Acoustic Masking of a Stealthy Outdoor Robot Tracking a Dynamic Target

Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 88)


This work is motivated by the desire to covertly track mobile targets, either animal or human, in previously unmapped outdoor natural environments using off-road robotic platforms with a non-negligible acoustic signature. The use of robots for stealthy surveillance is not new. Many studies exist but only consider the navigation problem to maintain visual covertness. However, robotic systems also have a significant acoustic footprint from the onboard sensors, motors, computers and cooling systems, and also from the wheels interacting with the terrain during motion. All these can jepordise any visual covertness. In this work, we experimentally explore the concepts of opportunistically utilizing naturally occurring sounds within outdoor environments to mask the motion of a robot, and being visually covert whilst maintaining constant observation of the target. Our experiments in a constrained outdoor built environment demonstrate the effectiveness of the concept by showing a reduced acoustic signature as perceived by a mobile target allowing the robot to covertly navigate to opportunistic vantage points for observation.


acoustic covert stealth robot tracking 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Birgersson, E., Howard, A., Sukhatme, G.S.: Towards stealthy behaviors. In: Proceedings of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), vol. 2, pp. 1703–1708 (October 2003)Google Scholar
  2. 2.
    Chu, S., Narayanan, S., Jay Kuo, C.-C.: Environmental sound recognition using mp-based features. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, pp. 1–4 (April 2008)Google Scholar
  3. 3.
    Cook, D.J., Gmytrasiewicz, P., Holder, L.B.: Decision-theoretic cooperative sensor planning. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(10), 1013–1023 (1996)CrossRefGoogle Scholar
  4. 4.
    Lee, K., Ellis, D.P.W., Loui, A.C.: Detecting local semantic concepts in environmental sounds using markov model based clustering. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), March 2010, pp. 2278–2281 (2010)Google Scholar
  5. 5.
    Martinson, E.: Hiding the acoustic signature of a mobile robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007, pp. 985–990 (November 2007)Google Scholar
  6. 6.
    Marzouqi, M., Jarvis, R.: Covert robotics: Covert path planning in unknown environments. In: Proceedings of Australasian Conference on Robotics and Automation (ACRA 2004) (December 2004)Google Scholar
  7. 7.
    Masoud, A.A.: Evasion of multiple, intelligent pursuers in a stationary, cluttered environment using a poisson potential field. In: Proceedings of IEEE International Conference on Robotics and Automation, ICRA 2003, vol. 3, pp. 4234–4239 (September 2003)Google Scholar
  8. 8.
    Tews, A., Mataric, M.J., Sukhatme, G.S.: Avoiding detection in a dynamic environment. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2004, vol. 4, pp. 3773–3778 (September-October 2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Autonomous Systems LaboratoryCSIRO ICT CentreKenmoreAustralia

Personalised recommendations