Abstract
Current robotics is perhaps the most complete paradigm of applied Artificial Intelligence, since it includes generic tasks involving pluri-sensorial integration and internal representation, as well as motor planning and control. In this paper we revise the architecture proposed by Craik and McCulloch and the concept of environment model introduced by K. Craik. Based on this architecture, which links the description in terms of properties with the selection of a mode of action, we study a simple example application in which an incremental procedure is proposed for the construction and use of a model of a structured medium (the interior of a building) using a graph. The type of graph used to store the descriptions of objects and the relations between them is inspired by the work of Hillier and Hanson on the analysis of interiors. The connections between the elements of the environment (graph nodes) are generated in such a way as to facilitate their efficient use for the selection of the most pertinent mode of action at any given moment. The derivation of the graph is carried out autonomously. In the development of this work, we have avoided as far as possible the use of anthropomorphic terms with no causal connection to the symbol level. Posed in this way, the problem of the representation and use of an environment model by a robot reduces to the use of models of generic tasks and methods at the knowledge level together with graphs and finite state machines at the formal level.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
R.C.Arkin: Integrating behavioural, perceptual, and world knowledge in reactive navigation. In “Designing Autonomous Agents” MIT Press. (1994) 105–121.
J. Borestein, Y. Koren: Real-time obstacle avoidance for fast mobile robots. IEEE Transactions on Systems, man and cybernetics. VOL 19, no 5 September (1989).
R.A.Brooks: A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, Vol RA-2, no 1 March (1986) 14–23.
K. Craik: The Nature of Explanation. Cambridge University Press. (1943).
Fennema, C., Hanson, A., Riseman, E., Beveridge, J.R. and Kumar, R.: Model-Directed Mobile Robot Navigation. IEEE Transactions on System, Man and Cybernetics. Vol. 20, n°6. (1990).
González, J., Ollero, A. And Reina, A.: Map building for a mobile robot equipped with a Laser Range Scanner, IEEE Int. Conf. on Robotics an Automation. San Diego, CA, (1994). 35–43.
Hillier, B. and Hanson, J.: The Social Logic of Space. Cambridge Press (1984).
Using Machine Learning Techniques in Real-World Mobile Robots: Kaiser, M., Klingspor, V., Millán, J. del R., Accame, M., Wallner, F., Dillmann, R.. IEEE Expert Intelligent Systems & their Applications, 1995. 37–45.
Kilmer, W.S. McCulloch, W.L: The Reticular Formation. Command and Control System. In Information Processing in the Nervous System. K.N. Leibovic (Ed). Springer-Verlag Berlin 1969. (1969). 297–307.
Kuipers, B.: Modeling spatial knowledge Cognitive Science, 2, (1978). 129–153.
Maes, P.: Situated agents can have goals. In “Designing Autonomous Agents” MIT Press (1994). 49–71
Mira, J., Delgado, A., Boticario, J. G. and Díez, F. J.: Aspectos básicos de la inteligencia artificial. Sanz y Torres, (1995).
Moravec, H. P., and Elfes, A.: High resolution maps from wide angle sonar, Proceedings of the 1985 IEEE Int. Conf on Robotics and Automation. (1985)
Moreno-Díaz, R. and Mira Mira, J.: Architectures for Integration of Artificial Perception and Action. Proceedings of Interkibernetic 87. World Association of Cybernetics, Computer Science and Systems Theory. University of Barcelona. Spain (1988).
Murciano, A., Millán, J. del R.: Learning signalling behaviours and specialization in cooperative agents. Journal of Adaptive Behavior 5(l), (1997).
Murciano, A., Zamora, J., de la Paz, F., Girón, J.M., Millán, J. del R.: Robot móvil para investigación en grupos cooperantes. XVIII Jomadas de Automática y reunión CEA-IFAC, pp 125–131.Gerona (1997).
Newell, A.: The Knowledge Level, AI Magazine, Summer (1981), 1–20.
Newell, A. and Simon, H. A.: Human Problem Solving. Prentice-Hall, Englewood Cliffs NJ, (1972).
Steels, L.: Discovering the competitors. Journal of Adaptive Behavior 4(2), (1996).
Sutro, L.L., Warren, R.E., Whitman, C., Zeise, F.: 1963 Advanced Sensor Investigations R-470. Instrumentation Laboratory, M.I.T. Cambridge MA. (1964).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Romo, J., de la Paz, F., Mira, J. (1998). Incremental building of a model of environment in the context of the McCulloch-Craik's functional architecture for mobile robots. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_420
Download citation
DOI: https://doi.org/10.1007/3-540-64574-8_420
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64574-0
Online ISBN: 978-3-540-69350-5
eBook Packages: Springer Book Archive