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.
R L Siegel, K D Miller, A Jemal. Cancer statistics. CA: A Cancer Journal for Clinicians, 2020, 70( 1): 7– 30 https://doi.org/10.3322/caac.21590
2
H B Carter. American Urological Association (AUA) Guideline on prostate cancer detection: process and rationale. BJU International, 2013, 112( 5): 543– 547 https://doi.org/10.1111/bju.12318
3
S Jiang, Y P Yang, Z Y Yang, Z Zhang, S Liu. Design and experiments of ultrasound image-guided multi-DOF robot system for brachytherapy. Transactions of Tianjin University, 2017, 23( 5): 479– 487 https://doi.org/10.1007/s12209-017-0067-9
4
T L Thomas, V K Venkiteswaran, G K Ananthasuresh, S Misra. Surgical applications of compliant mechanisms: a review. Journal of Mechanisms and Robotics, 2021, 13( 2): 020801– https://doi.org/10.1115/1.4049491
5
O Ukimura, N Hirahara, A Fujihara, T Yamada, T Iwata, K Kamoi, K Okihara, H Ito, T Nishimura, T Miki. 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 https://doi.org/10.1111/j.1442-2042.2010.02617.x
6
A K Singh, J Kruecker, S Xu, N Glossop, P Guion, K Ullman, P L Choyke, B J Wood. Initial clinical experience with real-time transrectal ultrasonography-magnetic resonance imaging fusion-guided prostate biopsy. BJU International, 2008, 101( 7): 841– 845 https://doi.org/10.1111/j.1464-410X.2007.07348.x
7
C Poquet, P Mozer, M A Vitrani, G Morel. An endorectal ultrasound probe comanipulator with hybrid actuation combining brakes and motors. IEEE/ASME Transactions on Mechatronics, 2015, 20( 1): 186– 196 https://doi.org/10.1109/TMECH.2014.2314859
8
S Lim, C Jun, D Chang, D Petrisor, M Han, D Stoianovici. Robotic transrectal ultrasound guided prostate biopsy. IEEE Transactions on Biomedical Engineering, 2019, 66( 9): 2527– 2537 https://doi.org/10.1109/TBME.2019.2891240
9
M Schlüter, C Fürweger, A Schlaefer. Optimizing robot motion for robotic ultrasound-guided radiation therapy. Physics in Medicine and Biology, 2019, 64( 19): 195012– https://doi.org/10.1088/1361-6560/ab3bfb
10
X B Yu, W He, H Y Li, J Sun. 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 https://doi.org/10.1109/TSMC.2019.2963072
11
L Y Kong, G L Chen, H Wang, G Y Huang, D Zhang. 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– https://doi.org/10.1016/j.rcim.2021.102158
12
Z B Li, S Li, X Luo. An overview of calibration technology of industrial robots. IEEE/CAA Journal of Automatica Sinica, 2021, 8( 1): 23– 36 https://doi.org/10.1109/JAS.2020.1003381
13
H F Quintero, L A Mejia, M Diaz-Rodriguez. 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 https://doi.org/10.1007/s12206-019-0644-z
14
Z H Jiang, W G Zhou, H Li, Y Mo, W C Ni, Q Huang. 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 https://doi.org/10.1109/TIE.2017.2748058
15
Y H Gan, J J Duan, X Z Dai. A calibration method of robot kinematic parameters by drawstring displacement sensor. International Journal of Advanced Robotic Systems, 2019, 16( 5): 1– 9 https://doi.org/10.1177/1729881419883072
16
J Li, L D Yu, J Q Sun, H J Xia. A kinematic model for parallel-joint coordinate measuring machine. Journal of Mechanisms and Robotics, 2013, 5( 4): 044501– https://doi.org/10.1115/1.4025121
W J Tian, Z Q Shen, D P Lv, F W Yin. A systematic approach for accuracy design of lower-mobility parallel mechanism. Robotica, 2020, 38( 12): 2173– 2188 https://doi.org/10.1017/S0263574720000028
19
Z Y Zhang. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22( 11): 1330– 1334 https://doi.org/10.1109/34.888718
20
S H Liao, Q Zeng, K F Ehmann, J Cao. Parameter identification and nonparametric calibration of the tri-pyramid robot. IEEE/ASME Transactions on Mechatronics, 2020, 25( 5): 2309– 2317 https://doi.org/10.1109/TMECH.2020.3001021
21
D Daney, Y Papegay, B Madeline. 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 https://doi.org/10.1177/0278364905053185
22
C T Mao, Z W Chen, S Li, X Zhang. Separable nonlinear least squares algorithm for robust kinematic calibration of serial robots. Journal of Intelligent & Robotic Systems, 2021, 101( 1): 2– https://doi.org/10.1007/s10846-020-01268-z
23
W Y Xu, H D Xu, F K Liu, Y Y Tang, Z Wu, X J Wang, J Wang, J Q Feng. Millimeter wave power monitoring in EAST ECRH system. IEEE Access: Practical Innovations, Open Solutions, 2016, 4 : 5809– 5817 https://doi.org/10.1109/ACCESS.2016.2611618
24
J Kennedy, R Eberhart. Particle swarm optimization. In: Proceedings of ICNN'95-International Conference on Neural Networks. Perth: IEEE, 1995, 1942– 1948 https://doi.org/10.1109/ICNN.1995.488968
25
Y Qi, T Sun, Y M Song. Multi-objective optimization of parallel tracking mechanism considering parameter uncertainty. Journal of Mechanisms and Robotics, 2018, 10( 4): 041006– https://doi.org/10.1115/1.4039771
26
F W Flocker, R H Bravo. On global convergence in design optimization using the particle swarm optimization technique. Journal of Mechanical Design, 2016, 138( 8): 081402– https://doi.org/10.1115/1.4033727
27
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
28
Q Zhao, Y H Yue, Q Guan. 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 https://doi.org/10.1109/ICMTMA.2009.620
29
Y X Zheng, Y Liao. Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization. Optik (Stuttgart), 2016, 127( 19): 7865– 7874 https://doi.org/10.1016/j.ijleo.2016.05.145
30
S Shankar Ganesh, A B Koteswara Rao. Error analysis and optimization of a 3-degree of freedom translational parallel kinematic machine. Frontiers of Mechanical Engineering, 2014, 9( 2): 120– 129 https://doi.org/10.1007/s11465-014-0300-3
31
N Qiu, C Park, Y K Gao, J G Fang, G Y Sun, N H Kim. Sensitivity-based parameter calibration and model validation under model error. Journal of Mechanical Design, 2018, 140( 1): 011403– https://doi.org/10.1115/1.4038298
32
D Drignei, Z P Mourelatos, V Pandey, M Kokkolaras. Concurrent design optimization and calibration-based validation using local domains sized by bootstrapping. Journal of Mechanical Design, 2012, 134( 10): 100910– https://doi.org/10.1115/1.4007572
33
M Verner, F F Xi, C Mechefske. Optimal calibration of parallel kinematic machines. Journal of Mechanical Design, 2005, 127( 1): 62– 69 https://doi.org/10.1115/1.1828461
34
S Xu, J Kruecker, B Turkbey, N Glossop, A K Singh, P Choyke, P Pinto, B J Wood. Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies. Computer Aided Surgery, 2008, 13( 5): 255– 264 https://doi.org/10.3109/10929080802364645