Advertisement

Man-made versus biological in-air sonar systems

  • Herbert Peremans
  • Fons De Mey
  • Filips Schillebeeckx

Abstract

In this chapter we will argue that biologically inspired sonar systems, i.e. man-made systems that implement functional principles of their biological counterparts, are capable of significantly improving the performance of current in-air sonar systems. Instead of collecting large numbers of sonar range readings from multiple observation points and combining them into a reliable environment map we advocate the use of intelligent sonar sensors capable of extracting significantly more information from a single measurement. As an example of this bio-inspired approach we present a binaural sonar sensor capable of localizing reflectors in 3 D-space using broadband spectral cues introduced by the emitter and receiver directional filters. Acoustic simulations indicate that duplicating the outer ears and combining them with an emitter that acts by directing emitted energy in the frontal direction should be sufficient to approximate the significant features of the directional properties of a real bat’s sonar system. Localisation is performed by a template matching scheme whereby the spectrum of the received echo signal is compared with a set of stored spectral templates, one for every direction. This bio-inspired 3 D localisation scheme was implemented on a robotic bat head and validated in a series of experiments. The results from these experiments show that both the monaural and the binaural spectral cues introduced by the emitter/receiver directional filters carry sufficient information to discriminate between different reflector locations in realistic noise conditions. The experiments further show that to track a moving spherical targetwith our robotic system spectral information from both receivers is required.

Keywords

Sonar System Sonar Sensor Target Impulse Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Altes R (1978) Angle estimation and binaural processing in animal echolocation. J Acoust Soc Am 63: 155–173PubMedCrossRefGoogle Scholar
  2. Aytekin M, Grassi E, Sahota M. Moss CF (2004) The bat head-related transfer function reveals binaural cues for sound localization in azimuth and elevation. J Acoust Soc Am 116: 3594–3605PubMedCrossRefGoogle Scholar
  3. Choset H, Lynch KM, Hutchinson S, Kantor G, Burgard W, Kavraki LE, Thrun S (2005) Principles of robot motion. The MIT Press, Cambridge LondonGoogle Scholar
  4. CIRCE (2005) URL: <http://www.ua.ac.be/main.aspx>?c=.APL&n=40656Google Scholar
  5. De Mey F, Reijniers J, Peremans H, Otani H, Firzlaff U (2008) Simulated head related transfer function of the phyllostomid bat Phyllostomus discolor. J Acoust Soc Am 124: 2123–2132PubMedCrossRefGoogle Scholar
  6. Firzlaff U, Schuller G (2003) Spectral directionality of the external ear of the lesser spear-nosed bat, Phyllostomus discolor. Hear Res 181: 27–39PubMedCrossRefGoogle Scholar
  7. Griffin DR (1958) Listening in the dark: the acoustic orientation of bats and men. Yale University Press, New HavenGoogle Scholar
  8. Kleeman L (2004) Advanced sonar with velocity compensation. Int J Rob Res 23(2): 111–126CrossRefGoogle Scholar
  9. Kuc R, Siegel M (1987) Physically based simulation model for acoustic sensor robot navigation. PAMI, 9(6): 766–778CrossRefGoogle Scholar
  10. Maslin GD (1983) A simple ultrasonic ranging system. In: 102nd Convention of AES, CincinattiGoogle Scholar
  11. Moravec H, Elfes A (1985) High resolution maps from wide angle sonar. In: Proc of the IEEE Int Conf on Robotics and Automation, St. LouisGoogle Scholar
  12. Peremans H (1994) A maximum likelihood algorithm for solving the correspondence problem in tri-aural perception. In: Proc IEEE Int Conf on Multisensor Fusion and Integration for Intelligent Systems, Las VegasGoogle Scholar
  13. Peremans H (1997) Broad beamwidth ultrasonic transducers for tri-aural perception. J Acoust Soc Am 102(3): 1567–1572CrossRefGoogle Scholar
  14. Peremans H, Audenaert K, Van Campenhout JM (1993) A high-resolution sensor based on triaural perception. IEEE Trans Robotics and Automation 9(1): 36–48CrossRefGoogle Scholar
  15. Peremans H, Reijniers J (2005) The CIRCE head: a biomimetic sonar system. In: Artificial neural networks: Biological inspirations — ICANN 2005, LNCS 3696, Springer Verlag, BerlinGoogle Scholar
  16. Pollak GD (1988) Time is traded for intensity in the bat’s auditory system. Hear Res 36(2–3): 107–124PubMedCrossRefGoogle Scholar
  17. Reijniers J, Peremans H (2007) Biomimetic sonar system performing spectrum-based localization. IEEE Trans Robotics 23(6): 1151–1159CrossRefGoogle Scholar
  18. Skolnik M (2008) Radar handbook 3rd Ed. McGraw-Hill, New YorkGoogle Scholar
  19. Smith R, Cheeseman P (1986) On the representation and estimation of spatial uncertainty. Int J Rob Res 5(4): 56–68CrossRefGoogle Scholar
  20. Suga N (1990) Cortical computational maps for auditory imaging. Neural networks 3: 3–21CrossRefGoogle Scholar
  21. Surlykke A, Ghose K, Moss C (2009) Acoustic scanning of natural scenes by echolocation in the big brown bat, Eptesicus fuscus. J Exp Biol 212: 1011–1020PubMedCrossRefGoogle Scholar
  22. Surlykke A, Moss CF (2000) Echolocation behavior of big brown bats, Eptesicus fuscus, in the field and the laboratory. J Acoust Soc Am 108: 2419–2429PubMedCrossRefGoogle Scholar
  23. Thrun S, Burgard W, Fox D (1998) A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning and Autonomous Robots (joint issue) 31/5: 1–25Google Scholar

Copyright information

© Springer-Verlag/Wien 2012

Authors and Affiliations

  • Herbert Peremans
    • 1
  • Fons De Mey
  • Filips Schillebeeckx
  1. 1.Active Perception LabUniversity of AntwerpAntwerpenBelgium

Personalised recommendations