Adjustment of Tele-Operator Learning When Provided with Different Levels of Sensor Support While Driving Mobile Robots

  • David SandersEmail author
  • David Ndzi
  • Simon Chester
  • Manish Malik
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)


A quantitative and qualitative empirical evaluation is presented to show the effect of providing some sensor support during driving lessons as a tele-operator learns to drive a mobile robot. Different levels of sensor support were provided and the effect on training was measured. Different levels of force feedback were provided through a joystick. Results are presented and conclusions drawn about the way that tele-operators behave during driving tasks.


Learning mobile robot sensor Tele-operation ultrasonic 


  1. 1.
    Chikura, D., Takahashi, M., Watanabe, S., Kitamura, M.: Adaptation of user behavior to the different level of tele-operation support. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 3, pp. 739–744 (1999)Google Scholar
  2. 2.
    Sheridan, T.B.: Telerobotics, Automalion and Human Supervisory Control. MIT Press, Cambridge (1992)Google Scholar
  3. 3.
    Sheridan, T.B.: Human-centered automation: oxymoron or common sense? In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vancouver Canada (1996)Google Scholar
  4. 4.
    Kirlik, A.: Modeling behavior in human-automation interaction: why an “Aid” can (and should) go unused. Hum. Factors 35(2), 221–242 (1993)CrossRefGoogle Scholar
  5. 5.
    Itoh, M., et al.: Experimental study of situation-adaptive human-automation collaboration for takeoff safety. In: Proceedings of the 7th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design and Evaluation of Man-Machine Systems, pp. 371–376 (1998)Google Scholar
  6. 6.
    Sanders, D.A., Graham-Jones, J., Gegov, A.: Improving ability of tele-operators to complete progressively more difficult mobile robot paths using simple expert systems and ultrasonic sensors. Ind. Robot 37(5), 431–440 (2010)CrossRefGoogle Scholar
  7. 7.
    Sanders, D.: Analysis of the effects of time delays on the teleoperation of a mobile robot in various modes of operation. Ind. Robot 36(6), 570–584 (2009)CrossRefGoogle Scholar
  8. 8.
    Sanders, D., Tewkesbury, G., Stott, I.J., et al.: Simple expert systems to improve an ultrasonic sensor-system for a tele-operated mobile-robot. Sens. Rev. 31(3), 246–260 (2011)CrossRefGoogle Scholar
  9. 9.
    Sanders, D.: Comparing ability to complete simple tele-operated rescue or maintenance mobile-robot tasks with and without a sensor system. Sens. Rev. 30(1), 40–50 (2010)CrossRefGoogle Scholar
  10. 10.
    Sanders, D.: Controlling the direction of “walkie” type forklifts and pallet jacks on sloping ground. Assembly Autom. 28(4), 317–324 (2008)CrossRefGoogle Scholar
  11. 11.
    Sanders, D.A., Stott, I., Robinson, D., Ndzi, D.: Analysis of successes and failures with a tele-operated mobile robot in various modes of operation. Robotica 30, 973–988 (2012)CrossRefGoogle Scholar
  12. 12.
    Backes, P.G.: Supervised Autonomy for Space Robotics. Prog. Astronaut. Aeronaut. 161, 139–158 (1994)Google Scholar
  13. 13.
    Draper, J.V., et al.: Measuring Operator Skill and Teleoprator Performance. In: Proceedings of the International Symposium on Teleoperation and Contro1 (1998)Google Scholar
  14. 14.
    Volpe, R.: Techniques for collision prevention, impact stability, and force control by space robots. Prog. Astronaut. Aeronaut. 161, I75–212 (1994)Google Scholar
  15. 15.
    Gao, W., Hinders, M.: Mobile robot sonar backscatter algorithm for automatically distinguishing walls, fences, and hedges. Int. J. Robot. Res. 25(2), 135–145 (2006)CrossRefGoogle Scholar
  16. 16.
    Stott, I.J., Sanders, D.A., Goodwin, M.J.: A software algorithm for the intelligent mixing of inputs to a tele-operated vehicle. J. Syst. Archit. 43(1–5), 67–72 (1997)CrossRefGoogle Scholar
  17. 17.
    Chester, S., Tewkesbury, G., Sanders, D., et al.: New electronic multi-media assessment system. Web Inf. Syst. Technol. 1, 414–420 (2007)CrossRefGoogle Scholar
  18. 18.
    Chester, S., Tewkesbury, G., Sanders, D., et al.: New electronic multi-media assessment system. In: WEBIST 2006: Proceedings of the Second International Conference on Web Information Systems and Technologies, pp 320–324 (2006)Google Scholar
  19. 19.
    Bergasa-Suso, J., Sanders, D.A., Tewkesbury, G.E.: Intelligent browser-based systems to assist Internet users. IEEE Trans. Educ. 48(4), 580–585 (2005)CrossRefGoogle Scholar
  20. 20.
    Sanders, D.A., Cawte, H., Hudson, A.D.: Modelling of the fluid dynamic processes in a high-recirculation airlift reactor. Int. J. Energy Res. 25(6), 487–500 (2001)CrossRefGoogle Scholar
  21. 21.
    Sanders, D.A., Rasol, Z.: An automatic system for simple spot welding tasks. In: Total Vehicle Technology: Challenging Current Thinking, pp 263–272 (2001)Google Scholar
  22. 22.
    Sanders, D.A., Hudson, A.D.: A specific blackboard expert system to simulate and automate the design of high recirculation airlift reactors. Math. Comput. Simul. 53(1–2), 41–65 (2000)CrossRefGoogle Scholar
  23. 23.
    Sanders, D.A., Hudson, A.D., Tewkesbury, G.E., et al.: Automating the design of high-recirculation airlift reactors using a blackboard framework. Expert Syst. Appl. 18(3), 231–245 (2000)CrossRefGoogle Scholar
  24. 24.
    Tewkesbury, G.E., Sanders, D.A.: A new simulation based robot command library applied to three robots. J. Robot. Syst. 16(8), 461–469 (1999)CrossRefzbMATHGoogle Scholar
  25. 25.
    Sanders, D.A.: Using self-reliance factors to decide how to share control between human powered wheelchair drivers and ultrasonic sensors. IEEE Trans. Neural Syst. Rehabil. Eng. (2016, in Press). doi: 10.1109/TNSRE.2016.2620988
  26. 26.
    Tewkesbury, G.E., Sanders, D.A.: A new robot command library which includes simulation. J. Robot. Syst. 26(1), 39–48 (1999)zbMATHGoogle Scholar
  27. 27.
    Sanders, D.A.: Comparing speed to complete progressively more difficult mobile robot paths between human tele-operators and humans with sensor-systems to assist. Assem. Autom. 29(3), 230–248 (2009)CrossRefGoogle Scholar
  28. 28.
    Sanders, D., Geov, A.: “AI tools for use in Assem. Autom. and some examples of recent applications. Assem. Autom. 33(2), 184–194 (2013)CrossRefGoogle Scholar
  29. 29.
    Sanders, D.A., Tewkesbury, G.E., Ndzi, D., et al.: Improving automatic robotic welding in shipbuilding through the introduction of a corner-finding algorithm to help recognise shipbuilding parts. J. Mar. Sci. Technol. 17(2), 231–238 (2012)CrossRefGoogle Scholar
  30. 30.
    Sanders, D., Lambert, G., Graham-Jones, J., et al.: A robotic welding system using image processing techniques and a CAD model to provide information to a multi-intelligent decision module. Assem. Autom. 30(4), 323–332 (2010)CrossRefGoogle Scholar
  31. 31.
    Sanders, D.A., Tewkesbury, G.E.: A pointer device for TFT display screens that determines position by detecting colours on the display using a colour sensor and an Artificial Neural Network. Displays 30(2), 84–96 (2009)CrossRefGoogle Scholar
  32. 32.
    Sanders, D.A., Tan, Y., Rogers, I., et al.: An expert system for automatic design-for-assembly. Assem. Autom. 29(4), 378–388 (2009)CrossRefGoogle Scholar
  33. 33.
    Sanders, D.A., Lambert, G., Pevy, L.: Pre-locating corners in images in order to improve the extraction of Fourier descriptors and subsequent recognition of shipbuilding parts. Proc. IMechE Part B J. Eng. Manufact. 223(9), 1217–1223 (2009)CrossRefGoogle Scholar
  34. 34.
    Sanders, D.: Controlling the direction of walkie type forklifts and pallet jacks on sloping ground. Assem. Autom. 28(4), 317–324 (2008)CrossRefGoogle Scholar
  35. 35.
    Sanders, D.A.: Progress in machine intelligence. Ind. Robot Int. J. 35(6), 485–487 (2008)CrossRefGoogle Scholar
  36. 36.
    Geov, A., Gobalakrishnan, N., Sanders, D.A.: Rule base compression in fuzzy systems by filtration of non-monotonic rules. J. Intell. Fuzzy Syst. 27(4), 2029–2043 (2014)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • David Sanders
    • 1
    Email author
  • David Ndzi
    • 1
  • Simon Chester
    • 2
  • Manish Malik
    • 1
  1. 1.School of EngineeringPortsmouth UniversityPortsmouthUK
  2. 2.Chester Associates PortsmouthPortsmouthUK

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