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Advanced Adaptive Sonar for Mapping Applications

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Abstract

An advanced adaptive sonar module is described, capable of being configured to different circumstances and distances according to reflectors found in the environment. Thanks to the sensory distribution, it is possible to identify three basic types of reflector (planes, edges and corners). Furthermore, a heuristic map of the environment is built. The proposed methods have been computationally optimized, and implemented in a real-time system based on a Field-Programmable Gate Array (FPGA) and a Digital Signal Processor (DSP). Results have been obtained in the detection, classification and mapping of obstacles; and finally testing has been carried out on a commercial vehicle.

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References

  1. Borenstein, J., Koren, Y.: Obstacle avoidance with ultrasonics sensors. IEEE J. Robot. Autom. 4(2), 213–218 (1988)

    Article  Google Scholar 

  2. Everett, H.R.: Sensors for Mobile Robot: Theory and Application. A K Peters Ltd., Wellesley, M. A. (1995)

    Google Scholar 

  3. Song, K.T., Chen, C.H., Huang, C.H.C.: Design and experimental study of an ultrasonic sensor system for lateral collision avoidance at low speeds. In: Proc. 2004 IEEE Intelligent Vehicles Symposium (IV’04), pp. 647–652. Parma, Italy (2004)

  4. Del Castillo, G., Skaar, S., Cardenas, A., Fehr, L.: A sonar approach to obstacle detection for a vision-based autonomous wheelchair. Robot. Auton. Syst. 54(12), 967–981 (2006)

    Article  Google Scholar 

  5. Jorg, K., Berg, M.: Mobile robot sonar sensing with pseudo-random codes. In: Proc. 1998 IEEE Conference on Robotics and Automation (ICRA’98), pp. 2807–2812. Leuven, Belgium (1998)

  6. Kuc, R., Barshan, B.: Bat-like sonar for guiding mobile robots. IEEE Control Syst. 23(2), 4–12 (1992)

    Article  Google Scholar 

  7. Jiménez, A.R., Seco, F.: Precise localisation of archaeological findings with a new ultrasonic 3D positioning sensor. Sens. Actuators A, 1234, 224–233 (2005)

    Google Scholar 

  8. Song, K.T., Chen, C.C.: Application of heuristic asymmetric mapping for mobile robot navigation using ultrasonic sensors. J. Intell. Robot. Syst. 17, 243–264 (1996)

    Article  Google Scholar 

  9. Barshan, B., Ayrulu, B.: Performance comparison of four time-of-flight estimation methods for sonar signals. Electron. Lett. 34(16), 1616–1617 (1998)

    Article  Google Scholar 

  10. Peremans, H., Audenaert, K., Van Campenhout, J.: A high resolution sensor based on tri-aural perception. IEEE Trans. Robot. Autom. 9(1), 36–48 (1993)

    Article  Google Scholar 

  11. Borenstein, J., Koren, Y.: Histogramic in-motion mapping for mobile robot obstacle avoidance. IEEE Trans. Robot. Autom. 7(4), 535–539 (1991)

    Article  Google Scholar 

  12. Chong, K.S., Kleeman, L.: Mobile robot map building for an advanced sonar array and accurate odometry. Int. J. Rob. Res. 18(1), 20–36 (1999)

    Google Scholar 

  13. Elfes, A.: Occupancy grids: a stochastic spatial representation for active robot perception. In: Proc. 6th IEEE Conf. on Uncertainty in AI. Cambridge, USA (1990)

  14. Kleeman, L.: Advanced sonar with velocity compensation. Int. J. Rob. Res. 23(2), 111–126 (2004)

    Article  Google Scholar 

  15. Kuc, R.: Pseudo-amplitude scan sonar maps. IEEE Trans. Robot. Autom. 17(5), 767–770 (2001)

    Article  Google Scholar 

  16. Wijk, O., Christensen, H.: Triangulation based fusion of sonar data with application in robot pose tracking. IEEE Trans. Robot. Autom. 16(6), 740–752 (2000)

    Article  Google Scholar 

  17. Choset, H., Nagatini, K., Lazar, N.A.: The arc-transversal median algorithm: a geometric approach to increasing ultrasonic sensor azimuth accuracy. IEEE Trans. Robot. Autom. 19(3), 513–522 (2003)

    Article  Google Scholar 

  18. Benet, G., Martínez, M., Blanes, F., Pérez, P., Simó, J.E.: Differentiating walls from corners using the amplitude of ultrasonic echoes. Robot. Auton. Syst. 50, 13–25 (2005)

    Article  Google Scholar 

  19. Moita, F., Lopes, A.C., Nunes, U.: A fast firing binaural system for ultrasonic pattern recognition. J. Intell. Robot. Syst. 50(2), 141–162 (2007)

    Article  Google Scholar 

  20. Peremans, H.: A maximum likelihood algorithm for solving the correspondence problem in tri-aural perception. In: Proc. 1994 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI’94), pp. 485–492. Las Vegas, USA (1994)

  21. Ureña, J., Mazo, M., García, J.J., Bueno, E., Hernández, A., Hernanz, D.: Low-cost improvement of an ultrasonic sensor and its characterization for map building. In: Proc. IFAC Workshop on Intelligent Components for Vehicles (ICV98), pp. 333–338. Seville, Spain (1998)

  22. Ureña, J., Mazo, M., García, J.J., Hernández, A., Bueno, E.: Classification of reflectors with an ultrasonic sensor for mobile robot applications. Robot. Auton. Syst. 29, 269–279 (1999)

    Article  Google Scholar 

  23. Hernández, A., Ureña, J., García, J.J., Mazo, M., Herranz, D., Dérutin, J.P., Sérot, J.: Ultrasonic ranging sensor using simultaneous emissions from different transducers. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 51(12), 1660–1670 (2004)

    Article  Google Scholar 

  24. Álvarez, F.J., Ureña, J., Mazo, M., Hernández, A., García, J.J., De Marziani, C.: High reliability outdoor sonar prototype based on efficent signal coding. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 53, 1863–1870 (2006)

    Article  Google Scholar 

  25. Barshan, B., Kuc, R.: Differentiating sonar reflections from corners and planes by employing an intelligent sensor. IEEE Trans. Pattern Anal. Mach. Intell. 12(6), 560–568 (1990)

    Article  Google Scholar 

  26. Kleeman, L., Kuc, R.: Mobile robot sonar for target localization and classification. Int. J. Rob. Res. 14(4), 295–318 (1995)

    Article  Google Scholar 

  27. Gao, W., Hinders, M.K.: Mobile robot sonar interpretation algorithm for distinguishing trees from poles. Robot. Auton. Syst. 53(2), 89–98 (2005)

    Article  Google Scholar 

  28. Lee, C., Yan, L., Lee, S.: Estimation of inclination angle of a planar reflector using sonar sensor. IEEE Sens. J 7(7/8), 1052 (2007)

    Article  Google Scholar 

  29. Kleeman, L.: Advanced sonar and odometry error modeling for simultaneous localisation and map building. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 699–704. Las Vegas, USA (2003)

  30. Karaman, O., Temeltas, H.: Comparison of different grid based techniques for real-time map building. In: Proc. 2004 IEEE International Conference on Industrial Technology (ICIT’04), vol. 2 (2004)

  31. Noykov, S., Roumenin, C.: Calibration and interface of a Polaroid ultrasonic sensor for mobile robots. Sens. Actuators, A, Phys. 135(1), 169–178 (2007)

    Article  Google Scholar 

  32. Leonard, J.J., DurrantWhyte, H.F.: Simultaneous map building and localization for an autonomous mobile robot. In: IEEE/RSJ Int. Workshop on Intelligent Robots and Systems (IROS’91), vol. 3, pp. 1442–1447. Osaka, Japan (1991)

  33. Newman, P., Cole, D., Ho, K.: Outdoor SLAM using visual appearance and laser ranging. In: IEEE International Conference on Robotics and Automation (ICRA’06). Orlando, USA (2006)

  34. Davison, A.J., David, W.M.: Simultaneous localization and map-building using active vision. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 865–880 (2002)

    Article  Google Scholar 

  35. Audenaert, K., Peremans, H., Kawahara, Y., Van Campenhout, J.: Accurate ranging of multiple objects using ultrasonic sensors. In: Proc. IEEE Int. Conf. on Robotics and Automation (ICRA’05), pp. 1733–1738. Nice, France (1992)

  36. Graham, P., Nelson, B.: Frequency-domain sonar processing in FPGAs and DSPs. In: Proc. IEEE Symposium on FPGAs for Custom Computing Machines (FCCM’98), pp. 306–307. Napa Valley CA, USA (1998)

  37. Nelson, B.E.: Configurable computing and sonar processing—architectures and implementations. In: Conference Record of the Thirty-Fifth Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 56–60. Pacific Grove CA, USA (2001)

  38. Clarke, C., Qiang, L., Peremans, H., Hernández, A.: FPGA implementation of a neuromimetic cochlea for a bionic bat head. In: Proc. 14th International Conference on Field-Programmable Logic and its applications (FPL’04), pp. 1073–1075. Antwerp, Belgium (2004)

  39. Lee, B.B., Furgason, E.S.: High speed digital Golay code flaw detection system. Ultrasonics 21(4), 153–161 (1983)

    Article  Google Scholar 

  40. Budisin, S.Z.: Efficient pulse compressor for Golay complementary sequences. Electron. Lett. 27(3), 219–220 (1991)

    Article  Google Scholar 

  41. Popovic, B.M.: Efficient Golay correlator. IEE Electron. Lett. 35(17), 1427–1428 (1999)

    Article  Google Scholar 

  42. Polaroid Corporation: 600 series. Instrument grade electrostatic transducers. Technical specification (1999)

  43. Sundance Multiprocessor Technology Ltd.: SMT320v4, SMT358, SMT335-375 User guide. User manual (2003)

  44. Hernández, A., Urena, J., Mazo, M., Jiménez, A., Garcá, J.J., Álvarez, F.J., De Marziani, C., Ochoa, A.M.: A comparison of computing architectures for ultrasonic signal processing. In: Proc. IEEE Int. Symposium on Intelligent Signal Processing (WISP’05). Faro, Portugal (2005)

  45. Robosoft: ROBOSOFT’s intelligent electric vehicles. Product specification (2003)

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Hernández, A., Ureña, J., Mazo, M. et al. Advanced Adaptive Sonar for Mapping Applications. J Intell Robot Syst 55, 81–106 (2009). https://doi.org/10.1007/s10846-008-9291-9

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