Abstract
We have developped a mobile robot control system based on hippocampus and prefrontal models. We propose an alternative to models that rely on cognitive maps linking place cells. Our experiments show that using transition cells is more efficient than using place cells. The transition cell links two locations with the integrated direction used. Furthermore, it is possible to fuse the different directions proposed by nearby transitions and obstacles into an effective direction by using a Neural Field. The direction to follow is the stable fixed point of the Neural Field dynamics, and its derivative gives the angular rotation speed. Simulations and robotics experiments are carried out.
Keywords
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Tolman, E.C.: Cognitive maps in rats and men. The Psychological Review 55(4) (1948)
Arbib, M.A., Lieblich, I.: Motivational learning of spatial behavior. In: Metzler, J. (ed.) Systems Neuroscience, pp. 221–239. Academic Press, London (1977)
Schmajuk, N.A., Thieme, A.D.: Puposive behavior and cognitive mapping: a neural network model. Biological Cybernetics, 165–174 (1997)
Bachelder, I.A., Waxman, A.M.: Mobile robot visual mapping and localization: A view-based neurocomputationnal architecture that emulates hippocampal place learning. Neural Networks 7, 1083–1099 (1994)
Trullier, O., Wiener, S.I., Berthoz, A., Meyer, J.A.: Biologically based artificial navigation systems: review and prospects. Progress in Neurobiology 51, 483–544 (1997)
Schölkopf, B., Mallot, H.A.: View-based cognitive mapping and path-finding. Adaptive Behavior 3, 311–348 (1995)
Bugmann, G., Taylor, J.G., Denham, M.J.: Route finding by neural nets. In: Taylor, J.G. (ed.) Neural Networks, Henley-on-Thames, pp. 217–230. Alfred Waller Ltd. (1995)
Donnart, J.Y., Meyer, J.A.: Learning reactive and planning rules in a motivationnally autonomous animat. IEEE Transactions on Systems, Man and Cybernetics-Part B 26(3), 381–395 (1996)
O’Keefe, J., Nadel, N.: The hyppocampus as a cognitive map. Clarenton Press, Oxford (1978)
Arleo, A., Gerstner, W.: Spatial cognition and neuro-mimetic navigation: A model of hippocampal place cell activity. Biol. Cybern. 83(3), 287–299 (2000)
Amari, S.: Dynamics of pattern formation in lateral-inhibition type neural fields. Biological Cybernetics 27, 77–87 (1997)
Schöner, G., Dose, M., Engels, C.: Dynamics of behavior: theory and applications for autonomous robot architectures. Robotics and Autonomous System (2–4), 213–245 (1995)
Meyer, J.A., Wilson, S.W.: From animals to animats. In: First International Conference on Simulation of Adaptive Behavior. MIT Press/Bardford Book2-4 ed. (1991)
Gaussier, P., Leprêtre, S., Quoy, M., Revel, A., Joulain, C., Banquet, J.P.: Experiments and models about cognitive map learning for motivated navigation. Robotics and Intelligent Systems Series 24, 53–94 (2000)
Banquet, J.P., Gaussier, P., Dreher, J.C., Joulain, C., Revel, A.: Space-Time, Order and Hierarchy in Fronto-Hippocampal System: A Neural Basis of Personality. In: Cognitive Science Perpectives on Personality and Emotion, vol. 124, Elsevier Science, Amsterdam (1997)
Gaussier, P., Revel, A., Banquet, J.P., Babeau, V.: From view cells and place cells to cognitive map learning: processing stages of the hippocampal system. Biological Cybernetics 86, 15–28 (2002)
Samsonovich, A., McNaughton, B.: Path integration and cognitive mapping in a continuous attractor neural network model. Journal of Neuroscience 17(15), 5900–5920 (1997)
Bellman, R.E.: On a routing problem. Quaterly of Applied Mathematics, 16, 87–90 (1958)
Quoy, M., Moga, S., Gaussier, P.: Dynamical neural networks for top-down robot control. IEEE transactions on Man, Systems and Cybernetics, Part A 33(4), 523–532 (2003)
Andry, P., Gaussier, P., Moga, S., Banquet, J.P., Nadel, J.: The dynamics of imitation processes: from temporal sequence learning to implicit reward communication. IEEE Trans. on Man, Systems and Cybernetics Part A: Systems and humans 31(5), 431–442 (2001)
Khatib, O.: Real-time obstcle avoidance for manipulators and mobile robots. Int. Journ. of Rob. Res. 5(1), 90–98 (1986)
Koren, Y., Borenstein, J.: Potential field methods and their inherent limitations for mobile robot navigation. In: Proc. IEEE Conf. on Rob. and Autom., pp. 1398–1404 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cuperlier, N., Quoy, M., Laroque, P., Gaussier, P. (2005). Transition Cells and Neural Fields for Navigation and Planning. In: Mira, J., Álvarez, J.R. (eds) Mechanisms, Symbols, and Models Underlying Cognition. IWINAC 2005. Lecture Notes in Computer Science, vol 3561. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499220_36
Download citation
DOI: https://doi.org/10.1007/11499220_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26298-5
Online ISBN: 978-3-540-31672-5
eBook Packages: Computer ScienceComputer Science (R0)