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Proceedings Paper

Application of a real neural collision avoidance system based on the locust to AGV navigation
Author(s): F. Claire Rind; Charles R. Allen
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Paper Abstract

The superb aereal performance of flying insects is achieved with comparatively simple neural machinery. Insects react rapidly to changing visual images. The abilities of insects to perform these computations in real time has already led to a successful prototype autonomous guided vehicle with a sensor and control structure modelled on the fly eye. Increasingly in visual neuroscience it is possible to isolate the critical image cues used by identified neurones to achieve a selective response to a feature or group of features within the changing visual image. In this paper we describe a biological neural network based on the input organization of such an identified motion detecting neurone, which responds selectively to the images of an object approaching on a collision course with the animal. We compare the response of the artificial neural network with the biological neural network in the same colliding stimulus. This approach led to a series of testable predictions about the organization of the biological neural network.

Paper Details

Date Published: 1 November 1992
PDF: 9 pages
Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); doi: 10.1117/12.131594
Show Author Affiliations
F. Claire Rind, Univ. of Newcastle upon Tyne and Univ. of Teeside (United Kingdom)
Charles R. Allen, Univ. of Newcastle upon Tyne and Univ. of Teeside (United Kingdom)


Published in SPIE Proceedings Vol. 1826:
Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods
David P. Casasent, Editor(s)

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