Abstract
There are many ways to define what constitutes a suitable landmark for mobile robot navigation, and automatically extracting landmarks from an environment as the robot travels is an open research problem. This paper describes an automatic landmark selection algorithm that chooses as landmarks any places where a trained sensory anticipation model makes poor predictions. The model is applied to a route navigation task, and the results are evaluated according to how well landmarks align between different runs on the same route. The quality of landmark matches is compared for several types of sensory anticipation models and also against a non-anticipatory landmark selector. We extend and correct the analysis presented in [6] and also present a more complete picture of the importance of sensory anticipation to the landmark selection process. Finally, we show that the system can navigate reliably in a goal-oriented route-following task, and we compare success rates using only metric distances with using a combination of odometric and landmark category information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baraldi, A., Alpaydin, E.: Constructive feedforward ART clustering networks - part II. IEEE Transactions on Neural Networks 13(3), 662–677 (2002)
Baraldi, A., Blonda, P.: A survey of fuzzy clustering algorithms for pattern recognition: Part II. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics 29(6), 786–801 (1999)
Bourque, E., Dudek, G.: On-line construction of iconic maps. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2000), pp. 2310–2315 (2000)
Butz, M.V., Sigaud, O., Gerard, P.: Internal models and anticipations in adaptive learning systems. In: Butz, M.V., Sigaud, O., Gérard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. LNCS (LNAI), vol. 2684, pp. 86–109. Springer, Heidelberg (2003)
Duckett, T., Nehmzow, U.: Mobile robot self-localisation using occupancy histograms and a mixture of gaussian location hypotheses. Robotics and Autonomous Systems 34(2–3), 119–130 (2001)
Fleischer, J., Marsland, S.: Learning to autnomously select landmarks for navigation and communication. In: Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior, pp. 151–160. MIT Press, Cambridge (2002)
Hollander, M., Wolfe, D.: Nonparametric Statistical Methods. Wiley, New York (1973)
Jirenhed, D.-A., Hesslow, G., Ziemke, T.: Exploring internal simulation of perception in mobile robots. In: Proceedings of the Fourth European Workshop on Advanced Mobile Robots, pp. 107–113 (2001)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82, 34–45 (1960)
Kohonen, T.: Self-organised formation of topologically correct feature maps. Biological Cybernetics 43, 59–69 (1982)
Kortenkamp, D., Weymouth, T.: Topological mapping for mobile robots using a combination of sonar and vision sensing. In: Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI 1994), Seattle, Washington, pp. 979–984 (1994)
Maistros, G., Marom, Y., Hayes, G.: Perception-action coupling via imitation and attention. In: Proceedings of the AAAI Fall Symposium on Anchoring Symbols to Sensor Data in Single and Multiple Robot Systems, pp. 52–59 (2001)
Marsland, S.: Novelty detection in learning systems. Neural Computing Surveys 3, 157–195 (2003)
Marsland, S., Nehmzow, U., Duckett, T.: Learning to select distinctive landmarks for mobile robot navigation. Robotics and Autonomous Systems 37, 241–260 (2001)
Marsland, S., Nehmzow, U., Shapiro, J.: A self-organising neural network that grows when required. Neural Networks 15(8-9), 1041–1058 (2002)
Stephan, V., Gross, H.-M.: Neural anticipative architecture for expectation driven perception. In: Proceedings of 2001 IEEE International Conference On Systems, Man & Cybernetics, pp. 2275–2280 (2001)
Thrun, S.: Bayesian landmark learning for mobile robot localisation. Machine Learning 33(1), 41–76 (1998)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM Toolbox for Matlab 5. Report A57, Helsinki University of Technology, Neural Networks Research Centre, Espoo, Finland (April 2000)
Wolpert, D.M., Ghahramani, Z.: Computational priniciples of movement neuroscience. Nature Neuroscience Supplement 3, 1212–1217 (2000)
Yamauchi, B., Beer, R.: Spatial learning for navigation in dynamic environments. IEEE Transactions on Systems, Man and Cybernetics Section B 26(3), 496–505 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Fleischer, J., Marsland, S., Shapiro, J. (2003). Sensory Anticipation for Autonomous Selection of Robot Landmarks. In: Butz, M.V., Sigaud, O., Gérard, P. (eds) Anticipatory Behavior in Adaptive Learning Systems. Lecture Notes in Computer Science(), vol 2684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45002-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-45002-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40429-3
Online ISBN: 978-3-540-45002-3
eBook Packages: Springer Book Archive