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Material Classification Using Reflected Signal of Ultrasonic Sensor

초음파의 반사 신호를 이용한 실내환경의 재질 인식

  • Published : 2006.06.01

Abstract

Material information for environment may be useful to accomplish mobile robot localization. A procedure to classify a set of indoor materials (glass, steel, wood, aluminum and concrete) with the reflected signal of ultrasonic sensor is proposed in this paper. The main idea is to use material-specific reflection characteristics for the recognition of material type. To achieve the classification task, we modeled reflected signal as a maximum amplitude with respect to distance. In this way, we can generate echo signal models for the given materials and these models are used to compare with the current sensor reading. The experimental results show that the proposed method may give material information during map building task of mobile robot.

Keywords

References

  1. I. Ihara, H. Koguchi, T. Aizawa, and J. Kihara, 'Determination of ultrasonic attenuation in surface layer of materials by ultrasonic reflectivity measurement,' Electron. Lett., vol. 29, pp. 1999-2000, 1993 https://doi.org/10.1049/el:19931331
  2. J. Budenske and M. Gini, 'Why is it so difficult for a robot to pass through a door way using ultrasonic sensors?,' Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 3124-3129, 1994 https://doi.org/10.1109/ROBOT.1994.351090
  3. B. Barshan and R. Kuc, 'Differentiating sonar reflections from corners and planes by employing an intelligent sensor,' IEEE Trans. on Pattern Anal. Mach. Intell., vol. 12, no. 6, pp. 560-569, 1990 https://doi.org/10.1109/34.56192
  4. H. Peremans, K. Audenaert, and J. Campenhout, 'A high resolution sensor based on tri-aural perception,' IEEE Trans. on Robotics and Automation, vol. 9, no. 1, pp. 36-48, 1993 https://doi.org/10.1109/70.210793
  5. R. Kuc and O. Bozma, 'A physical model-based analysis of heterogeneous environments using sonar,' IEEE Trans. on Pattern Anal. and Mach. Intell., vol. 16, no. 5, pp. 497-506, 1994 https://doi.org/10.1109/34.291448
  6. L. Kleeman and R. Kuc, 'Mobile robot sonar for target localization and classification,' Int. J. of Robot. Res. vol. 14, no. 4, pp. 295-318, 1995 https://doi.org/10.1177/027836499501400401
  7. J. Ko, W. Kim and C. Jin, 'A method of acoustic landmark extraction for mobile robot navigation,' IEEE Trans. on Robotics and Automation, vol. 12, no. 3, pp. 478-485, 1996 https://doi.org/10.1109/70.499829
  8. W. Lee and I. Kweon, 'Modeling and target classification using multiple reflections of sonar,' J of Control, Automation, and Systems Engineering, vol. 10, no. 9, pp. 779-784, 2004 https://doi.org/10.5302/J.ICROS.2004.10.9.779
  9. G. Benet, M. Martinez, F. Blanes, P. Perez, and J. Simo, 'Differentating walls from corners using the amplitude of ultrasonic echoes,' Robotics and Autonomous System, vol. 50, pp. 13-25, 2005 https://doi.org/10.1016/j.robot.2004.07.011
  10. M. Garcia and A. Solans, 'Estimation of distance to planar surfaces and type of material with infrared sensors,' Proc. of the 17th Int. Conf. on Pattern Recognition, 2004 https://doi.org/10.1109/ICPR.2004.1334298
  11. C.-Y. Lee, 'Differentiating a set of materials with ultrasonic sensors for mobile robot application,' Int. J. of Human-friendly Welfare Robots, Accepted for Publication, 2005
  12. A. Cracknell, Ultrasonics, Wykeham Publication Ltd., London, 1982
  13. Polaroid Corp., Ultrasonic Ranging System, Cambridge, MA, 1984
  14. D. Pagac, E. Nebot, and H. Durrant-Whyte, 'An evidential approach to map-building for autonomous vehicles,' IEEE Trans. on. Robotics and Automation, vol. 14, no. 4, pp. 623-629, 1998 https://doi.org/10.1109/70.704234