Abstract
Applying a robot system in ultrasound-guided percutaneous intervention is an effective approach for prostate cancer diagnosis and treatment. The limited space for robot manipulation restricts structure volume and motion. In this paper, an 8-degree-of-freedom robot system is proposed for ultrasound probe manipulation, needle positioning, and needle insertion. A novel parallel structure is employed in the robot system for space saving, structural rigidity, and collision avoidance. The particle swarm optimization method based on informative value is proposed for kinematic parameter identification to calibrate the parallel structure accurately. The method identifies parameters in the modified kinematic model stepwise according to parameter discernibility. Verification experiments prove that the robot system can realize motions needed in targeting. By applying the calibration method, a reasonable, reliable forward kinematic model is built, and the average errors can be limited to 0.963 and 1.846 mm for insertion point and target point, respectively.
Article PDF
Similar content being viewed by others
References
Siegel R L, Miller K D, Jemal A. Cancer statistics. CA: A Cancer Journal for Clinicians, 2020, 70(1): 7–30
Carter H B. American Urological Association (AUA) Guideline on prostate cancer detection: process and rationale. BJU International, 2013, 112(5): 543–547
Jiang S, Yang Y P, Yang Z Y, Zhang Z, Liu S. Design and experiments of ultrasound image-guided multi-DOF robot system for brachytherapy. Transactions of Tianjin University, 2017, 23(5): 479–487
Thomas T L, Venkiteswaran V K, Ananthasuresh G K, Misra S. Surgical applications of compliant mechanisms: a review. Journal of Mechanisms and Robotics, 2021, 13(2): 020801
Ukimura O, Hirahara N, Fujihara A, Yamada T, Iwata T, Kamoi K, Okihara K, Ito H, Nishimura T, Miki T. Technique for a hybrid system of real-time transrectal ultrasound with preoperative magnetic resonance imaging in the guidance of targeted prostate biopsy. International Journal of Urology, 2010, 17(10): 890–893
Singh A K, Kruecker J, Xu S, Glossop N, Guion P, Ullman K, Choyke P L, Wood B J. Initial clinical experience with real-time transrectal ultrasonography-magnetic resonance imaging fusion-guided prostate biopsy. BJU International, 2008, 101(7): 841–845
Poquet C, Mozer P, Vitrani M A, Morel G. An endorectal ultrasound probe comanipulator with hybrid actuation combining brakes and motors. IEEE/ASME Transactions on Mechatronics, 2015, 20(1): 186–196
Lim S, Jun C, Chang D, Petrisor D, Han M, Stoianovici D. Robotic transrectal ultrasound guided prostate biopsy. IEEE Transactions on Biomedical Engineering, 2019, 66(9): 2527–2537
Schlüter M, Fürweger C, Schlaefer A. Optimizing robot motion for robotic ultrasound-guided radiation therapy. Physics in Medicine and Biology, 2019, 64(19): 195012
Yu X B, He W, Li H Y, Sun J. Adaptive fuzzy full-state and output-feedback control for uncertain robots with output constraint. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(11): 6994–7007
Kong L Y, Chen G L, Wang H, Huang G Y, Zhang D. Kinematic calibration of a 3-PRRU parallel manipulator based on the complete, minimal and continuous error model. Robotics and Computer-Integrated Manufacturing, 2021, 71: 102158
Li Z B, Li S, Luo X. An overview of calibration technology of industrial robots. IEEE/CAA Journal of Automatica Sinica, 2021, 8(1): 23–36
Quintero H F, Mejia L A, Diaz-Rodriguez M. End-effector positioning due to joint clearances: a comparison among three planar 2-DOF parallel manipulators. Journal of Mechanical Science and Technology, 2019, 33(7): 3497–3507
Jiang Z H, Zhou W G, Li H, Mo Y, Ni W C, Huang Q. A new kind of accurate calibration method for robotic kinematic parameters based on the extended Kalman and particle filter algorithm. IEEE Transactions on Industrial Electronics, 2018, 65(4): 3337–3345
Gan Y H, Duan J J, Dai X Z. A calibration method of robot kinematic parameters by drawstring displacement sensor. International Journal of Advanced Robotic Systems, 2019, 16(5): 1–9
Li J, Yu L D, Sun J Q, Xia H J. A kinematic model for parallel-joint coordinate measuring machine. Journal of Mechanisms and Robotics, 2013, 5(4): 044501
Huang T, Liu H T, Chetwynd D G. Generalized Jacobian analysis of lower mobility manipulators. Mechanism and Machine Theory, 2011, 46(6): 831–844
Tian W J, Shen Z Q, Lv D P, Yin F W. A systematic approach for accuracy design of lower-mobility parallel mechanism. Robotica, 2020, 38(12): 2173–2188
Zhang Z Y. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330–1334
Liao S H, Zeng Q, Ehmann K F, Cao J. Parameter identification and nonparametric calibration of the tri-pyramid robot. IEEE/ASME Transactions on Mechatronics, 2020, 25(5): 2309–2317
Daney D, Papegay Y, Madeline B. Choosing measurement poses for robot calibration with the local convergence method and tabu search. The International Journal of Robotics Research, 2005, 24(6): 501–518
Mao C T, Chen Z W, Li S, Zhang X. Separable nonlinear least squares algorithm for robust kinematic calibration of serial robots. Journal of Intelligent & Robotic Systems, 2021, 101(1): 2
Xu W Y, Xu H D, Liu F K, Tang Y Y, Wu Z, Wang X J, Wang J, Feng J Q. Millimeter wave power monitoring in EAST ECRH system. IEEE Access: Practical Innovations, Open Solutions, 2016, 4: 5809–5817
Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks. Perth: IEEE, 1995, 1942–1948
Qi Y, Sun T, Song Y M. Multi-objective optimization of parallel tracking mechanism considering parameter uncertainty. Journal of Mechanisms and Robotics, 2018, 10(4): 041006
Flocker F W, Bravo R H. On global convergence in design optimization using the particle swarm optimization technique. Journal of Mechanical Design, 2016, 138(8): 081402
Gao G B, Liu F, San H J, Wu X, Wang W. Hybrid optimal kinematic parameter identification for an industrial robot based on BPNN-PSO. Complexity, 2018, 4258676
Zhao Q, Yue Y H, Guan Q. A PSO-based ball-plate calibration for laser scanner. In: Proceedings of 2009 International Conference on Measuring Technology and Mechatronics Automation. Zhangjiajie: IEEE, 2009, 2: 479–481
Zheng Y X, Liao Y. Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization. Optik (Stuttgart), 2016, 127(19): 7865–7874
Shankar Ganesh S, Koteswara Rao A B. Error analysis and optimization of a 3-degree of freedom translational parallel kinematic machine. Frontiers of Mechanical Engineering, 2014, 9(2): 120–129
Qiu N, Park C, Gao Y K, Fang J G, Sun G Y, Kim N H. Sensitivity-based parameter calibration and model validation under model error. Journal of Mechanical Design, 2018, 140(1): 011403
Drignei D, Mourelatos Z P, Pandey V, Kokkolaras M. Concurrent design optimization and calibration-based validation using local domains sized by bootstrapping. Journal of Mechanical Design, 2012, 134(10): 100910
Verner M, Xi F F, Mechefske C. Optimal calibration of parallel kinematic machines. Journal of Mechanical Design, 2005, 127(1): 62–69
Xu S, Kruecker J, Turkbey B, Glossop N, Singh A K, Choyke P, Pinto P, Wood B J. Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies. Computer Aided Surgery, 2008, 13(5): 255–264
Acknowledgements
This paper was supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 51521003), the National Natural Science Foundation of China (Grant No. 61803341), and the Self-Planned Task of State Key Laboratory of Robotics and System (Harbin Institute of Technology, China) (Grant No. SKLRS202009B). No conflicts of interest exist in this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format as long as appropriate credit is given to the original author(s) and source, a link to the Creative Commons license is provided, and indicate if changes were made.
The images or other third-party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Visit http://creativecommons.org/licenses/by/4.0/ to view a copy of this license.
About this article
Cite this article
Yan, J., Pan, B. & Fu, Y. Ultrasound-guided prostate percutaneous intervention robot system and calibration by informative particle swarm optimization. Front. Mech. Eng. 17, 3 (2022). https://doi.org/10.1007/s11465-021-0659-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11465-021-0659-x