Cognitive modeling for navigation of mobile robots using the sensory gradient concept

  • Francisco Serradilla
  • Darío Maravall
3 Engineering Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1333)


In order to build models reflecting accurately the structure of the real world Artificial Intelligent-based systems have the difficult problem of reducing the continuous, extremely complex sensory information into a discrete and simple model with the optimal computational burden. The new concept of sensory gradient is introduced in this paper to obtain computational models of the sensory information available for an autonomous mobile robot. The main objective is to guarantee that these models are simple and at the same time powerful enough to allow the performance of complex navigation tasks. We use the sensory gradient's module to identify situations and places that must be recorded (we call them Relevant Sensory Places or RSPs for short) and we build upon these RSPs a graph-based model of the universe that allows to develop navigation plans using plan-as-communication techniques. This novel approach is applied to the spatial reasoning problem as an aid to the autonomous navigation of mobile robots. Using a simulation environment the paper presents some empirical results which have been very encouraging. This novel approach has been successfully tested on a NOMAD-200 mobile robot platform.


mobile robots autonomous learning topological maps ultrasonic sensors path planning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Agre, R. E.; Chapman, D. 1990. What are Plans for? Designing of Autonomous Agents. P. Maes (ed). Cambridge, MA, MIT Press. pp. 17–34.Google Scholar
  2. Brooks, R. Mataric, M. 1993. Real Robots, Real Learning Problems, in Connell, J. H.; Mahadevan, S. (eds.). Robot Learning. Kluwer Academic Publishers. pp. 193–213.Google Scholar
  3. Elfes, A. 1991. Dynamic Control of Robot Perception Using Stochastic Spatial Models. Proceedings of the International Workshop on Information Processing in Mobile Robots. pp. 203–218.Google Scholar
  4. Hofstadter, D. R. 1979. Gödel, Escher, Bach, an Eternal Golden Braid. Basic Books.Google Scholar
  5. Kohonen, T. 1982. Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43. pp. 59–69.CrossRefGoogle Scholar
  6. Kuipers, B.; Byun, Y. 1988. A Robust, Qualitative Approach to a Spatial Learning Mobile Robot. SPIE Vol. 1003 Sensor Fusion: Spatial Reasoning and Scene Interpretation. pp. 366–375.Google Scholar
  7. Kumpel, D.; Serradilla, F. 1990. Robot Navigation Systems in a Partially Known Environment Using a Space-Time Learning Graph. Proceedings of CIM-Europe 6th Annual Conference. pp. 65–75.Google Scholar
  8. Nilsson, N. J. 1980. Principles of Artificial Intelligence. Palo Alto, Ca., Tioga Publishing Company.Google Scholar
  9. Payton, D. 1991. Internalized Plans: A Representation for Action Resources. In Designing Autonomous Agents. P. Maes (ed). MIT Press. pp. 89–103.Google Scholar
  10. Pierce, D.; Kuipers, B. 1994. Learning to Explore and Build Maps. Proceedings of the Twelfth National Conference on Artificial Intelligence. Vol. 2. MIT Press. pp. 1264–1271.Google Scholar
  11. Serradilla, F.; Maravall, D. 1996. A Navigation System for Mobile Robots Using Visual Feedback and Artificial Potential Fields. Proceedings of the Thirteenth European Meeting on Cybernetics and System Research. Trappl, R (ed.). pp. 1159–1164.Google Scholar
  12. Shen, W. 1994. Autonomous Learning from the Environment. Computer Science Press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Francisco Serradilla
    • 1
  • Darío Maravall
    • 2
  1. 1.Department of Applied Intelligent SystemsTechnical University of MadridMadridSpain
  2. 2.Department of Articial IntelligenceTechnical University of MadridBoadilla del Monte, MadridSpain

Personalised recommendations