neuron arrays is treated as a general methodology for bio-inspired feature clustering. The use of the resonance allows to emphasize particular signal contents which depend on ego-motion: the detection and compensation of such components hidden in the acquired signal is a real added value towards the construction of robust and bio-inspired methodologies for simple and efficient forward models. The methodology is applied to a bionic antenna which was mounted on a roving robot. This could be very useful in different scenarios enriching the multimodal sensory system usually available on a navigation platform. Simulation results and experiments performed on a roving platform are reported.