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Insects use a relatively simple visual system to navigate and avoid obstacles. In particular, they use self motion to determine the range to objects by the angular velocities of the contrasts across the retina array. Adopting principles learnt from studying insect behaviour and neurophysiology we have modelled aspects of the motion detection mechanism of an insect visual system into a means of categorising edges and computing their motion and thus determining range. Copying insect motion perception, a camera is scanned across a scene and a temporal sequence of line images captured. The 8-bit grey scale image is immediately reduced to a 1og23 = 1.6 bit image by saturating the contrast. Behind each pixel, one state is formed by increasing intensity, one by decreasing intensity and a third is indeterminate.Pairs of receptors at two consecutive times, forming a 2 by 2 template in space-time, give a finite number of combinations, of which it is found that only a small subset provide useful motion information. Combinations of selected templates results in a distribution of template responses that is amenable to analysis by the Hough transform. Running the model on real scenes reveals the value of lateral inhibition as well as insights into the effect of different edge types and the use of parallax. The model suggests a possible new neurophysiological construction that can be copied in hardware to provide a fast means of inferring 3-d structure in a scene where the observer is moving with a known velocity.

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Abstract.A scheme is presented that uses the self-motion of a robot, equipped with a single visual sensor, to navigate in a safe manner. The motion strategy used is modelled on the motion of insects that effectively have a single eye and must move in order to determine range. The essence of the strategy is to employ a zigzag motion in order to (a) estimate the range to objects and (b) know the safe distance of travel in the present direction. An example is presented of a laboratory robot moving in a cluttered environment. The results show that this motion strategy can be successfully employed in an autonomous robot to avoid collisions.

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