Abstract
Estimating the position of a mobile robot in an environment is a crucial issue. It allows the robot to obtain more precisely the knowledge of its current state and to make the problem of generating command sequences for achieving a certain goal an easier task. The robot learns the environment using an unsupervised learning method and generates a percept – action- percept graph, based on the readings of an ultrasound sensor. The graph is then used in the process of position estimation by matching the current sensory reading category with an existing node category. Our approach allows the robot to generate a set of controls to reach a desired destination. For the learning of the environment, two unsupervised algorithms FuzzyART neural network and GNG network were used. The approach was tested for its ability to recognize previously learnt positions. Both algorithms that were used were compared for their precision.
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
Thrun, S., Burgard, W., Fox, D.: Probabalistic Robotics, 1st edn. The MIT Press, Cambridge (2005)
Racz, J., Dubrawski, A.: Qualitative Pose Estimation Using An Artificial Neural Network. In: ICAR 1995 (1995)
Mayer, G., Kaufmann, U., Kraetzschmar, G.: Neural robot detection in roboCup. In: Wermter, S., Palm, G., Elshaw, M. (eds.) Biomimetic Neural Learning for Intelligent Robots. LNCS (LNAI), vol. 3575, pp. 349–361. Springer, Heidelberg (2005)
Omidvar, O., Van der Smagt, P.: Neural Systems for Robotics. Academic Press, New York (1997)
Lameski, P., Kulakov, A., Davcev, D.: Learning and position estimation of a mobile robot using FuzzyART neural network. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2009)
Kulakov, A., Stojanov, G.: Structure, Inner Values, Hierarchies, and Stages: Essentials for Developmental Robot Architecture. In: Proceedings of the 2nd International Workshop on Epigenetic Robotics, pp. 63–70. Lund University Cognitive Studies (2002)
Stojanov, G., Trajkovski, G., Kulakov, A.: Interactivism in artificial intelligence (AI) and intelligent robotics. New Ideas in Psychology 24, 163–185 (2006)
Grossberg, S.: Adaptive Resonance Theory. Encyclopedia of Cognitive Science. Macmillan Reference Ltd., Basingstoke (2000)
Carpenter, G.A., Grossberg, S., Rosen, D.B.: Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4, 759–771 (1991)
Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 613–618. Springer, Heidelberg (1997)
Fritzke, B.: A growing neural gas network learns topologies. In: Advances in Neural Information Processing Systems (NIPS 1994), vol. 7, pp. 625–632. MIT Press, Cambridge (1995)
Martinetz, T.M.: Competitive Hebbian learning rule forms perfectly topology preserving maps. In: ICANN 1993: International Conference on Artificial Neural Networks, Amsterdam, pp. 427–434. Springer, Heidelberg (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lameski, P., Kulakov, A. (2010). Position Estimation of Mobile Robots Using Unsupervised Learning Algorithms. In: Davcev, D., Gómez, J.M. (eds) ICT Innovations 2009. ICT Innovations 2009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10781-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-10781-8_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10780-1
Online ISBN: 978-3-642-10781-8
eBook Packages: EngineeringEngineering (R0)