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A psychophysical evaluation of haptic controllers: viscosity perception of soft environments

Published online by Cambridge University Press:  19 July 2013

Hyoung Il Son*
Affiliation:
Institute of Industrial Technology, Samsung Heavy Industries, 217 Munji-ro, Yuseong-gu, Daejeon 305-380, Republic of Korea
Hoeryong Jung
Affiliation:
Institute of Industrial Technology, Samsung Heavy Industries, 217 Munji-ro, Yuseong-gu, Daejeon 305-380, Republic of Korea
Doo Yong Lee
Affiliation:
Department of Mechanical Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea. E-mail: leedy@kaist.ac.kr
Jang Ho Cho
Affiliation:
Department of Automatic Control, Lund University, PO Box 118, SE-221 00 Lund, Sweden. E-mail: jangho@control.lth.se
Heinrich H. Bülthoff
Affiliation:
Max Planck Institute for Biological Cybernetics, Spemannstraße 38, 72076 Tübingen, Germany. E-mail: hhb@tuebingen.mpg.de Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, Republic of Korea
*
*Corresponding author. E-mail: hyoungil.son@kaist.ac.kr, hyoungil.son@gmail.com

Summary

In this paper, human viscosity perception in haptic teleoperation systems is thoroughly analyzed. An accurate perception of viscoelastic environmental properties such as viscosity is a critical ability in several contexts, such as telesurgery, telerehabilitation, telemedicine, and soft-tissue interaction. We study and compare the ability to perceive viscosity from the standpoint of detection and discrimination using several relevant control methods for the teleoperator. The perception-based method, which was proposed by the authors to enhance the operator's kinesthetic perception, is compared with the conventional transparency-based control method for the teleoperation system. The fidelity-based method, which is a primary method among perception-centered control schemes in teleoperation, is also studied. We also examine the necessity and impact of the remote-site force information for each of the methods. The comparison is based on a series of psychophysical experiments measuring absolute threshold and just noticeable difference for all conditions. The results clearly show that the perception-based method enhances both detection and discrimination abilities compare with other control methods. The results further show that the fidelity-based method confers a better discrimination ability than the transparency-based method, although this is not true with respect to detection ability. In addition, we show that force information improves viscosity detection for all control methods, as predicted from previous theoretical analysis, but improves the discrimination threshold only for the perception-based method.

Type
Articles
Copyright
Copyright © Cambridge University Press 2013 

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References

1.van der Meijden, O. and Schijven, M., “The value of haptic feedback in conventional and robot-assisted minimal invasive surgery and virtual reality training: A current review,” Surg. Endosc. 23 (6), 11801190 (2009).CrossRefGoogle ScholarPubMed
2.Hill, J., Holst, P., Jensen, J., Goldman, J., Gorfu, Y. and Ploeger, D., “Telepresence interface with applications to microsurgery and surgical simulation,” Stud. Health Technol. Inform. 50, 96102 (1998).Google ScholarPubMed
3.Tendick, F., Sastry, S., Fearing, R. and Cohn, M., “Applications of micromechatronics in minimally invasive surgery,” IEEE/ASME Trans. Mechatronics 3 (1), 3442 (1998).CrossRefGoogle Scholar
4.Heijnsdijk, E., Pasdeloup, A., Van der Pijl, A., Dankelman, J. and Gouma, D., “The influence of force feedback and visual feedback in grasping tissue laparoscopicauy,” Surg. Endosc. 18 (6), 980985 (2004).CrossRefGoogle Scholar
5.Jones, L. A., “Kinesthetic Sensing,” In: Human and Machine Haptics (Cutkosky, M., Howe, R., Salisbury, K. and Srinivasan, M., eds.) (MIT Press, Cambridge, MA, 2000).Google Scholar
6.Jones, L. and Hunter, I., “A perceptual analysis of viscosity,” Exp. Brain Res. 94 (2), 343351 (1993).CrossRefGoogle ScholarPubMed
7.Huang, F., Patton, J. and Mussa-Ivaldi, F., “Manual skill generalization enhanced by negative viscosity,” J. Neurophysiol. 104 (4), 20082019 (2010).CrossRefGoogle ScholarPubMed
8.Steele, C., Van Lieshout, P. and Goff, D., “The rheology of liquids: A comparison of clinicians subjective impressions and objective measurement,” Dysphagia. 18 (3), 182195 (2003).CrossRefGoogle ScholarPubMed
9.Lederman, S., Klatzky, R., Tong, C. and Hamilton, C., “The perceived roughness of resistive virtual textures: II. Effects of varying viscosity with a force-feedback device,” ACM Trans. Appl. Perception 3 (1), 1530 (2006).CrossRefGoogle Scholar
10.Sheridan, T., “Telerobotics,” Automatica 25 (4), 487507 (1989).CrossRefGoogle Scholar
11.Hokayem, P. and Spong, M., “Bilateral teleoperation: An historical survey,” Automatica 42 (12), 20352057 (2006).CrossRefGoogle Scholar
12.Lawrence, D., “Stability and transparency in bilateral teleoperation,” IEEE Trans. Robot. Autom. 9 (5), 624637 (1993).CrossRefGoogle Scholar
13.Hashtrudi-Zaad, K. and Salcudean, S., “Analysis of control architectures for teleoperation systems with impedance/admittance master and slave manipulators,” Int. J. Robot. Res. 20 (6), 419445 (2001).CrossRefGoogle Scholar
14.Çavusoglu, M., Sherman, A. and Tendick, F., “Design of bilateral teleoperation controllers for haptic exploration and telemanipulation of soft environments,” IEEE Trans. Robot. Autom. 18 (4), 641647 (2002).CrossRefGoogle Scholar
15.De Gersem, G., Van Brussel, H. and Tendick, F., “Reliable and enhanced stiffness perception in soft-tissue telemanipulation,” Int. J. Robot. Res. 24 (10), 805822 (2005).CrossRefGoogle Scholar
16.Malysz, P. and Sirouspour, S., “Nonlinear and filtered force/position mappings in bilateral teleoperation with application to enhanced stiffness discrimination,” IEEE Trans. Robot. 25 (5), 11341149 (2009).CrossRefGoogle Scholar
17.Botturi, D., Vicentini, M., Righele, M. and Secchi, C., “Perception-centric force scaling in bilateral teleoperation,” Mechatronics 20 (7), 802811 (2010).CrossRefGoogle Scholar
18.Son, H. I., Bhattacharjee, T. and Hashimoto, H., “Enhancement in operator's perception of soft tissues and its experimental validation for scaled teleoperation systems,” IEEE/ASME Trans. Mechatronics 16 (6), 10961109 (2011).CrossRefGoogle Scholar
19.Son, H. I., Bhattacharjee, T. and Hashimoto, H., “Effect of impedance-shaping on perception of soft tissues in macro-micro teleoperation,” IEEE Trans. Ind. Electron. 59 (8), 32733285 (2012).CrossRefGoogle Scholar
20.Sun, Y. and Nelson, B., “Biological cell injection using an autonomous microrobotic system,” Int. J. Robot. Res. 21 (10–11), 861868 (2002).CrossRefGoogle Scholar
21.Dhruv, N. and Tendick, F., “Frequency Dependence of Compliance Contrast Detection,” In: Proceedings of the Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (Orlando, FL, 2000) pp. 10871093.Google Scholar
22.Sherman, A., Çavusoglu, M. and Tendick, F., “Comparison of Teleoperator Control Architectures for Palpation Task,” In: Proceedings of the Symposium on Haptic Interfaces for Virtual Environments and Teleoperator Systems (Orlando, FL, 2000) pp. 12611268.Google Scholar
23.Son, H. I., Bhattacharjee, T., Jung, H. and Lee, D. Y., “Psychophysical Evaluation of Control Scheme Designed for Optimal Kinesthetic Perception in Scaled Teleoperation,” In: Proceedings of the IEEE International Conference on Robotics and Automation (Anchorage, AK, 2010) pp. 53465351.Google Scholar
24.Son, H. I., Franchi, A., Chuang, L., Kim, J., Bülthoff, H. and Giordano, P. Robuffo, “Human-Centered Design and Evaluation of Haptic Cueing for Teleoperation of Multiple Mobile Robots,” IEEE Trans. Syst. Man Cybern. 43 (2), 597609 (2013).Google ScholarPubMed
25.Son, H. I., Bhattacharjee, T. and Lee, D. Y., “Estimation of environmental force for the haptic interface of robotic surgery,” Int. J. Med. Robot. Comput. Assist. Surg. 6 (2), 221230 (2010).CrossRefGoogle ScholarPubMed
26.Gescheider, G., Psychophysics: The Fundamentals (Lawrence Erlbaum, Mahwah, NJ, 1997).Google Scholar
27.Keppel, G. and Wickens, T., Design and Analysis (Prentice Hall, Englewood Cliffs, NJ, 2007).Google Scholar
28.Diolaiti, N., Niemeyer, G., Barbagli, F. and Salisbury, J., “Stability of haptic rendering: Discretization, quantization, time delay, and coulomb effects,” IEEE Trans. Robot. 22 (2), 256268 (2006).CrossRefGoogle Scholar
29.Belegundu, A. and Chandrupatla, T., Optimization Concepts and Applications in Engineering (Prentice Hall, Upper Saddle River, NJ, 1999).Google Scholar
30.Haykin, S., Active Network Theory (Addison-Wesley, Reading, MA, 1970).Google Scholar
31.Woo, H., Kim, W., Ahn, W., Lee, D., and Yi, S., “Haptic interface of the KAIST-Ewha colonoscopy simulator II,” IEEE Trans. Inf. Technol. Biomed. 12 (6), 746753 (2008).Google ScholarPubMed
32.Beauregard, G., Srinivasan, M. and Durlach, N., “The Manual Resolution of Viscosity and Mass,” In: Proceedings of the ASME Dynamic Systems and Control Division, vol. DSC-57-2, (Ann Arbor, MI, 1995) pp. 657662.Google Scholar
33.MacFarlane, M., Rosen, J., Hannaford, B., Pellegrini, C. and Sinanan, M., “Force-feedback grasper helps restore sense of touch in minimally invasive surgery,” J. Gastrointestinal Surg. 3 (3), 278285 (1999).CrossRefGoogle ScholarPubMed
34.Tavakoli, M., Aziminejad, A., Patel, R. and Moallem, M., “Methods and mechanisms for contact feedback in a robot-assisted minimally invasive environment,” Surg. Endosc. 20 (10), 15701579 (2006).CrossRefGoogle Scholar
35.Peirs, J., Clijnen, J., Reynaerts, D., Brussel, H., Herijgers, P., Corteville, B. and Boone, S., “A micro optical force sensor for force feedback during minimally invasive robotic surgery,” Sensors Actuators A: Phys. 115 (2–3), 447455 (2004).CrossRefGoogle Scholar
36.Seibold, U., Kubler, B. and Hirzinger, G., “Prototype of Instrument for Minimally Invasive Surgery with 6-axis Force Sensing Capability,” In: Proceedings of the IEEE International Conference on Robotics and Automation (Barcelona, Spain, 2005) pp. 496501.Google Scholar
37.Seibold, U., Kubler, B., Thielmann, S. and Hirzinger, G., “Endoscopic 3 Dof-Instrument with 7 Dof Force/Torque Feedback,” In: Workshop at the IEEE International Conference on Robotics and Automation (Kobe, Japan, 2009) pp. 14.Google Scholar
38.Hagn, U., Konietschke, R., Tobergte, A., Nickl, M., Jörg, S., Kübler, B., Passig, G., Gröger, M., Fröhlich, F., Seibold, U., et al.DLR mirosurge: A versatile system for research in endoscopic telesurgery,” Int. J. Comput. Assist. Radiol. Surg. 5 (2), 183193 (2010).CrossRefGoogle ScholarPubMed