Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-22T09:07:52.125Z Has data issue: false hasContentIssue false
  • English
  • Français

Closed-loop control of bevel-tip needles based on path planning

Published online by Cambridge University Press:  24 August 2018

Benyan Huo Open the ORCID record for Benyan Huo [Opens in a new window] ,
Xingang Zhao ,
Jianda Han  and
Weiliang Xu
Benyan Huo*
Affiliation:
School of electrical engineering, Zhengzhou University, Zhengzhou, P.R. China Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, P.R. China. Emails: [email protected], [email protected]
Xingang Zhao
Affiliation:
Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, P.R. China. Emails: [email protected], [email protected]
Jianda Han
Affiliation:
Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, P.R. China. Emails: [email protected], [email protected]
Weiliang Xu
Affiliation:
University of Auckland, Auckland, New Zealand. Email: [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Bevel-tip needles have the potential to improve paracentetic precision and decrease paracentetic traumas. In order to drive bevel-tip needles precisely with the constrains of path length and path dangerousness, we propose a closed-loop control method that only requires the position of the needle tip and can be easily applied in a clinical setting. The control method is based on the path planning method proposed in this paper. To establish the closed-loop control method, a kinematic model of bevel-tip needles is first presented, and the relationship between the puncture path and controlled variables is established. Second, we transform the path planning method into a multi-objective optimization problem, which takes the path error, path length and path dangerousness into account. Multi-objective particle swarm optimization is employed to solve the optimization problem. Then, a control method based on path planning is presented. The current needle tip attitude is essential to plan an insertion path. We analyze two methods to obtain the tip attitude and compare their effects using both simulations and experiments. In the end, simulations and experiments in phantom tissue are executed and analyzed, the results show that our methods have high accuracy and have the ability to deal with the model parameter uncertainty.

Keywords

Medical roboticsBevel-tip needleParticle swarm optimizationPath planningUKF
Type
Articles
Information
Robotica , Volume 36 , Issue 12 , December 2018 , pp. 1857 - 1873
Copyright
Copyright © Cambridge University Press 2018 

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.)

References

1. Webster, R., Cowan, N., Chirikjian, G. and Okamura, A., Nonholonomic Modeling of Needle Steering (Springer, Berlin-Heidelberg, 2006), vol. 21, pp. 35–44.Google Scholar
2. Misra, S., Reed, K., Schafer, B., Ramesh, K. and Okamura, A., “Mechanics of flexible needles robotically steered through soft tissue,” Int. J. Robot. Res. 29 (13), 1640–1660 (2010).Google Scholar
3. Wei, D., Han, H. and Du, Z. J., “The Tip Interface Mechanics Modeling of a Bevel-Tip Flexible Needle Insertion,” International Conference on Mechatronics and Automation (2012) pp. 581–586.Google Scholar
4. Roesthuis, R. J., van Veen, Y. R. J., Jahya, A. and Misra, S., “Mechanics of Needle-Tissue Interaction,” International Conference on Intelligent Robots and Systems (2011) pp. 2557–2563.Google Scholar
5. Asadian, A., Patel, R. V. and Kermani, M. R., “A Distributed Model for Needle-Tissue Friction in Percutaneous Interventions,” IEEE International Conference on Robotics and Automation (2011) pp. 1896–1901.Google Scholar
6. Asadian, A., Kermani, M. R. and Patel, R. V., “An Analytical Model for Deflection of Flexible Needles During Needle Insertion,” IEEE/RSJ International Conference on Intelligent Robots and Systems (2011) pp. 2551–2556.Google Scholar
7. Abayazid, M., Roesthuis, R. J., Reilink, R. and Misra, S., “Integrating deflection models and image feedback for real-time flexible needle steering,” IEEE Trans. Robot. 29 (2), 542–553 (2013).Google Scholar
8. Haddadi, A. and Hashtrudi-Zaad, K., “Development of a Dynamic Model for Bevel-Tip Flexible Needle Insertion into Soft Tissues,” IEEE International Conference on Engineering in Medicine and Biology Society (2011) pp. 7478–7482.Google Scholar
9. Yan, K. G., Podder, T., Yan, Y., Tien, I. L., Cheng, C. W. S. and Wansing, N., “Flexible needle-tissue interaction modeling with depth-varying mean parameter: Preliminary study,” IEEE Trans. Biomed. Eng. 56 (2), 255–262 (2009).Google Scholar
10. Wooram, P., Jin Seob, K., Yu, Z., Cowan, N. J., Okamura, A. M. and Chirikjian, G. S., “Diffusion-Based Motion Planning for a Nonholonomic Flexible Needle Model,” IEEE International Conference on Robotics and Automation (2005) pp. 4600–4605.Google Scholar
11. Alterovitz, R., Lim, A., Goldberg, K., Chirikjian, G. S. and Okamura, A. M., “Steering Flexible Needles Under Markov Motion Uncertainty,” IEEE/RSJ International Conference on Intelligent Robots and Systems (2005) pp. 1570–1575.Google Scholar
12. Alterovitz, R., Goldberg, K. and Okamura, A., “Planning for Steerable Bevel-tip Needle Insertion Through 2D Soft Tissue with Obstacles,” Proceedings of the 2005 IEEE International Conference on Robotics and Automation (2005) pp. 1640–1645.Google Scholar
13. Zhang, Y., Zhao, Y. and Hao, C., “2D path planning of bevel tip flexible needle in soft tissue,” Robot 33 (6), 750–757 (2011).Google Scholar
14. Huo, B., Zhao, X., Han, J. and Xu, W., “Motion Planning for Flexible Needle in Multilayer Tissue Environment with Obstacles,” IEEE International Conference on Systems, Man, and Cybernetics (2012) pp. 3292–3297.Google Scholar
15. Xu, J., Vincent, D., Ron, A. and Ken, G., “Motion Planning for Steerable Needles in 3D environments with Obstacles Using Rapidly-Exploring Random Trees and Backchaining,” IEEE International Conference on Automation Science and Engineering (2008) pp. 41–46.Google Scholar
16. Patil, S. and Alterovitz, R., “Interactive Motion Planning for Steerable Needles in 3D Environments with Obstacles,” International Conference on Biomedical Robotics and Biomechatronics (2010) pp. 893–899.Google Scholar
17. Patil, S., Burgner, J., Webster, R. J. and Alterovitz, R., “Needle steering in 3-D via rapid replanning,” IEEE Trans. Robot. 30 (4), 853–864 (2014).Google Scholar
18. Liu, F., Garriga-Casanovas, A., Secoli, R. and Baena, F. R. y., “Fast and adaptive fractal tree-based path planning for programmable bevel tip steerable needles,” IEEE Robot. Autom. Lett. 1 (2), 601–608 (2016).Google Scholar
19. Wang, J., Li, X., Zheng, J. and Sun, D., “Dynamic path planning for inserting a steerable needle into a soft tissue,” IEEE/ASME Trans. Mechatronics 19 (2), 549–558 (2014).Google Scholar
20. Duindam, V., Alterovitz, R., Sastry, S. and Goldberg, K., “Three-dimensional motion planning algorithms for steerable needles using inverse kinematics,” Int. J. Robot. Res. 29 (7), 789–800 (2010).Google Scholar
21. Rucker, D. C., Das, J., Gilbert, H. B., Swaney, P. J., Miga, M. I., Sarkar, N. and Webster, R. J., “Sliding mode control of steerable needles,” IEEE Trans. Robot. 29 (5), 1289–1299 (2013).Google Scholar
22. Huo, B. Y., Zhao, X. G., Han, J. D. and Xu, W. L., “A control method of flexible needle puncture control based on reachable decision,” Control Theory & Appl. 31 (10), 1423–1430 (2014).Google Scholar
23. Krupa, A., “A New Duty-Cycling Approach for 3D Needle Steering Allowing the Use of the Classical Visual Servoing Framework for Targeting Tasks,” International Conference on Biomedical Robotics and Biomechatronics (2014) pp. 301–307.Google Scholar
24. Bernardes, M. C., Adorno, B. V., Poignet, P. and Borges, G. A., “Robot-assisted automatic insertion of steerable needles with closed-loop imaging feedback and intraoperative trajectory replanning,” Mechatronics 23 (6), 630–645 (2013).Google Scholar
25. Vrooijink, G., Abayazid, M., Patil, S., Alterovitz, R. and Misra, S., “Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images,” Int. J. Robot. Res. 33 (10), 1361–1374 (2014).Google Scholar
26. Adebar, T. K., Fletcher, A. E. and Okamura, A. M., “3-D ultrasound-guided robotic needle steering in biological tissue,” IEEE Trans. Biomed. Eng. 61 (12), 2899–2910 (2014).Google Scholar
27. Zhao, X., Guo, H., Ye, D. and Huo, B., “Comparison of Estimation and Control Methods for Flexible Needle in 2D,” 2016 Chinese Control and Decision Conference (2016) pp. 5444–5449.Google Scholar
28. Duindam, V., Alterovitz, R., Sastry, S. and Goldberg, K., “Screw-Based Motion Planning for Bevel-Tip Flexible Needles in 3D Environments with Obstacles,” IEEE International Conference on Robotics and Automation (2008) pp. 2483–2488.Google Scholar
29. Zhang, Z., “A flexible new technique for camera calibration,” Tpami 22 (11), 1330–1334 (2000).Google Scholar
30. Swensen, J., Lin, M., Okamura, A. and Cowan, N., “Torsional dynamics of steerable needles: Modeling and fluoroscopic guidance,” IEEE Trans. Biomed. Eng. 61 (11), 2707–2717 (2014).Google Scholar