Skip to main content
SpringerLink
Log in

Object Recognition for Obstacle Avoidance in Mobile Robots

  • Conference paper
Book cover Artificial Intelligence and Soft Computing – ICAISC 2006 (ICAISC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4029))

Included in the following conference series:

Abstract

In this paper is shown an obstacle avoidance strategy based on object recognition using an artificial vision application. Related works focus on the implementation of efficient algorithms for image processing. This work emphasizes in using minimum information from an image in order to generate free obstacles trajectories. The algorithm used is based on Pattern Matching for detection of the robot and Classification for the rest of objects. Each form of detection has its particular algorithm: Cross Correlation for Pattern matching and Nearest Neighbor for Classification. The objective pursued is to demonstrate that, with a very simple system, precise information can be provided to a navigation system in order to find free obstacle paths.

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 139.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Hel-Or, Y., Hel-Or, H.: Real Time Pattern Matching Using Projection Kernels. IEEE Trans. on Patt. Anal. and Mach. Int. 27, 1430–1445 (2005)

    Article  Google Scholar 

  2. Uenohara, M., Kanade, T.: Use of Fourier and Karhunen-Loeve decomposition for fast pattern matching with a large set of templates. IEEE Trans. on Patt. Anal. and Mach. Int., 19, 891–898 (1997)

    Google Scholar 

  3. National Instruments: IMAQ Vision Concepts Manual, pp. 12-1–12-8, 16-1–16-21 (2005)

    Google Scholar 

  4. Bolanos, J.M.: Embedded Control System Implementation for a Differencial Drive Vehicle. BSc. Thesis. Simon Bolivar University, pp. 42–53, 69–76 (2006)

    Google Scholar 

  5. Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis. Third revised and enlarged edition, pp. 124–132. Springer, Heidelberg (1999)

    Google Scholar 

  6. Lewis, J. P.: Fast Normalized Cross-Correlation. Industrial Light & Magic, available in http://www.idiom.com/~zilla/index.html

  7. Gonzales, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley Publishing Company, Inc., Reading (1992)

    Google Scholar 

  8. Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley Publishing Company, Inc., Reading (1974)

    MATH  Google Scholar 

  9. Sing-Tze, B.: Pattern Recognition: Application to Large Data-Set Problems. Marcel-Dekker, New York (1984)

    Google Scholar 

  10. Mitchell, T.: Machine Learning. pp. 230–235, McGraw-Hill Science/Engineering/Math (1997)

    Google Scholar 

  11. Willard, S.: General Topology, p. 16. Addison-Wesley, Reading (1970)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mechatronics Group, ELE-302, Simon Bolivar University, 1080-A, Sartenejas, Miranda, Venezuela

    José M. Bolanos, Wilfredis Medina Meléndez, Leonardo Fermín, José Cappelletto, Gerardo Fernández-López & Juan C. Grieco

Authors
  1. José M. Bolanos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wilfredis Medina Meléndez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leonardo Fermín
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Cappelletto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gerardo Fernández-López
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Juan C. Grieco
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Berkeley Initiative in Soft Computing (BISC), University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Żurada

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bolanos, J.M., Meléndez, W.M., Fermín, L., Cappelletto, J., Fernández-López, G., Grieco, J.C. (2006). Object Recognition for Obstacle Avoidance in Mobile Robots. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_75

Download citation

  • DOI: https://doi.org/10.1007/11785231_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35748-3

  • Online ISBN: 978-3-540-35750-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation