<title>Collision avoidance in a robot using looming detectors from a locust</title>

Abstract

The superb aerial performance of flying insects is achieved with comparatively simple neural machinery. We have been investigating the pathway in the locust visual system that signals the rapid approach of an object towards the eye. Two identified neurons have been shown to respond selectively to the images of an object approaching the locust's eye. A neural network based on the input organization of these neurons responds directionally when challenged with approaching and receding objects and reveals the importance of a critical race, between excitation passing down the network and inhibition directed laterally, for the rapid build-up of excitation in response to approaching objects. The strongest response is given to an object approaching on a collision course with they eye, when collision is imminent. Like the neurons, the network is tightly tuned to a collision trajectory. We have incorporated this network into the control structure of a small mobile Kephera robot using the IQR 4021 software we developed. The network responds to looming motion and is effective at evoking avoidance maneuvers in the robot, moving at speeds from 1-12.5cm/s. Our aim is to use the circuit as an artificial looming detector for use in moving vehicles.