Skip to main content

A New Criterion of Human Comfort Assessment for Wheelchair Robots by Q-Learning Based Accompanist Tracking Fuzzy Controller

Abstract

Wheelchair users often struggle to drive safely and accompanied by caregivers. Wheelchair robots can employ the autonomous functions to avoid obstacles and reduce the workload of the caregivers. To achieve the goal, the accompanist needs to be steadily recognized and tracked by the robots. By using Q-learning based accompanist tracking fuzzy controller, wheelchair robots can peacefully follow the accompanist. Meanwhile, it is important to make the people on wheelchair robots feel comfortable. Based on ISO 2631-1, the ride qualities are obtained by averaging the acceleration data in the frequency bands, and the critical thresholds utilizing to assess the ride comfort are also determined inside. To make the level of bumpiness be as multiple as possible, four kinds of pavements are chosen in the experiments. The results show that the feelings of the occupants on the wheelchair robot are quite different from the ride comfort defined in ISO 2631-1, so a new standard is proposed to assess the ride comfort in this study. The accuracy of the proposed standard is 90.67 %, which is higher than that of ISO 2631-1, 42.48 %. Furthermore, to the best of our knowledge, this paper is thought to be the first one to present the ISO 2631-1-based comfort criterion for wheelchair robots.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

References

  1. Jung, E.-J., Lee, J.-H., Yi, B.-J., Noh, S.-T.: Development of a laser-range-finder-based human tracking and control algorithm for a marathoner service robot. IEEE/ASME Trans. Mechatron. 19, 1963–1976 (2014)

    Article  Google Scholar 

  2. Wu, B.-F., Jen, C.-L., Tsou, T.-Y., Chen, P.-Y.: Accompanist recognition and tracking for intelligent wheelchairs. In: Proceedings of IEEE Systems, Man, and Cybernetics (SMC), San Diego, USA, pp. 2138–2143 (2014)

  3. Deng, R.-W., Wang, Y.-H., Lin, C.-J., Li, T.-H.: Implementation of human following mission by using fuzzy head motion control and Q-learning wheel motion control for home service robot. In: Proceedings of International Conference on Fuzzy Theory and Its Applications, Taipei, Taiwan, pp. 55–60 (2013)

  4. Sugano, T., Dan, Y., Okajima, H., Matsunaga, N., Zhencheng, H.: Platoon driving experiment of electric wheelchair in narrow space by using model error compensator. In: International Conference on Advanced Mechatronic Systems, pp. 324–329 (2014)

  5. Wu, B.-F., Jen, C.-J., Huang, T.-W.: Intelligent radio based positioning and fuzzy based navigation for robotic wheelchair with wireless local area networks. In: Proceedings of IEEE International Conference on Robot, Vision and Signal Processing, Taiwan, pp. 61–64 (2011)

  6. Lin, C.-T., Chiang, H.-H., Lee, T.-T.: A practical fuzzy controller with Q-learning approach for the path tracking of a walking-aid robot. In: Proceedings of SICE Annual Conference, Nagoya, Japan, pp. 888–893 (2013)

  7. Liu, J., Laghrouche, S., Wack, M.: Adaptive-gain second-order sliding mode observer design for switching power converters. Control Engineering Practice, pp. 124–131 (2014)

  8. Laghrouche, S., Liu, J., Ahmed, F., Harmouche, M., Wack, M.: Adaptive second-order sliding mode observer-based fault reconstruction for PEM fuel cell air-feed system. IEEE Transactions on Control System Technology, pp. 1098–1109 (2015)

  9. Huang, J.-J., Chiang, H.-H., Lee, L.T.-T., Kou, K.-Y.: Riding comfort improvement by considering passenger’s behavior suppression on powered wheelchairs. In: Proceedings of International Conference on System Science and Engineering, Dalian, China, pp. 131–136 (2012)

  10. Seki, H., Sugimoto, T., Tadakuma, S.: Novel straight road driving control of power assisted wheelchair based on disturbance estimation and minimum jerk control. In: IAS, pp. 1711–1717 (2005)

  11. Fujimoto, Y., Murakami, T.: A realization of wheelchair pushing operation considering high tracking performance and ride quality improvement by mobile manipulator. In: Proceedings of IEEE International Workshop on Advanced Motion Control, Nagaoka, Japan, pp. 732–737 (2010)

  12. Terashima, K., Miyoshi, T., Urbano, J., Kitagawa, H.: Velocity control of an omni-directional wheelchair considering user’s comfort by suppressing vibration. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems, Alberta, Canada, pp. 3169–3174 (2005)

  13. Solea, R., Nunes, U.: Robotic wheelchair trajectory control considering user comfort: modeling and experimental evaluation. In: Proceedings of International Conference on Informatics in Control, Automation and Robotics, Funchal (2008)

  14. Yamagamei, D. et al.: Wheelchair driving control with passenger’s posture behavior suppression and evaluation of comfort of ride by emotional sweating. In: Proceedings of International Conference on Advanced Computer Control, pp. 30–35 (2008)

  15. Kawaguchi, A. et al.: A study on the safety and drivability enhancement of mobile wheelchairs. In: Proceedings of International Conference on Computing Engineering and Information, pp. 384–387 (2009)

  16. Yamashita, K., Noda, Y., Miyoshi, T., Terashima, K.: Tailor-made modeling and sway control of human posture riding on electrical wheelchair for comfort driving. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, pp. 6034–6039 (2010)

  17. Chiang, H.-H., Wu, S.-J., Perng, J.-W., Wu, B.-F., Lee, T.-T.: The human-in-the-loop design approach to the longitudinal automation system for an intelligent vehicle. IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum. 40(4), 708–720 (2010)

    Article  Google Scholar 

  18. International organization for standardization, ISO 2631-1:1997, Mechanical vibration and shock—evaluation of human exposure to whole-body vibration—Part 1: General requirements (1997)

  19. Leishman, F., Monfort, V., Horn, O., Bourhis, G.: Driving assistance by deictic control for a smart wheelchair: the assessment issue. IEEE Trans. Hum. Mach. Syst. 44, 66–77 (2014)

    Article  Google Scholar 

  20. Morales, Y., Even, J., Watanabe, A., Kondo, T., Hagita, N. et al.: Modeling of human velocity habituation for a robotic wheelchair. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3284–3290 (2014)

  21. Wu, B.-F., Tseng, P.-Y., Jen, C.-L., Tsou, T.-Y., Hsiao, K.-T.: Adaptive online learning for human tracking. In: Proceedings of Automatic Control Conference (CACS), Nantou, Taiwan, pp. 152–157 (2013)

  22. Wu, B.-F., Jen, C.-L., Jen, T.-Y., Tseng, P.-Y., Hsiao, K.-T.: RGB-D sensor based SLAM and human tracking with Bayesian framework for wheelchair robots. In: International Conference on Advanced Robotics and Intelligent Systems, pp. 110–115 (2013)

  23. Sutton, R.S., Barto, A.G.: Reinforcement Learning. MIT Press, Cambridge (1998)

    Google Scholar 

  24. Lockery, D., Peters, J.-F.: Adaptive learning by a target-tracking system. Int. J. Intell. Comput. Cybern. 1, 46–68 (2008)

    MathSciNet  Article  Google Scholar 

  25. Talabi, A.-A., Schwartz, H.-M.: A two stage learning technique using PSO-based FLC and QFIS for the pursuit evasion differential game. In: IEEE International Conference of Mechatronics and Automation (ICMA), pp. 762–769 (2014)

  26. Juang, C.-F., Hsu, C.-H.: Reinforcement ant optimized fuzzy controller for mobile–robot wall-following control. IEEE Trans. Ind. Electron. 56(10), 3931–3940 (2009)

    Article  Google Scholar 

  27. Glorennec, P.-Y., Jouffe, L.: Fuzzy Q-learning. In: Proceedings of IEEE International Conference on Fuzzy System, pp. 659–662 (1997)

  28. Wang, Y., Lang, H., De Silva, C.W.: A hybrid visual servo controller for robust grasping by wheeled mobile robots. IEEE/ASME Trans. Mechatron. 15(5), 757–769 (2010)

    Article  Google Scholar 

  29. Chen, P.-Y., Wu, B.-F., Hsiao, K.-T., Chao, C.-C.: A Q-learning based accompanist tracking fuzzy logic controller for wheelchair robots. In: Proceedings of 2015 National Symposium on System Science and Engineering, Taipei (2015)

  30. Wu, B.-F., Jen, C.-L., Tsou, T.-Y., Li, W.-F., Tseng, P.-Y.: Accompanist detection and following for wheelchair robots with fuzzy controller. In: Proceedings of ICAMechS, Tokyo, Japan (2012)

  31. Chen, J.C., Chang, W.R., Shih, T.S., Chen, C.J., Chang, W.P., Dennerlein, J.T., Ryan, L.M., Christiani, D.C.: Predictors of whole-body vibration levels among urban taxi drivers. Ergonomics, pp. 1075–1090 (2003)

  32. Mansfield, N.J., Griffin, M.J.: Effects of posture and vibration magnitude on apparent mass and pelvis rotation during exposure to whole-body vertical vibration. J. Sound Vib. 93–107 (2002)

  33. Griffin, M.J.: General hazards: vibration, “Encyclopedia of Occupational Health and Safety”. In: International Labour Organization Geneva, pp. 50.2–50.15 (1998)

  34. Chen, H.C. et al.: Whole-body vibration exposure experienced by motorcycle riders. In: Proceedings of the 15th Annual conference of the Ergonomics Society of Taiwan (2008)

  35. Whole-body vibration exposure experienced by motorcycle riders—an evaluation according to ISO 2631-1 and ISO 2631-5 standards (2008)

  36. Cann, A.P., Salmoni, A.W., Eger, T.R.: Predictors of whole-body vibration exposure experienced by highway transport truck operators. Ergonomics, pp. 1432–1453 (2004)

  37. Rehn, B., Lundström, R., Nilsson, L., Liljelind, I., Jörvholm, B.: Variation in exposure to whole-body vibration for operators of forwarder vehicles—aspects on measurement strategies and prevention. Int. J. Ind. Ergon, 831–842 (2005)

  38. Hoy, J., Mubarak, N., Nelson, S., Sweerts, de Landas M., Magnusson, M., Okunribido, O., Pope, M.: Whole-body vibration and posture as risk factors for low back pain among forklift truck drivers. J. Sound Vib. 933–946 (2005)

  39. Scarlett, A.J., Price, J.S., Stayner, R.M.: Whole-body vibration: evaluation of emission and exposure levels arising from agricultural tractors. J. Terramech. 665–673 (2007)

  40. Eger, T., Stevenson, J., Boileau, P.-É., Salmoni, A., VibRG: Predictions of health risks associated with the operation of load-haul-dump mining vehicles: part 1—analysis of whole-body vibration exposure using ISO 2631-1 and ISO 2631-5 standards. Int. J. Ind. Ergon. 726–738 (2008)

  41. Hsu, C.-F., Lin, C.-M., Li, M.-C.: Adaptive dynamic RBF fuzzy neural controller design with a constructive learning. Int. J. Fuzzy Syst. 175–184 (2011)

  42. Lin, C.-M., Hsu, C.-F., Chen, T.-Y.: Adaptive fuzzy total sliding-mode control of unknown nonlinear systems. Int. J. Fuzzy Syst. 434–443 (2012)

  43. Lee, R., et al.: Intelligent navigation and micro-spectrometer content inspection system for a homecare mobile robot. Int. J. Fuzzy Syst. 389–399 (2014)

  44. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)

  45. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Japan, pp. 39–43 (1995)

Download references

Acknowledgments

This work was supported by Ministry of Science and Technology under Grant No. MOST 103-2221-E-009-186.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Po-Yen Chen.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, BF., Chen, PY. & Lin, CH. A New Criterion of Human Comfort Assessment for Wheelchair Robots by Q-Learning Based Accompanist Tracking Fuzzy Controller. Int. J. Fuzzy Syst. 18, 1039–1053 (2016). https://doi.org/10.1007/s40815-015-0120-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-015-0120-6

Keywords

  • Human following
  • ISO 2631-1
  • Fuzzy controller
  • Comfort evaluation
  • Wheelchair robots