Skip to main content
SpringerLink
Log in

Steady-State Genetic Algorithms for Growing Topological Mapping and Localization

  • Conference paper
PRICAI 2010: Trends in Artificial Intelligence (PRICAI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6230))

Included in the following conference series:

Abstract

This paper proposes a method of simultaneous localization and mapping based on computational intelligence for a robot partner in unknown environments. First, we propose a method of topological map building based on a growing neural network. Next, we propose a method of localization based on steady-state genetic algorithm. Finally, we discuss the effectiveness of the proposed methods through several experimental results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Michalski, R., Tecuci, G.: Machine Learning: A Multistrategy Approach. Morgan Kaufmann Publishing, San Francisco (1994)

    Google Scholar 

  2. Bianco, G., Cassinis, R.: Multi-strategic approach for robot path planning in an unknown environment (1993)

    Google Scholar 

  3. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)

    MATH  Google Scholar 

  4. Sasaki, H., Kubota, N., Taniguchi, K.: Topological Map and Cell Space Map for SLAM of A Mobile Robot. GESTS International Transactions on Computer and Engineering 45(3) (2008)

    Google Scholar 

  5. Tomono, M., Yuta, S.: Object-based Localization and Mapping using Loop Constraints and Geometric Prior Knowledge. In: Proc. of International Conference on Robotics and Automation, pp. 862–867 (2003)

    Google Scholar 

  6. Thrun, S.: Robotic mapping: A survey. In: Lakemeyer, G., Neberl, B. (eds.) Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann, San Francisco (2002)

    Google Scholar 

  7. Wang, C., Thorpe, C.: Simultaneous localization and mapping with detection and tracking of moving objects. In: IEEE International Conference on Robotics and Automation, pp. 2918–2924 (2002)

    Google Scholar 

  8. Singh, K., Fujimura, K.: Map Making by Cooperative Mobile Robots. In: Proc. of IEEE International Conference on Robotics and Automation, pp. 254–259 (1993)

    Google Scholar 

  9. Tomono, M., Yuta, S.: Mobile Robot Localization based on an Inaccurate Map. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 399–405 (2001)

    Google Scholar 

  10. Thrun, S., Fox, D., Burgard, W., Dellaert, F.: Robust Monte Carlo localization for mobile robots. Artificial Intelligence 128, 99–141 (2001)

    Article  MATH  Google Scholar 

  11. Brooks, R.A.: Planning Collision Free Motions for Pick and Place Operation. In: Robotics Research, pp. 5–38. MIT Press, Cambridge (1983)

    Google Scholar 

  12. Latombe, H.-L.: Robot Motion Planning. Kluwer Academic Publishers, Dordrecht (1991)

    Google Scholar 

  13. Fu, K.S., Gonzalez, R.C., Lee, C.S.G.: Robotics. McGraw-Hill Book Company, New York (1987)

    Google Scholar 

  14. Thrun, S.: Robotic mapping: A survey. In: Lakemeyer, G., Neberl, B. (eds.) Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann, San Francisco (2002)

    Google Scholar 

  15. Wang, C., Thorpe, C.: Simultaneous localization and mapping with detection and tracking of moving objects. In: IEEE International Conference on Robotics and Automation, pp. 2918–2924 (2002)

    Google Scholar 

  16. Singh, K., Fujimura, K.: Map Making by Cooperative Mobile Robots. In: Proc. of IEEE International Conference on Robotics and Automation, pp. 254–259 (1993)

    Google Scholar 

  17. Tomono, M., Yuta, S.: Object-based Localization and Mapping using Loop Constraints and Geometric Prior Knowledge. In: Proc. of International Conference on Robotics and Automation, pp. 862–867 (2003)

    Google Scholar 

  18. Brady, M., Paul, R.: Robotics Research, The First International Symposium. The MIT Press, Massachusetts (1984)

    Google Scholar 

  19. Kubota, N., Neya, H., Taniguchi, K.: Sensory Network and Evolutionary Programming for a Mobile Robot. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL 2002), pp. 119–123 (2002) (CD-ROM)

    Google Scholar 

  20. Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Trans. Signal Processing 50(2), 174–188 (2002)

    Article  Google Scholar 

  21. Kubota, N., Yuki, K., Baba, N.: Integration of Intelligent Technologies for Simultaneous Localization and Mapping. In: The Society of Instrument and Control Engineers, SICE (2009)

    Google Scholar 

  22. Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  23. Fritzke, B.: Growing Cell Structures - A Self-Organising Network for Unsupervised and Supervised Learning. Neural Networks 7(9), 1441–1460 (1994)

    Article  Google Scholar 

  24. Hodge, V.J., Austin, J.: Hierarchical Growing Cell Structures: TreeGCS. IEEE Trans. Knowledge and Data Engineering 13(2), 207–218 (2001)

    Article  Google Scholar 

  25. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2001)

    MATH  Google Scholar 

  26. Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 625–632. MIT Press, Cambridge (1995)

    Google Scholar 

  27. Fukuda, T., Kubota, N., Arakawa, T.: GA Algorithms in Intelligent Robots. In: Fuzzy Evolutionary Computation, pp. 81–105. Kluwer Academic Publishers, Dordrecht (1997)

    Google Scholar 

  28. Kubota, N., Neya, H., Taniguchi, K.: Sensory Network and Evolutionary Programming for a Mobile Robot. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL 2002), pp. 119–123 (2002) (CD-ROM)

    Google Scholar 

  29. Fogel, D.B.: Evolutionary Computation. IEEE Press, Los Alamitos (1995)

    Google Scholar 

  30. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Reading (1989)

    MATH  Google Scholar 

  31. Syswerda, G.: A Study of Reproduction in Generational and Steady-State Genetic Algorithms. In: Foundations of Genetic Algorithms, pp. 94–101. Morgan Kaufmann, San Francisco (1991)

    Google Scholar 

  32. Hafnel, D., Burgard, W., Fox, D., Fishkin, K., Philipose, M.: Mapping and Localization with RFID Technology. In: Proc. of the IEEE International Conference on Robotics and Automation, ICRA (2004)

    Google Scholar 

  33. Bertsekas, D.P., Tsitsiklis, J.N.: Neuro-Dynamic programming. Athena Scientific, Belmont (1996)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo, 191-0065, Japan

    Jinseok Woo & Naoyuki Kubota

  2. School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea

    Beom-Hee Lee

Authors
  1. Jinseok Woo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naoyuki Kubota
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Beom-Hee Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, Seoul National University, 151-744, Seoul, Korea

    Byoung-Tak Zhang

  2. Department of Computing,, Macquarie University, NSW, Sydney, Australia

    Mehmet A. Orgun

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Woo, J., Kubota, N., Lee, BH. (2010). Steady-State Genetic Algorithms for Growing Topological Mapping and Localization. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15246-7_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15245-0

  • Online ISBN: 978-3-642-15246-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation