Abstract
The use of a fixed Continuous Transmitted Frequency Modulate (CTFM) Ultrasonic sensor for robot navigation has advantages and presents many challenges, least of which is real-time environment perception and environment feature tracking. This requires timely and robust signal data classification algorithms. The work described here explores the adaptation and use of a simple classification separate and conquer decision list algorithm. The algorithm is able to deal with preceptor signal data sequentially and can provide real-time feature classification. The classification algorithm success rate of 82% in this experiment is adequate for feature tracking by an autonomous navigator translating at low speed. This work demonstrates the viability of simple classification techniques for environment feature tracking based on few geometric attributes perceived with a CTFM sensor.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Antoun, S.: Biologically inspired perception for robotics in hostile environments. Biol. Inspired Cogn. Arch. 16, 1–7 (2016)
Antoun, S.M., McKerrow, P.J.: Perceiving a corridor with CTFM ultrasonic sensing. In: Dunbabin, M., Srinivasan, M (eds.) Proceedings of the Australasian Conference on Robotics and Automation 2007 (ACRAA’07). Australian Robotics & Automation Association, Brisbane (2007)
Antoun, S.: Sensing for autonomous navigation by identifying and tracking multiple continuous and discontinuous reflectors in each echo of a mono-aural CTFM ultrasonic sensor. University of Wollongong Thesis Collection 1954–2016 (2012)
Antoun, S.M., McKerrow, P.J.: Issues in wall tracking With a CTFM ultrasonic sensor. IEEE Sens. J. 13, 4671–4681 (2013). https://doi.org/10.1109/JSEN.2013.2272635
Giusti, A., Guzzi, J., Cirean, D.C., He, F.L., Rodrguez, J.P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Caro, G.D., Scaramuzza, D., Gambardella, L.M.: A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robot. Autom. Lett. 1, 661–667 (2016). https://doi.org/10.1109/LRA.2015.2509024
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11(1), 63–90 (1993)
Kay, L.: Electronic aids for blind persons: an interdisciplinary subject. IEEE Rev. Part A 131A, 559–76 (1984)
Kay, L.: Auditory perception of objects by blind persons, using bio-acoustic high resolution air sonar. J. Acoust. Soc. Am. 11076, 3266–3275 (June 2000)
Kinsler, L.E., Frey, A.R., Coppens, A.B., Sanders, J.V.: Fundamentals of acoustics. In: Kinsler, L.E., Frey, A.R., Coppens, A.B., Sanders, J.V. (eds.) Fundamentals of Acoustics, 4th ed, p. 560. ISBN 0-471-84789-5. Wiley-VCH, December 1999 (1999)
Liu, Y., Li, S., Xia, Z.: Path planning efficiency maximization for ball-picking robot using machine learning algorithm. In: 2015 International Conference on Intelligent Transportation, Big Data and Smart City. Presented at the 2015 International Conference on Intelligent Transportation, Big Data and Smart City, pp. 551–555 (2015). https://doi.org/10.1109/ICITBS2015.141
Martnez Rojas, J., Alpuente, J., Montero, R., Esp, P.: Physical analysis of several organic signals for human echolocation: oral vacuum pulses. Acta Acust. United Acust. 95, 325–330 (2009). https://doi.org/10.3813/AAA.918155
Terlau, T.: University of Louisville, Louisville, Kentucky Copyright 2008, American Printing House for the Blind, Inc. All rights reserved (2008)
Yu, L., Long, Z., Xi, N., Jia, Y., Ding, C.: Local path planning based on ridge regression extreme learning machines for an outdoor robot. In: 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 745–750. https://doi.org/10.1109/ROBIO2015.7418858 (2015)
Walas, K.: Terrain classification and negotiation with a walking robot. J. Intell. Robot. Syst. 78, 401–423 (2015). https://doi.org/10.1007/s10846-014-0067-0
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques, pp. 85–116. Morgan Kaufmann, Los Altos, CA (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Antoun, S.M. (2019). Mining CTFM Echo Signal Data for Navigation. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_89
Download citation
DOI: https://doi.org/10.1007/978-3-030-01057-7_89
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01056-0
Online ISBN: 978-3-030-01057-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)