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A new forecasting kinematic algorithm of automatic navigation for a laparoscopic minimally invasive surgical robotic system

Published online by Cambridge University Press:  11 February 2016

Lingtao Yu ,
Zhengyu Wang Open the ORCID record for Zhengyu Wang [Opens in a new window] ,
Liqiang Sun ,
Wenjie Wang ,
Lan Wang  and
Zhijiang Du
Lingtao Yu
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, P.R. China E-mails: [email protected], [email protected], [email protected], [email protected], [email protected] State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, P.R. China E-mail: [email protected] Faculty of Engineering, Department of Mechanical Engineering, National University of Singapore, 117575, Singapore
Zhengyu Wang*
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, P.R. China E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Liqiang Sun
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, P.R. China E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Wenjie Wang
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, P.R. China E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Lan Wang
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, P.R. China E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Zhijiang Du
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150080, P.R. China E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper presents a novel forecasting kinematic algorithm for autonomously navigating the 3D visual window of laparoscopic minimally invasive surgical robotic system (LMISRS). By the application of the proposed technique, a constant distribution area ratio of the micro devices can be guaranteed in the visual window; real-time concurrency motion of the visual window of the laparoscope and the mark points of the instruments is realized, i.e. the visual window can keep tracking the movement of the marks automatically, so that the user does not have to switch between the master-slave controlling targets. The implementation of the new technique is summarized as follows: the robotic kinematics and space analytic geometry are thoroughly analyzed and modeled, and a “following kinematic algorithm” is proposed for the visual window of the laparoscope, which tracks the mark points of the instrument arms; a “forecasting kinematic algorithm” is established by using a combination of the “following kinematic algorithm”, the basic visual parameters of 3D visual field, the Verhulst Grey Model and the filtered amendment method. The proposed technique is verified by a series of simulations by using two groups of marks' motion trails with different sampling times, indicating that the technique is accurate, feasible and robust.

Keywords

Surgical robotVisual windowAutomatic navigationVerhulst Grey modelFollowing kinematic algorithmForecasting kinematic algorithm
Type
Articles
Information
Robotica , Volume 35 , Issue 5 , May 2017 , pp. 1192 - 1222
Copyright
Copyright © Cambridge University Press 2016 

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References

1. Sung, G. T. and Gill, I. S., “Robotic laparoscopic surgery: A comparison of the daVinci and Zeus systems,” Urology 58 (6), 893–898 (2001).Google Scholar
2. Valero, R., Ko, Y. H., Chauhan, S., Schatloff, O., Sivaraman, A., Coelho, R. F., Ortega, F., Palmer, K. J., Sanchez-Salas, R., Davila, H., Cathelineau, X. and Patel, V. R., “Robotic surgery: History and teaching impact,” Actas Urológicas Españolas 35 (9), 540–545 (2011).CrossRefGoogle ScholarPubMed
3. Rassweiler, J., Rassweiler, M. C., Kenngott, H., Frede, T., Michel, M. S., Alken, P. and Clayman, R., “The past, present and future of minimally invasive therapy in urology: A review and speculative outlook,” Minimally Invasive Ther. Allied Technol. 22 (4), 200–209 (2013).Google Scholar
4. Ruel, M., Une, D., Bonatti, J. and McGinn, J. T., “Minimally invasive coronary artery bypass grafting: Is it time for the robot,” Curr. Opin. Cardiology 28 (6), 639–645 (2013).Google Scholar
5. Yang, B., Roys, S., Tan, U. X., Philip, M., Richard, H., Gullapalli, R. and Desai, J. P., “Design, development, and evaluation of a master-slave surgical system for breast biopsy under continuous MRI,” Int. J. Robot. Res. 33 (4), 616–630 (2014).Google Scholar
6. Ng, T. L. Ada and Tam, P. C., “Current status of robot-assisted surgery,” Hong Kong Med. J. 20 (3), 241–250 (2014).Google Scholar
7. Haidegger, T. and Benyo, Z., “Surgical robotic support for long duration space missions,” Acta Astronautica 63 (7–10), 996–1005 (2008).Google Scholar
8. Monsarrat, N., Collinet, P., Narducci, F., Leblanc, E. and Vinatier, D., “Robotic assistance in gynaecological surgery: State-of-the-art,” Gynecologie, Obstetrique & Fertilite 37 (5), 415–424 (2009).Google Scholar
9. Turchetti, G., Palla, I., Pierotti, F. and Cuschieri, A., “Economic evaluation of da vinci-assisted robotic surgery: A systematic review,” Surgical Endoscopy 26 (3), 598–606 (2012).Google Scholar
10. Freschi, C., Ferrari, V., Melfi, F., Ferrari, M., Mosca, F. and Cuschieri, A., “Technical review of the da Vinci surgical telemanipulator,” Int. J. Med. Robot. Comput. Assist. Surg. 9 (4), 396–406 (2013).Google Scholar
11. Gou, W. and Norihiro, I., “da Vinci surgical system. Kyobu geka,” Japan. J. Thoracic Surg. 67 (8), 686–9 (2014).Google Scholar
12. Hagn, U., Nickl, M., Jorg, S., Passig, G., Bahls, T., Nothhelfer, A., Hacker, F., Le-Tien, L., Albu-Schäffer, A., Konietschke, R., Grebenstein, M., Warpup, R., Haslinger, R., Frommberger, M. and Hirzinger, G., “The DLR MIRO: A versatile lightweight robot for surgical applications,” Ind. Robot: Int. J. 35 (4), 324–336 (2008).Google Scholar
13. Konietschke, R., Hagn, U., Nickl, M., Jorg, S., Tobergte, A., Passig, G., Seibold, U., Le-Tien, L., Kubler, B., Groger, M., Frohlich, F., Rink, C., Albu-Schäffer, A., Grebenstein, M., Ortmaier, T. and Hirzinger, G., “The DLRMiroSurge: A Robotic System for Surgery,” Proceedings of the 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan (May 12–17, 2009) pp. 1589–1590.Google Scholar
14. Lum, M. J., Friedman, D. C., Sankaranarayanan, G., King, H., Fodero, K., Leuschke, R. and Sinanan, M. N., “The RAVEN: Design and validation of a telesurgery system,” The Int. J. Robot. Res. 28 (9), 1183–1197 (2009).Google Scholar
15. Jacob, R., Mitchell, L., Mika, S. and Blake, H., “Raven: Developing a Surgical Robot from a Concept to a Transatlantic Teleoperation Experiment,” In: Surgical Robotics: Systems Applications and Visions (Rosen, J., Hannaford, B. and Satava, R. M., eds.) (Springer, New York, US, 2011) pp. 159–197.Google Scholar
16. Hornyak, T., (2010). Paging Raven II: The open-source surgery robot. CNET [internet]. Available at: http://www.cnet.com/news/paging-raven-ii-the-open-source-surgery-robot/.Google Scholar
17. van den Bedema, L., Rosiellea, N. and Steinbuch, M., “Design of Slave Robot for Laparoscopic and Thoracoscopic Surgery,” Proceedings of the 20th International Conference of Society for Medical Innovation and Technology, Vienna, Austria (Aug. 28–30, 2008) pp. F–016.Google Scholar
18. Berkelman, P. and Ma, J., “A compact modular teleoperated robotic system for laparoscopic surgery,” Int. J. Robot. Res. 28 (9), 1198–1215 (2009).CrossRefGoogle ScholarPubMed
19. Staub, C., Lenz, C., Panin, G., Knoll, A. and Bauernschmitt, R., “Contour-Based Surgical Instrument Tracking Supported by Kinematic Prediction,” Proceedings of the 2010 3rd IEEE RAS & EMBS on Biomedical Robotics and Biomechatronics (BioRob), Tokyo, Japan (Sep. 26–29, 2010) pp. 746–752.Google Scholar
20. Gherman, B., Pisla, D., Vaida, C. and Plitea, N., “Development of inverse dynamic model for a surgical hybrid parallel robot with equivalent lumped masses,” Robot. Comput.-Integr. Manuf. 28 (3), 402–415 (2012).Google Scholar
21. Brouwer, O. R., Buckle, T., Bunschoten, A., Kuil, J., Vahrmeijer, A. L., Wendler, T., Valdés-Olmos, R. A., van der Poel, H. G. and van Leeuwen, F. W. B., “Image navigation as a means to expand the boundaries of fluorescence-guided surgery,” Phys. Med. Biol. 57 (10), 3123–3136 (2012).Google Scholar
22. Tully, S., Kantor, G., Zenati, M. A. and Choset, H., “Shape Estimation for Image-Guided Surgery with a Highly Articulated Snake Robot,” Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA (Sep. 25–30, 2011) pp. 1353–1358.Google Scholar
23. Weede, O., Mönnich, H., Müller, B. and Wörn, H., “An Intelligent and Autonomous Endoscopic Guidance System forMinimally Invasive Surgery,” Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China (May 9–13, 2011) pp. 5762–5768.Google Scholar
24. Yu, L., Wang, Z., Yu, P., Wang, T. and Song, H., “Research on the Kinematic Method of 3D Visual Window for Surgical Robot in Dynamic Vision,” Proceedings of the 2012 IEEE International Conference on Mechatronics and Automation, Chengdu, China (Aug. 5–8, 2012) pp. 2037–2042.Google Scholar
25. Yu, L., Wang, Z., Yu, P., Wang, T., Song, H. and Du, Z., “A new kinematics method based on a dynamic visual window for a surgical robot,” Robotica 32 (04), 571–589 (2014).Google Scholar
26. Yu, L., Wang, Z., Sun, L., Wang, W. and Wang, T., “A kinematics Method of Automatic Visual Window for Laparoscopic Minimally Invasive Surgical Robotic System,” Proceedings of the 2013 IEEE International Conference on Mechatronics and Automation, Takamatsu, Japan (Aug. 4–7, 2013) pp. 997–1002.CrossRefGoogle Scholar
27. Xu, J., Tan, T., Tu, M. and Qi, L., “Improvement of grey models by least squares,” Expert Syst. Appl. 38 (11), 13961–13966 (2011).Google Scholar
28. Wen, K.-L. and Huang, Y.-F., “The Development of Grey Verhulst Toolbox and the Analysis of Population Saturation State in Taiwan-Fukien,” Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics, the Netherlands (Oct. 10–13, 2004), vol. 6, pp. 5007–5012.Google Scholar
29. MA, R., Wang, W., Dong, W. and DU, Z., “Preoperative positioning analysis of the celiac minimally invasive surgery robotic system based on an improved gradient projection algorithm,” Robot 32 (2), 156–163 (2014).Google Scholar
30. Li, G., Wu, D., Ma, R., Huang, K. and Du, Z., “Pose Planning for Robotically Assisted Minimally Invasive Surgery,” Proceedings of the 2010 3rd IEEE International Conference on Biomedical Engineering and Informatics, Yantai, China (Oct. 16–18, 2010) pp. 1769–1774.Google Scholar
31. Yu, L., Wang, Z., Sun, L., Wang, W. and Wang, L., “Research on Preoperative Positioning Analysis of Instrument Arms for Minimally Invasive Surgical Robot,” Proceedings of the 2014 IEEE International Conference on Mechatronics and Automation, Tianjin, China (Aug. 4–7, 2014) pp. 1269–1275.CrossRefGoogle Scholar
32. Wang, Z.-X., Dang, Y.-G. and Liu, S.-F., “Unbiased grey Verhulst model and its application,” Syst. Eng.-Theory Pract. 29 (10), 138–144 (2009).Google Scholar
33. Erdal, K., Baris, U. and Okyay, K., “Grey system theory-based models in time series prediction,” Exp. Syst. Appl. 37 (2), 1784–1789 (2010).Google Scholar
34. Sun, L.-H. and Shen, J.-H., “Prediction of ship pitching based on an improved Grey GM(1, 1) model,” Harbin Gongcheng Daxue Xuebao/J. Harbin Eng. Univ. 30 (3), 287–291 (2009).Google Scholar
35. Chen, C.-I. and Huang, S.-J., “The necessary and sufficient condition for GM(1, 1) grey prediction model,” Appl. Math. Comput. 219 (11), 6152–6162 (2013).Google Scholar
36. Abreu, A., “Robot Localization from Minimalist Inertial Data using a Hidden Markov Model,” Proceedings of the 2014 IEEE International Conference on Autonomous Robot Systems and Competitions, Espinho, Portugal (May 14–15, 2014) pp. 247–252.Google Scholar
37. Nguyen-Duc-Thanh, N., Lee, S. and Kim, D., “Two-stage hidden markov model in gesture recognition for human robot interaction,” Int. J. Adv. Robot. Syst. 9 (39), DOI: 10.5772/50204 (2012).Google Scholar
38. Liu, S. and Yi, L., “Grey information: Theory and practical applications,” Springer Sci. Business Media, ISBN: 978-1-85233-995-1, (2006) pp. 275–314.Google Scholar
39. Liu, S., Yi, L. and Forrest, J. Y. L., “Grey systems: Theory and applications,” Springer Sci. Business Media, ISBN: 978-3-642-16157-5, (2011) Volume 68, pp. 107–148.Google Scholar
40. Shen, S., Xia, C., Sprick, R., Perez, L. C. and Goddard, S., “Comparison of Three Kalman Filters for an Indoor Passive Tracking System,” Proceedings of the 2007 IEEE International Conference on Electro/Information Technology, Chicago, USA (May 17–20, 2007) pp. 284–289.Google Scholar
41. Särkkä, S. and Sarmavuori, J., “Gaussian filtering and smoothing for continuous-discrete dynamic systems,” Signal Process., 93 (2), 500–510 (2013).Google Scholar
42. Aysal, T. C. and Barner, K. E., “Generalized mean-median filtering for robust frequency-selective applications,” IEEE Trans. Signal Process., 55 (3), 937–948 (2007).Google Scholar
43. Cao, Y. and Mao, X., “Improved Filtering-Smoothing Algorithm for GPS Positioning,” Proceedings of the 2008 IEEE Conference on Intelligent Transportation Systems, Beijing, China (Oct. 12–15, 2008) pp. 857–861.CrossRefGoogle Scholar