Global Navigation Through Local Reference Frames

  • John Pisokas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)


The contribution of this paper is that illustrates the use of funneling actions in combination with local deictic reference frames for forming consistent and useful large scale maps. These maps do not rely on any geodetic sensors. Indications for the feasibility of such representations in humans, and other species, can be found in studies of spatial cognition. However, such implementations or applications in robotics have not been illustrated until now.


Mobile Robot Spatial Reasoning Magnetic Compass Local Reference Frame Egocentric Reference Frame 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Agre, P.E.: The Dynamic Structure of Everyday Life. Technical report AI-TR-1085, MIT Artificial Intelligence Laboratory (1988)Google Scholar
  2. 2.
    Ballard, D., Hayhoe, M., Pook, P., Rao, R.: Deictic codes for the embodiment of cognition. Behavioral And Brain Sciences 20, 723–767 (1997)Google Scholar
  3. 3.
    Brooks, R.A.: A robust layered control system for a mobile robot. Technical Report 864, MIT AI Lab (September 1985)Google Scholar
  4. 4.
    Bugmann, G.: A connectionist approach to spatial memory and planning, ch. 5, pp. 109–146. Springer, London (1997)Google Scholar
  5. 5.
    Deacon, G.E., Low, P.L., Malcolm, C.: Orienting objects in a minimum number of robot sweeping motions. Technical Report DAI Research Paper No. 619, Division of Informatics, University of Edinburgh (2001)Google Scholar
  6. 6.
    Finney, S., Wakker, P., Kaelbling, L., Oates, T.: The thing that we tried didn’t work very well: Deictic representation in reinforcement learning. In: Proceedings of the 18th Annual Conference on Uncertainty in Artificial Intelligence (2002)Google Scholar
  7. 7.
    Gat, E.: Integrating planning and reacting in a heterogeneous asynchronous architecture for controlling real-world mobile robots. In: Proceedings AAAI 1992, vol. 4(4), pp. 809–815 (1992)Google Scholar
  8. 8.
    Gillner, S., Mallot, H.: Navigation and acquisition of spatial knowledge in a virtual maze. Journal of Cognitive Neuroscience 10, 445–463 (1998)CrossRefGoogle Scholar
  9. 9.
    Koenig, S., Simmons, R.G.: Xavier: A Robot Navigation Architecture Based on Partially Observable Markov Decision Process Models. In: Artificial Intelligence and Mobile Robots, pp. 91–122. AAAI and MIT Press (1998)Google Scholar
  10. 10.
    Kuipers, B., Byun, Y.T.: A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. Robotics and Autonomous Systems 8, 46–63 (1991)CrossRefGoogle Scholar
  11. 11.
    Malcolm, C., Smithers, T.: Symbol grounding via a hybrid architecture in an autonomous assembly system. Robotics and Autonomous Systems (Special Issue – Designing Autonomous Agents) 6(1&2) (June 1990)Google Scholar
  12. 12.
    Matarić, M.J.: Environment learning using a distributed representation. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 402–406 (1990)Google Scholar
  13. 13.
    O’Keefe, J., Nadel, L.: The Hippocampus as Cognitive Map. Oxford University Press, Oxford (1978)Google Scholar
  14. 14.
    Suchman, L.: Plans and Situated Actions: The Problem of Human-Machine Communication. Cambridge Press, Cambridge (1987)Google Scholar
  15. 15.
    Wang, R.: Learning and unlearning spatial relationships during navigation. Journal of Vision 3 (2003)Google Scholar
  16. 16.
    Wills, T., Lever, C., Cacucci, F., Burgess, N., O’Keefe, J.: Experience dependent attractors in the hippocampal representation of the local environment. Science 308, 873–876 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • John Pisokas
    • 1
  1. 1.Computer Science DepartmentUniversity of EssexColchesterUK

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