Using SVM to Avoid Humans: A Case of a Small Autonomous Mobile Robot in an Office

Conference paper
First Online:

Abstract

In this paper we construct a small, low-cost, autonomous mobile robot that avoids humans in an office. It applies an SVM classifier to each of 16 regions of interest in each picture taken by its camera mounted toward the ceiling. Experiments with 1 and 2 subjects with 3 kinds of velocities show encouraging results.

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

Notes

Acknowledgments

A part of this research was supported by Strategic International Cooperative Program funded by Japan Science and Technology Agency (JST) on the Japanese side and Agence Nationale de Recherches (ANR) on the French side.

References

  1. 1.
    Burgard, W., Cremers, A., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., Steiner, W., Thrun, S.: Experiences with an interactive museum tour-guide robot. Artif. Intell. 114(1-2), 3–55 (1999)CrossRefMATHGoogle Scholar
  2. 2.
    Jakobi, N., Husbands, P., Harvey, I.: Noise and the reality gap: the use of simulation in evolutionary robotics. In: Proceedings of the ECAL 1995, pp. 704–720, (1995)Google Scholar
  3. 3.
    Joachims, T.: Making Large-Scale Support Vector Machine Learning Practical, pp. 169–184. MIT Press, Cambridge (1999)Google Scholar
  4. 4.
    Nomdedeu, L., Sales, J., Cervera, E., Alemany, J., Sebastia, R., Penders, J., Gazi, V.: An experiment on squad navigation of human and robots. In: Proceedings of the ICARCV 2008, pp. 1212–1218. IEEE (2008)Google Scholar
  5. 5.
    Okada, K., Kagami, S., Inaba, M., Inoue, H.: Walking Human Avoidance and Detection from a Mobile Robot Using 3D Depth Flow. In Proc. ICRA 2001(3), 2307–2312 (2001)Google Scholar
  6. 6.
    Tamura, Y., Fukuzawa, T., Asama, H.: Smooth collision avoidance in human-robot coexisting environment. In Proc. IROS 2010, pp. 3887–3892 (2010)Google Scholar
  7. 7.
    Tsalatsanis, A., Valavanis, K., Yalcin, A.: Vision based target tracking and collision avoidance for mobile robots. J. Intell. Robotic Syst. 48(2), 285–304 (2007)CrossRefGoogle Scholar
  8. 8.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited  2011

Authors and Affiliations

  • Emi Matsumoto
    • 1
  • Michèle Sebag
    • 2
  • Einoshin Suzuki
    • 1
  1. 1.Department of Informatics, ISEEKyushu UniversityFukuokaJapan
  2. 2.TAO—CNRS & Univ.OrsayFrance

Personalised recommendations