16 September 1992 Multimodal real-world mapping and navigation system for autonomous mobile robots based on neural maps
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This work describes a neural network-based approach to multimodal real-world mapping and navigation for autonomous mobile robots in unknown environments. The system is built on top of a vector associative map to combine range data from stereo vision and ultrasonic rangefinders. Visual output from a boundary contour system is used to extract range data from a pair of 2-D images. In addition, range data from ultrasonic lasers is used to eliminate uncertainties, noise, and intrinsic errors introduced by the measurements. A recurrent competitive field used to model multimodal working memory excites a trajectory formation network which transforms desired temporal patterns (i.e., a trajectory formation pattern) into spatial patterns. The output of this network is processed by direction-sensitive cells which in turn activates the motor system that guides a mobile robot in unstructured environments. The model is capable of unsupervised, real-time, fast error-based learning of an unstructured environment.
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Jose L. Contreras-Vidal, Jose L. Contreras-Vidal, J. Mario Aguilar, J. Mario Aguilar, Juan Lopez-Coronado, Juan Lopez-Coronado, Eduardo Zalama, Eduardo Zalama, } "Multimodal real-world mapping and navigation system for autonomous mobile robots based on neural maps", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140003; https://doi.org/10.1117/12.140003


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