Advertisement

Rule-Based System to Assist a Tele-Operator with Driving a Mobile Robot

  • David Adrian SandersEmail author
  • Heather May Sanders
  • Alexander Gegov
  • David Ndzi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)

Abstract

Simple real time AI techniques are presented that support tele-operated mobile robot operators when they are steering. They permit a tele-operator to be included in the steering as much as possible, while offering help when required to avoid obstacles and to reach their target destination. The direction to a destination (via point) becomes an extra input along with the usual inputs from a joystick and an obstacle avoidance sensor system. A recommended direction is suggested and that is mixed with joystick position and angle. A rule-based system provides a suggested angle to turn the robot and that is mixed with input from a joystick to help a tele-operator to steer their mobile robot towards a destination.

Keywords

Tele-operation Mobile robot Assist Rule-based AI Steering Collision avoidance 

References

  1. 1.
    Parhi, D.R., Singh, M.K.: Rule-based hybrid neural network for navigation of a mobile robot. Proc. IMechE Part B J. Eng. Manuf. 224, 11103–11117 (2009)Google Scholar
  2. 2.
    Nguyen, A.V., Nguyen, L.B., Su, S., Nguyen, H.T.: Shared control strategies for human - machine interface in an intelligent robot. In: 35th Annual International Conference of IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Osaka, Japan. IEEE Engineering in Medicine and Biology Society Conference Proceedings, pp. 3638–3641 (2013)Google Scholar
  3. 3.
    Parhi, Z.R., Pradhan, S.K., Panda, A.K., Behra, R.K.: The stable and precise motion control for multiple mobile robots. Appl. Soft Comput. 9(2), 477–487 (2009)CrossRefGoogle Scholar
  4. 4.
    Sanders, D.A., Ndzi, D., Chester, S., Malik, M.: Adjustment of tele-operator learning when provided with different levels of sensor support while driving mobile robots. In: Proceedings of SAI Intelligent Systems Conference 2016, London, UK (2016) (In press)Google Scholar
  5. 5.
    Song, K.T., Chen, C.C.: Application of asymmetric mapping for mobile robot navigation using ultrasonic sensors. J. Intell. Robot. Syst. 17(3), 243–264 (1996)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Huq, R., Mann, G.K.I., Gosine, R.G.: Mobile robot navigation using motor schema and fuzzy context dependent behaviour modulation. Appl. Soft Comput. 8(1), 422–436 (2008)CrossRefGoogle Scholar
  7. 7.
    Begum, M., Mann, G.K.I., Gosine, R.G.: Integrated fuzzy logic and genetic algorithmic approach for simultaneous localization and mapping of mobile robots. Appl. Soft Comput. 8(1), 150–165 (2008)CrossRefGoogle Scholar
  8. 8.
    Bennewitz, M., Burgard, W.: A probabilistic method for planning collision-free trajectories of multiple mobile robots. In: Proceedings of 14th European Conference on AI, Berlin, Germany, pp. 20–25, August 2000, pp. 9-15 (ECAI)Google Scholar
  9. 9.
    Gueaieb, W., Miah, M.S.: An intelligent mobile robot navigation technique using RFID technology. IEEE Trans. Instrum. Meas. 57(9), 1908–1917 (2008)CrossRefGoogle Scholar
  10. 10.
    Hwang, C.L., Chang, N.W.: Fuzzy decentralized sliding-mode control of a car-like mobile robot in distributed sensor-network spaces. IEEE Trans. Fuzzy Syst. 16(1), 97–109 (2008)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Nguyen, A.V., Nguyen, L.B., Su, S., Nguyen, H.T.: The advancement of an obstacle avoidance bayesian neural network for an intelligent robot. In: 35th International Conference of IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Osaka, Japan. IEEE Eng in Medicine and Biology Society Conference Proceedings, pp. 3642–3645 (2013)Google Scholar
  12. 12.
    Sanders, D.A.: Using a self-reliance factor for a disabled driver to decide on the share of combined-control between a powered wheelchair and an ultrasonic sensor system. IEEE Trans. Neural Syst. Rehab. Eng. (In press)Google Scholar
  13. 13.
    Sanders, D., Stott, I., Graham-Jones, J., Gegov, A., Tewkesbury, G.E.: Expert system to interpret hand tremor and provide joystick position signals for tele-operated mobile robots with ultrasonic sensor systems. Ind. Robot. 38(6), 585–598 (2011)CrossRefGoogle Scholar
  14. 14.
    Robinson, D.C., Sanders, D.A., Mazharsolook, E.: Ambient intelligence for optimal manufacturing and energy efficiency. Assem. Autom. 35(3), 234–248 (2015)CrossRefGoogle Scholar
  15. 15.
    Sanders, D.A., Tewkesbury, G., Gegov, A.: Fast transformations to provide simple geometric models of moving objects. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds.) ICIRA 2015. LNCS, vol. 9244, pp. 604–615. Springer, Cham (2015). doi: 10.1007/978-3-319-22879-2_55 CrossRefGoogle Scholar
  16. 16.
    Sanders, D., Langner, M., Tewkesbury, G.: Improving robot- driving using a sensor system to control robot-veer and variable-switches as an alternative to digital-switches or joysticks. Ind. Robot. Int. J. 37(2), 151–167 (2010)CrossRefGoogle Scholar
  17. 17.
    Larsson, J., Broxvall, M., Saffiotti, A.: Laser-based corridor detection for reactive Navigation. Ind. Robot. Int. J. 35(1), 69–79 (2008)CrossRefGoogle Scholar
  18. 18.
    Sanders, D., 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. Int. J. 37(5), 431–440 (2010)CrossRefGoogle Scholar
  19. 19.
    Lee, S.: Use of infrared light reflecting landmarks for localization. Ind. Robot. Int. J. 36(2), 138–145 (2009)CrossRefGoogle Scholar
  20. 20.
    Milanes, V., Naranjo, J., Gonzalez, C., et al.: Autonomous vehicle based in cooperative GPS and inertial systems. Robotica 26, 627–633 (2008)CrossRefGoogle Scholar
  21. 21.
    Sanders, D., Stott, I.: A new prototype intelligent mobility system to assist tele-operated mobile robot users. Ind. Robot. 26(6), 466–475 (2009)CrossRefGoogle Scholar
  22. 22.
    Chang, Y.C., Yamamoto, Y.: On-line path planning strategy integrated with collision and dead-lock avoidance schemes for wheeled mobile robot in indoor environments. Ind. Robot. Int. J. 35(5), 421–434 (2008)CrossRefGoogle Scholar
  23. 23.
    Sanders, D.: Progress in machine intelligence. Ind. Robot. 35(6), 485–487 (2008)CrossRefGoogle Scholar
  24. 24.
    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. Inst. Mech. Eng. Part B J. Eng. Manuf. 223(9), 1217–1223 (2009)CrossRefGoogle Scholar
  25. 25.
    Sanders, D.: 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
  26. 26.
    Sanders, D.: Analysis of the effects of time delays on the tele-operation of a mobile robot in various modes of operation. Ind. Robot. 36(6), 570–584 (2009)CrossRefGoogle Scholar
  27. 27.
    Sanders, D., Gegov, A.: Different levels of sensor support changes the learning behaviour of wheelchair drivers. Assist. Technol. (In press)Google Scholar
  28. 28.
    Sanders, D.A., Bausch, N.: Improving steering of a powered wheelchair using an expert system to interpret hand tremor. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds.) ICIRA 2015. LNCS, vol. 9245, pp. 460–471. Springer, Cham (2015). doi: 10.1007/978-3-319-22876-1_39 CrossRefGoogle Scholar
  29. 29.
    Sanders, D., Stott, I.J., Robinsosn, D.C., et al.: Analysis of successes and failures with a tele-operated mobile robot in various modes of operation. Robotica 30, 973–988 (2012)CrossRefGoogle Scholar
  30. 30.
    Sanders, D.A., Tewkesbury, G.E., 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
  31. 31.
    Sanders, D., Geov, A.: AI tools for use in Assembly Automation and some examples of recent applications. Assem. Autom. 33(2), 184–194 (2013)CrossRefGoogle Scholar
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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 of IMechE Part B J. Eng. Manuf. 223(9), 1217–1223 (2009)CrossRefGoogle Scholar
  38. 38.
    Sanders, D.: Controlling the direction of walkie type forklifts and pallet jacks on sloping ground. Assem. Autom. 28(4), 317–324 (2008)CrossRefGoogle Scholar
  39. 39.
    Sanders, D.A.: Progress in machine intelligence. Ind. Robot. Int. J. 35(6), 485–487 (2008)CrossRefGoogle Scholar
  40. 40.
    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 Adrian Sanders
    • 1
    Email author
  • Heather May Sanders
    • 2
  • Alexander Gegov
    • 3
  • David Ndzi
    • 1
  1. 1.School of EngineeringUniversity of PortsmouthPortsmouthUK
  2. 2.Peter Symonds CollegeWinchesterUK
  3. 3.School of ComputingUniversity of PortsmouthPortsmouthUK

Personalised recommendations