Hostname: page-component-76d6cb85b7-lcgwf Total loading time: 0 Render date: 2026-07-13T21:38:52.101Z Has data issue: false hasContentIssue false
  • English
  • Français

Robotic wheelchair controlled through a vision-based interface

Published online by Cambridge University Press:  08 August 2011

Elisa Perez ,
Carlos Soria ,
Oscar Nasisi ,
Teodiano Freire Bastos  and
Vicente Mut
Elisa Perez*
Affiliation:
Gabinete de Tecnología Médica, Facultad de Ingeniería, Universidad Nacional de San Juan, Argentina
Carlos Soria
Affiliation:
Instituto de Automática, Facultad de Ingeniería, Universidad Nacional de San Juan, Argentina
Oscar Nasisi
Affiliation:
Instituto de Automática, Facultad de Ingeniería, Universidad Nacional de San Juan, Argentina
Teodiano Freire Bastos
Affiliation:
Centro Tecnológico, Universidade Federal do Espírito Santo, Brazil
Vicente Mut
Affiliation:
Instituto de Automática, Facultad de Ingeniería, Universidad Nacional de San Juan, Argentina
*
*Corresponding author. E-mail: eperez@gateme.unsj.edu.ar
Get access Rights & Permissions [Opens in a new window]

Summary

In this work, a vision-based control interface for commanding a robotic wheelchair is presented. The interface estimates the orientation angles of the user's head and it translates these parameters in command of maneuvers for different devices. The performance of the proposed interface is evaluated both in static experiments as well as when it is applied in commanding the robotic wheelchair. The interface calculates the orientation angles and it translates the parameters as the reference inputs to the robotic wheelchair. Control architecture based on the dynamic model of the wheelchair is implemented in order to achieve safety navigation. Experimental results of the interface performance and the wheelchair navigation are presented.

Keywords

Assistive robotsService robotsComputer visionMobile robotsNavigation

Information

Type
Articles
Information
Robotica , Volume 30 , Issue 5 , September 2012 , pp. 691 - 708
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1.Tapus, A., Mataric, M. J. and Scassellati, B., “Socially assistive robotics,” IEEE Robot. Autom. Mag. 14, 35–42 (2007).CrossRefGoogle Scholar
2.Mahoney, R. M., Van der Loos, H. F. Machiel, Lum, P. S. and Burgar, C., “Robotic stroke therapy assistant,” Robotica 21 (1), 33–44 (2003).CrossRefGoogle Scholar
3.Dellon, B. and Matsuoka, Y., “Prosthetics, exoskeletons and rehabilitation,” IEEE Robot. Autom. Mag. 14, 30–34 (2007).CrossRefGoogle Scholar
4.Krebs, H. I., Volpe, B. T., Aisen, M. L., Hening, W., Adamovich, S., Poizner, H., Subrahmanyan, K. and Hogan, N., “Robotic applications in neuromotor rehabilitation,” Robotica 21 (1), 3–11 (2003).CrossRefGoogle Scholar
5.Horn, O. and Kreutner, M., “Smart wheelchair perception using odometry ultrasound sensors and camera,” Robotica 27 (2), 303–310 (2009).CrossRefGoogle Scholar
6.Hillman, M., Hagan, K., Hagan, S., Jepson, J. and Orpwood, R., “The Weston wheelchair mounted assistive robot—The design story,” Robotica 20 (2), 125–132 (2002).CrossRefGoogle Scholar
7.Ren, M. and Karimi, H. A., “A hidden Markov model-based map-matching algorithm for wheelchair navigation,” J. Navig. 62, 383–395 (2009).CrossRefGoogle Scholar
8.Kuo, C. H., Hunag, H. L. and Lee, M. Y., “Development of agent-based autonomous robotic wheelchair control systems”. Biomed. Eng. Appl. Basis. Comm. 15, 223–234 (2003).CrossRefGoogle Scholar
9.Montesano, L., Minguez, J., Alcubierre, J. M. and Montano, L., “Towards the Adaptation of a Robotic Wheelchair for Cognitive Disabled Children,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Beijin, China (2006).Google Scholar
10.Jia, P., Hu, H., Lu, T. and Yuan, K., “Head gesture recognition for hands-free control of an intelligent wheelchair,” Ind. Robot: Int. J. 34, 60–68 (2007).CrossRefGoogle Scholar
11.Nguyen, N. T., Nguyen, H. T. and Su, S., “Advanced Robust Tracking Control of a Powered Wheelchair System,” Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Lyon, France (2007) pp. 4767–4770.Google Scholar
12.Zeng, Q., Teo, C. L., Rebsamen, B. and Burdet, E., “A collaborative wheelchair system,” IEEE Trans. Neural Syst. Rehabil. Eng. 16, 161–170 (2008).CrossRefGoogle ScholarPubMed
13.Bauckhage, C., Käster, T., Rotenstein, A. M. and Tsotsos, J. K., “Fast Learning for Customizable Head Pose Recognition in Robotic Wheelchair Control,” Proceedings of the Seventh International Conference on Automatic Face and Gesture Recognition (FGR) (2006).Google Scholar
14.Ju, J. S., Shin, Y. and Kim, E. Y., “Vision based interface system for hands free control of an intelligent wheelchair,” J. Neuroeng. Rehabil. 6, 33 (2009) (doi:10.1186/1743-0003-6-33).CrossRefGoogle ScholarPubMed
15.Ding, D. and Cooper, R. A., “Electric powered wheelchairs,” IEEE Control Syst. Mag. 25 (2), 22–34 (2005).Google Scholar
16.Muphy-Chutorian, E. and Trivedi, M. Manubhai, “Head pose estimation in computer vision: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. 31 (4), 607–626 (2009).CrossRefGoogle Scholar
17.Ferreira, A., Celeste, W. C., Cheein, F. Auat, Filho, T. F. Bastos, Filho, M. Sarcinelli, and Carelli, R., “Human-machine interfaces based on EMG and EEG applied to robotic systems,” J. Neuroeng. Rehabil. 5, 1–15 (2008).CrossRefGoogle ScholarPubMed
18.Gong, S., MacKenna, S. J. and Psarrou, A., Dynamic Vision from Images to Face Recognition (Imperial Collage Press, London, 2000).CrossRefGoogle Scholar
19.Dornaika, F. and Ahlberg, J., “Face and facial feature tracking using deformable models,” Int. J. Image Graphics. 4, 499–532 (2004).CrossRefGoogle Scholar
20.Chen, J. B. and Tiddeman, E., “Robust Facial Feature Tracking System, Advanced Video and Signal Based Surveillance,” Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS '05), Italy (2005) pp. 445–449.Google Scholar
21.Hsu, R. L., Abdel-Mottaleb, M., and Jain, A. K., “Face detection in color images,” Trans. Pattern Anal. Mach. Intell. 24, 696–705 (2002).Google Scholar
22.Cho, K. M., Jang, J. H. and Hong, K. S., “Adaptive skin-color filter,” Pattern Recognit. 34, 1067–1073 (2001).CrossRefGoogle Scholar
23.Jones, M. J. and Rehg, J. M., “Statistical color models with application to skin detection,” Int. J. Comput. Vis. 46, 86–96 (2002).CrossRefGoogle Scholar
24.Bhaskaran, V. and Konstantinides, K., Image and Video Compression Standards Algorithms and Architectures (Kluwer Academic Publishers, USA, 1999).Google Scholar
25.Li, S. Z. and Jain, A. K., Handbook of Face Recognition (Springer Science + Business Media, USA, 2005).Google Scholar
26.Berbar, M. A., Kelash, H. M. and Kandeel, A. A., “Face and Facial Features Detection in Color Images,” Proceedings of the Fourth International Conference on Informatics and Systems (INFOS '06) Cairo, Egipt (2006).Google Scholar
27.Horn, B. K. P., Robot Vision (MIT Press, McGraw-Hill, 1986).Google Scholar
28.Su, M. C., Wang, K. C. and Chen, G. D., “An eye tracking system and its application in aids for people with severe disabilities,” Biomed. Eng. Appl. Basis Commum. 8 (6), 319–327 (2006).CrossRefGoogle Scholar
29.Hannuksela, J., Heikkilä, J. and Pietikäinen, M., “Human-Computer Interaction Using Head Movements,” Proceedings of the Infotech Oulu International Workshop on Processing Sensory Information for Proactive Systems (PSIPS), Oulu, Finland (2004).Google Scholar
30.Lanzarotti, R., Campadelli, P. and Borghese, N. A., “Automatic Features Detection for Overlapping Face Images on their 3D Range Models,” Proceedings of the 11th International Conference Image Analysis and Processing, Palermo, Italy (2001) pp. 316–321.Google Scholar
31.Selim, S. Z. and Ismail, M. A., “K-means-type algorithms: A generalized convergence theorem and characterization of local optimality,” IEEE Trans. Pattern Anal. Mach. Intell. 6, 81–86 (1984).CrossRefGoogle ScholarPubMed
32.Trucco, E. and Verri, A., Introductory Techniques for 3-D Computer Vision (Prentice-Hall, New Jersey, 1998).Google Scholar
33.Brown, R. and Hwang, P., Introduction to Random Signals and Applied Kalman Filtering, 3rd ed. (John Wiley & Sons, New York, USA, 1997).Google Scholar
34.Soria, C. M., Carelli, R. and Sarcinelli-Filho, M., “Optical Flow Estimation Using Data Fusion,” VI Simposio Brasileiro de Automacao Inteligente, Bauru (2003) pp. 259–264.Google Scholar
35.Zhu, Y. and Fujimora, K., “3D Head Pose Estimation with Optical Flow and Depth Constraints,” Proceedings of the Fourth International Conference on 3D Digital Imaging an Modeling, Computer Society IEEE, Banff, Canada (Oct. 6–10, 2003).Google Scholar
36.Soria, C., Freire, E. and Carelli, R., “Stable AGV corridor navigation based on data and control signal fusión,” Latin Am. Appl. Res. 36 (2), 71–78 (2006).Google Scholar
37.Young, P. C., Recursive Estimation and Time Series Analysis: An Introduction (Springer Verlag, Berlin, 1984).CrossRefGoogle Scholar
38.Ferreira, A.; Cavalieri, D. C., Silva, R. L., Filho, T. F. Bastos and Filho, M. A. Sarcinelli, “Versatile Robotic Wheelchair Commanded by Brain Signals or Eye Blinks,” Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies, INSTICC, Portugal, (2008) vol. 2, pp. 62–67.Google Scholar
39.De la Cruz, C. and Carelli, R., “Dynamic model based formation control and obstacle avoidance of multi-robot systems,” Robotica 26 (3), 345–356 (2008).CrossRefGoogle Scholar
40.Soria, C., Carelli, R. and Sarcinelli-Filho, M., “Using Panoramic Images and Optical Flow to Avoid Obstacles in Mobile Robot Navigation,” Proceedings of the International Symposium on Industrial Electronics (ISIE), Canada (2006).Google Scholar
41.Filho, T. F. Bastos, Filho, M. Sarcinelli, Ferreira, A., Celeste, W. C., Silva, R. L., Martins, V. R., Cavalieri, D. C., Filgueira, P. N. and Arantes, I. B., “Case Study: Cognitive Control of a Robotic Wheelchair,” In: Wearable Robots: Biomechatronic Exoskeletons (Pons, J. L., ed.) Chapter 9, Section 9.6 (Wiley, 2008) pp. 315–319.Google Scholar