Multimodal real-world mapping and navigation system for autonomous mobile robots based on neural maps

José L. Contreras-Vidal, J. Mario Aguilar, Juan Lopez Coronado, Eduardo Zalama

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


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 map7 to combine range data from stereo vision and ultrasonic rangefinders. Visual output from a boundary contour system11'2, is used to extract range data from a pair of 2D 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.

Original languageEnglish (US)
Pages (from-to)247-256
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Sep 16 1992
EventApplications of Artificial Neural Networks III 1992 - Orlando, United States
Duration: Apr 20 1992 → …

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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