Abstract
Research interest in robotic wheelchairs is driven in part by their potential for improving the independence and quality-of-life of persons with disabilities and the elderly. However the large majority of research to date has focused on indoor operations. In this paper, we aim to develop a smart wheelchair robot system that moves independently in outdoor terrain smoothly. To achive this, we propose a robotic wheelchair system that is able to classify the type of outdoor terrain according to their roughness for the comfort of the user and also control the wheelchair movements to avoid drop-off and watery areas on the road. An artificial neural network based classifier is constructed to classify the patterns and features extracted from the Laser Range Sensor (LRS) intensity and distance data. The overall classification accuracy is 97.24 % using extracted features from the intensity and distance data. These classification results can in turn be used to control the motor of the smart wheelchair.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kuno, Y., Shimada, N., Shirai, Y.: Look where you’re going: a robotic wheelchair based on the integration of human and environmental observations. IEEE Robot. Autom. 10(1), 26–34 (2003)
Min, J., Lee, K., Lim, S., Kwon, D.: Human friendly interfaces of wheelchair robotic system for handicapped persons. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS), vol. 2, pp. 1505–1510 (2011)
Satoh, Y., Sakaue, K.: An omnidirectional stereo vision-based smart wheelchair. J. Video Process. 2007, 87646 (2007)
Iwase, T., Zhang, R., Kuno, Y.: Robotic wheelchair moving with the caregiver. In: Proceedings of the SICE-ICASE International Joint Conference 2006, pp. 238–243 (2006)
Suzuki, R., Yamada, T., Arai, M., Sato, Y., Kobayashi, Y., Kuno, Y.: Multiple robotic wheelchair system considering group communication. In: Bebis, G., et al. (eds.) ISVC 2014, Part I. LNCS, vol. 8887, pp. 805–814. Springer, Heidelberg (2014)
Lv, J., et al.: Indoor slope and edge detection by using two-dimensional EKF-SLAM with orthogonal assumption. Int. J. Adv. Robot. Syst. 12, 1–16 (2015)
Yamada, T., Ito, T., Ohya, A.: Detection of road surface damage using mobile robot equipped with 2D laser scanner. In: 2013 IEEE/SICE International Symposium on System Integration (SII). IEEE (2013)
Hamedi, M., et al.: Comparison of different time-domain feature extraction methods on facial gestures’ EMGs. In: Electromagnetics Research Symposium Proceedings, PIER, KL (2012)
Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)
Hemachandra, S., Kollar, T., Roy, N., Teller, S.: Following and interpreting narrated guided tours. In: Proceedings of the International Conference on Robotics and Automation (ICRA), Shanghai, China, May 2011
Bostelman, R., Albus, J.: Sensor experiments to facilitate robot use in assistive environments. In: Proceedings of the International Conference on Pervasive Technologies Related to Assistive Environments, Athens, Greece, July 2008
Xu, J., Grindle, G., Salatin, B., Ding, D., Cooper, R.A.: Manipulability evaluation of the personal mobility and manipulation appliance (permma). In: International Symposium on Quality of Life Technology, Las Vegas, United States, June 2010
Montella, C., Pollock, M., Schwesinger, D., Spletzer, J.: Stochastic classification of urban terrain for smart wheelchair navigation. In: Proceedings of the IROS Workshop on Progress, Challenges and Future Perspectives in Navigation and Manipulation Assistance for Robotic Wheelchairs (2012)
Meyers, A.R., Anderson, J.J., Miller, D.R., Schipp, K., Hoenig, H.: Barriers, facilitators, and access for wheelchair users: sbstantive and methodologic lessons from a pilot study of environmental effects. Soc. Sci. Med. 55(8), 1435–1446 (2002)
Kaiser, M.S., et al.: A neuro-fuzzy control system based on feature extraction of surface electromyogram signal for solar-powered wheelchair. J. Cogn. Comput. 8(1), 1–9 (2016)
Mamun, S.A., Kaiser, M.S., Ahmed, M.R., Islam, M.S., Islam, M.I.: Performance analysis of optical wireless communication system employing neuro-fuzzy based spot-diffusing techniques. J. Commun. Netw. 5(3), 260–265 (2013)
Acknowledgments
This work was supported by Saitama Prefecture Leading-edge Industry Design Project and JSPS KAKENHI Grant Number 26240038.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Mamun, S.A., Suzuki, R., Lam, A., Kobayashi, Y., Kuno, Y. (2016). Terrain Recognition for Smart Wheelchair. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-42297-8_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42296-1
Online ISBN: 978-3-319-42297-8
eBook Packages: Computer ScienceComputer Science (R0)