Abstract
Limitations of state-of-the-art teleoperation systems can be compensated by using shared-control teleoperation architectures that provide haptic assistance to the human operator. This paper presents a new approach for computer-assisted teleoperation, which recognizes human intentions and dependent on the classified task activates different types of assistances. For this purpose, time series haptic data is recorded during interaction, passed through an event-based feature extraction, and finally used for task classification by applying a Hidden Markov Model approach. The effect of the assistance function on human behavior is discussed and taken into account by training multiple classifiers for each type of assistance. The introduced approach is finally validated in a real hardware experiment. Results show an accurate intention recognition for assisted and non-assisted teleoperation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Corteville, B., Aertbelien, E., Bruyninckx, H., Schutter, J.D., Brussel, H.V.: Human-inspired robot assistant for fast point-to-point movements. In: 2007 IEEE International Conference on Robotics and Automation, pp. 3639–3644. IEEE, Los Alamitos (2007)
Unterhinninghofen, U., Freyberger, F.K., Buss, M.: Study on computer assistance for telepresent reaching movements. In: Ferre, M. (ed.) EuroHaptics 2008. LNCS, vol. 5024, pp. 745–754. Springer, Heidelberg (2008)
Lee, D., Kulic, D., Nakamura, Y.: Missing motion data recovery using factorial hidden markov models. In: IEEE International Conference on Robotics and Automation, ICRA 2008, May 2008, pp. 1722–1728 (2008)
Aarno, D., Kragic, D.: Motion intention recognition in robot assisted applications. Robot. Auton. Syst. 56(8), 692–705 (2008)
Zollner, R., Rogalla, O., Dillmann, R., Zollner, M.: Understanding users intention: programming fine manipulation tasks by demonstration. In: IEEE/RSJ International Conference on Intelligent Robots and System, vol. 2, pp. 1114–1119 (2002)
Castellani, A., Botturi, D., Bicego, M., Fiorini, P.: Hybrid hmm/svm model for the analysis and segmentation of teleoperation tasks. In: Proceedings of IEEE International Conference on Robotics and Automation, ICRA 2004, April-1 May, vol. 3, pp. 2918–2923 (2004)
Aarno, D., Kragic, D.: Layered HMM for motion intention recognition. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5130–5135 (October 2006)
Stefanov, N., Peer, A., Buss, M.: Online intention recognition for computer-assisted teleoperation. In: IEEE International Conference on Robotics and Automation (to appear, 2010)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience Publication, Hoboken (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stefanov, N., Peer, A., Buss, M. (2010). Online Intention Recognition in Computer-Assisted Teleoperation Systems. In: Kappers, A.M.L., van Erp, J.B.F., Bergmann Tiest, W.M., van der Helm, F.C.T. (eds) Haptics: Generating and Perceiving Tangible Sensations. EuroHaptics 2010. Lecture Notes in Computer Science, vol 6191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14064-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-14064-8_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14063-1
Online ISBN: 978-3-642-14064-8
eBook Packages: Computer ScienceComputer Science (R0)