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Proceedings Paper

Neural networks for mobile robot visual exploration
Author(s): Ivan A. Bachelder; Allen M. Waxman
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Paper Abstract

This work describes the implementation of some of the neural systems that will enable a mobile robot to actively explore and learn its environment visually. These systems perform the real-time extraction of robust visual features, the segmentation of landmarks from the background and from each other using binocular attentional mechanisms, the predictive binocular tracking of landmarks, and the learning and recognition of landmarks from their features. Also described are preliminary results of incorporating most of these systems into a mobile robot called MAVIN, which can demonstrate the visual exploration of simplified landmarks. Finally, we discuss plans for using similar neural strategies to extend MAVIN's capabilities by implementing a biologically plausible system for navigating through an environment that has been learned by exploration. This explorational learning consists of quantizing the environment into orientation-specific place fields generated by the view-based spatial distribution of landmarks, and associating these place fields in order to form qualitative, behavioral, spatial maps.

Paper Details

Date Published: 4 May 1993
PDF: 12 pages
Proc. SPIE 1831, Mobile Robots VII, (4 May 1993); doi: 10.1117/12.143783
Show Author Affiliations
Ivan A. Bachelder, Lincoln Lab./MIT (United States)
Allen M. Waxman, Lincoln Lab./MIT (United States)


Published in SPIE Proceedings Vol. 1831:
Mobile Robots VII
William J. Wolfe; Wendell H. Chun, Editor(s)

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