Skip to main content
Log in

Feature extraction method for a robot map using neural networks

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

Many map-building algorithms using ultrasonic sensors have been developed for mobile robot applications. In indoor environments, the ultrasonic sensor system gives some uncertain data. To compensate for this effect, a new feature extraction method using neural networks is proposed. A new, effective representation of the target is defined, and the reflection wave data patterns are learnt using neural networks. As a consequence, the targets are classified as planes, corners, or edges, which all frequently occur in indoor environments. We constructed our own robot system for the experiments which were carried out to show the performance.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Elfes A (1987) Sonar-based real-world mapping and navigation. IEEE J Robotics Autom RA-3:249–256

    Google Scholar 

  2. Borenstein J, Koren Y (1991) Histogramic in-motion mapping for mobile robot obstacle avoidance. IEEE Trans Robotics Autom 7:535–539

    Article  Google Scholar 

  3. Santos V, Goncalves J, Vaz F (1994) Perception maps for the local navigation of a mobile robot: a neural network approach Proceedings of IEEE International Conference on Robotics and Automation, Vol. 3. IEEE Computer Society Press, San Diego, pp 2193–2198

    Google Scholar 

  4. Toledo FJ, Luis JD, Tomas LM (2000) Map building with ultrasonic sensors of indoor environments using neural networks. Proceedings of IEEE International Conference on Systems, Man, and Cybernatics, Vol. 2, Nashville, pp. 920–925

    Google Scholar 

  5. Kuc R, Siegel MW (1987) Physically based simulation model for acoustic sensor robot navigation. IEEE Trans Pattern Anal Mach Intell 9:766–778

    Article  Google Scholar 

  6. Barshan B, Kuc R (1990) Differentiating sonar reflections from corners and planes by employing an intelligent sensor. IEEE Trans Pattern Anal Mach Intell 12:560–569

    Article  Google Scholar 

  7. Peremans H, Van Campenhout J (1993) Tri-aural perception on a mobile robot. Proceedings of IEEE International Conference on Robotics and Automation, Vol. 1. IEEE Computer Society Press, Atlanta, pp 265–270

    Google Scholar 

  8. Barshan B, Ayrulu B, Utete SW (2000) Neural network-based target differentiation using sonar for robotics applications. IEEE Trans Robotics Autom 16:435–442

    Article  Google Scholar 

  9. Borenstein J, Koren Y (1995) Error eliminating rapid ultrasonic firing for mobile robot obstacle avoidance. IEEE Trans Robotics Autom 11:132–138

    Article  Google Scholar 

  10. Fausett L (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. -H. Kim.

About this article

Cite this article

Kim, C.H., Lee, J.Y. & Lee, J.J. Feature extraction method for a robot map using neural networks. Artif Life Robotics 7, 86–90 (2003). https://doi.org/10.1007/BF02481153

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02481153

Key words

Navigation