Use of the Analytical Hierarchy Process to Determine the Steering Direction for a Powered Wheelchair

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Intelligent Systems and Applications (IntelliSys 2020)

Abstract

The Analytical Hierarchy Process (AHP) is utilized to propose a driving course for a powered-wheelchair. A safe route for a wheelchair is proposed by a decision-making system that aims to avoid obstacles. Two ultrasonic transceivers are fitted onto a wheelchair. The area in front of a wheelchair is segmented to left and right zones. The system inputs are distance to an object from the midpoint of the chair, distance to an object from the left of the chair and distance to an object from the right of the chair. The resulting route is a blend between a provided direction from a user’s input device and a proposed direction from the decision-making system that steers a powered-wheelchair to safely avoid obstacles in the way of the wheelchair. The system helps a disabled user to navigate their wheelchair by deciding on a direction that is a compromise between a direction provided by the sensors and a direction desired by the driver. Sensitivity analysis investigates the effects of risk and uncertainty on the resulting directions. An appropriate direction is identified but a human driver can over-ride the decision if necessary.

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References

  1. Parhi, D.R., Singh, M.K.: Rule-based hybrid neural network for navigation of a wheelchair. Proc. IMechE Part B J. Eng. Manuf. 224, 11103–11117 (2009)

    Google Scholar 

  2. Sanders, D.A., Gegov, A., Ndzi, D.: Knowledge-based expert system using a set of rules to assist a tele-operated mobile robot. In: Bi, Y., Kapoor, S., Bhatia, R. (eds.) Studies in Computational Intelligence, vol. 751, pp. 371–392. Springer (2018)

    Google Scholar 

  3. Sanders, D.A., et al.: Rule-based system to assist a tele-operator with driving a mobile robot. In: Lecture Notes in Networks and Systems, vol. 16, pp. 599–615. Springer (2018)

    Google Scholar 

  4. Sanders, D., Langner, M., Bausch, N., Huang, Y., Khaustov, S.A., Simandjuntak, S.: Improving human-machine interaction for a powered wheelchair driver by using variable-switches and sensors that reduce wheelchair-veer. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) Advances in Intelligent Systems and Computing, vol. 1038, pp. 1173–1191. Springer, Cham (2019)

    Google Scholar 

  5. Okonor, O.M., Gegov, A., Adda, M., Sanders, D., Haddad, M.J.M., Tewkesbury, G.: Intelligent approach to minimizing power consumption in a cloud-based system collecting sensor data and monitoring the status of powered wheelchairs. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) Advances in Intelligent Systems and Computing, vol. 1037, pp. 694–710. Springer, Cham (2019)

    Google Scholar 

  6. Sanders, D., Okonor, O.M., Langner, M., Hassan Sayed, M., Khaustov, S.A., Omoarebun, P.O.: Using a simple expert system to assist a powered wheelchair user. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) Advances in Intelligent Systems and Computing, vol. 1037, pp. 662–679. Springer, Cham (2019)

    Google Scholar 

  7. Bausch, N., Shilling, P., Sanders, D., Haddad, M.J.M., Okonor, O.M., Tewkesbury, G.: Indoor location and collision feedback for a powered wheelchair system using machine learning. In: 2019 IEEE SAI Intelligent Systems Conference. Advances in Intelligent Systems and Computing, London, United Kingdom, 5 September 2019, vol. 1, pp. 721–739. Springer (2019)

    Google Scholar 

  8. Tewkesbury, G., Sanders, D., Haddad, M.J.M., Bausch, N., Gegov, A., Okonor, O.M.: Task programming methodology for powered wheelchairs. In: 2019 IEEE SAI Intelligent Systems Conference. Advances in Intelligent Systems and Computing, London, United Kingdom, 5 September 2019, vol. 1, pp. 711–720. Springer (2019)

    Google Scholar 

  9. Sanders, D., Tewkesbury, G., Parchizadeh, H., Robertson, J.J., Omoarebun, P.O., Malik, M.: Learning to drive with and without intelligent computer systems and sensors to assist. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Advances in Intelligent Systems and Computing, vol. 868, pp. 1171–1181. Springer, Cham (2019)

    Google Scholar 

  10. Sanders, D., Gegov, A., Haddad, M., Ikwan, F., Wiltshire, D., Tan, Y.C.: A rule-based expert system to decide on direction and speed of a powered wheelchair. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Advances in Intelligent Systems and Computing, vol. 868, pp. 822–838. Springer, Cham (2019)

    Google Scholar 

  11. Haddad, M., Sanders, D., Bausch, N., Tewkesbury, G., Gegov, A., Hassan Sayed, M.: Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Advances in Intelligent Systems and Computing, vol. 868, pp. 1303–1316. Springer, Cham (2019)

    Google Scholar 

  12. Parhi, D.R., et al.: The stable and precise motion control for multiple wheelchairs. Appl. Soft Comput. 9(2), 477–487 (2009)

    Google Scholar 

  13. Nguyen, V., et al.: Strategies for human - machine interface in an intelligent wheelchair. In: 35th Annual International Conference of IEEE Engineering in Medicine & Biology Society Conference Proceedings, (EMBC), Osaka, Japan, pp. 3638–3641 (2013)

    Google Scholar 

  14. Haddad, M.J.M., Sanders, D., Gegov, A., Hassan Sayed, M., Huang, Y., Al-Mosawi, M.: Combining multiple criteria decision making with vector manipulation to decide on the direction for a powered wheelchair. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) Advances in Intelligent Systems and Computing, vol. 1037, pp. 680–693. Springer, Cham (2019)

    Google Scholar 

  15. Haddad, M.J.M., Sanders, D., Tewkesbury, G., Gegov, A., Hassan Sayed, M., Ikwan, F.C.: Initial results from using Preference Ranking Organization METHods for Enrichment of Evaluations to help steer a powered wheelchair. In: Bi, Y., Bhatia, R., Kapoor, S. (eds.) Advances in Intelligent Systems and Computing, vol. 1037, pp. 648–661. Springer, Cham (2019)

    Google Scholar 

  16. Sanders, D., Wang, Q., Bausch, N., Huang, Y., Khaustov, S.A., Popov, I.: A method to produce minimal real time geometric representations of moving obstacles. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Advances in Intelligent Systems and Computing, vol. 868, pp. 881–892. Springer, Cham (2019)

    Google Scholar 

  17. Sanders, D., Gegov, A., Tewkesbury, G., Khusainov, R.: Sharing driving between a vehicle driver and a sensor system using trust-factors to set control gains. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Advances in Intelligent Systems and Computing, vol. 868, pp. 1182–1195. Springer, Cham (2019)

    Google Scholar 

  18. Sanders, D.A., et al.: Results from investigating powered wheelchair users learning to drive with varying levels of sensor support. In: Proceedings of the SAI Intelligent System, London, U.K. (2017)

    Google Scholar 

  19. Song, K.T., Chen, C.C.: Application of asymmetric map** for wheelchair navigation using ultrasonic sensors. J. Intell. Wheelchair Syst. 17(3), 243–264 (1996)

    Google Scholar 

  20. Sanders, D., Langner, M., Tewkesbury, G.: Improving wheelchair- driving using a sensor system to control wheelchair-veer and variable-switches as an alternative to digital-switches or joysticks. Ind. Robot Int. J. 37(2), 151–167 (2010)

    Google Scholar 

  21. Lee, S.: Use of infrared light reflecting landmarks for localization. Ind. Robot Int. J. 36(2), 138–145 (2009)

    Google Scholar 

  22. Sanders, D., Stott, I.: A new prototype intelligent mobility system to assist powered wheelchair users. Ind. Robot 26(6), 466–475 (2009)

    Google Scholar 

  23. Larsson, J., Broxvall, M., Saffiotti, A.: Laser-based corridor detection for reactive navigation. Ind. Robot Int. J. 35(1), 69–79 (2008)

    Google Scholar 

  24. Milanes, V., Naranjo, J., Gonzalez, C.: Autonomous vehicle based in cooperative GPS and inertial systems. Robotica 26, 627–633 (2008)

    Google Scholar 

  25. Sanders, D.A.: Controlling the direction of walkie type forklifts and pallet jacks on slo** ground. Assem. Autom. 28(4), 317–324 (2008)

    Google Scholar 

  26. Sanders, D.: Recognizing shipbuilding parts using artificial neural networks and Fourier descriptors. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 223(3), 337–342 (2009)

    Google Scholar 

  27. Chang, Y.C., Yamamoto, Y.: On-line path planning strategy integrated with collision and dead-lock avoidance schemes for wheeled wheelchair in indoor environments. Ind. Robot Int. J. 35(5), 421–434 (2008)

    Google Scholar 

  28. 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)

    Google Scholar 

  29. Ishizaka, A., Siraj, S.: Are multi-criteria decision-making tools useful? An experimental comparative study of three methods. EJOR 264, 462–471 (2018)

    MathSciNet  MATH  Google Scholar 

  30. 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. 25(8), 1221–1229 (2017)

    Google Scholar 

  31. Sanders, D.A., et al.: Tele-operator performance and their perception of system time lags when completing mobile robot tasks. In: Proceedings of the 9th International Conference on Human Systems Interaction, pp. 236–242 (2016)

    Google Scholar 

  32. Raju, K., Kumar, D.: Irrigation planning using genetic algorithms. Water Resour. Manag. 18, 163–176 (2004)

    Google Scholar 

  33. Haddad, M., Sanders, D., Bausch, N.: Selecting a robust decision making method to evaluate employee performance. Int. J. Manag. Decis. Making 8(4), 333–351 (2019)

    Google Scholar 

  34. Saaty, T.L.: Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1(1), 83–98 (2008)

    Google Scholar 

  35. Ishizaka, A., Labib, A.: Analytic hierarchy process and expert choice: benefits and limitations. Or Insight 22(4), 201–220 (2009)

    Google Scholar 

  36. Gegov, 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)

    MathSciNet  MATH  Google Scholar 

  37. Sanders, D.A., et al.: A rule-based expert system to decide on direction and speed of a powered wheelchair. In: IEEE Proceedings of the SAI Conference on Intelligent Systems, London, U.K., pp. 426–433 (2018)

    Google Scholar 

  38. Haddad, M., Sanders, D.: The behavior of three discrete multiple criteria decision making methods in the presence of uncertainty. Oper. Res. Perspect., to be published

    Google Scholar 

  39. Haddad, M.J.M., Sanders, D., Bausch, N.: Selecting a robust decision making method to evaluate employee performance. Int. J. Manag. Decis. Making 18(4), 333–351 (2019)

    Google Scholar 

  40. Haddad, M.J.M., Sanders, D.: Selecting a best compromise direction for a powered wheelchair using PROMETHEE. IEEE Trans. Neural Syst. Rehabil. Eng. 27(2), 228–235 (2019). https://doi.org/10.1109/TNSRE.2019.2892587

    Article  Google Scholar 

  41. Sanders, D., Robinson, D.C., Hassan Sayed, M., Haddad, M.J.M., Gegov, A., Ahmed, N.: Making decisions about saving energy in compressed air systems using ambient intelligence and artificial intelligence. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Advances in Intelligent Systems and Computing, vol. 869, pp. 1229–1236. Springer, Cham (2019)

    Google Scholar 

  42. Sanders, D., Gegov, A.: Using artificial intelligence to share control of a powered-wheelchair between a wheelchair user and an intelligent sensor system. EPSRC project 2019–2022 (2018)

    Google Scholar 

  43. Sanders, D.A.: The modification of pre-planned manipulator paths to improve the gross motions associated with the pick and place task. Robotica 13, 77–85 (1995)

    Google Scholar 

  44. Sanders, D.A.: Viewpoint - force sensing. Ind. Robot 34, 177 (2007)

    Google Scholar 

  45. 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)

    Google Scholar 

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Acknowledgment

Research in this paper was funded by EPSRC grant EP/S005927/1 and supported by The Chailey Heritage Foundation and the University of Portsmouth.

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Correspondence to Malik Haddad .

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Haddad, M. et al. (2021). Use of the Analytical Hierarchy Process to Determine the Steering Direction for a Powered Wheelchair. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_46

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