Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (01): 68-76.doi: 10.12052/gdutxb.210120

Previous Articles     Next Articles

Dynamic Modeling and H∞ Control Method of an MRI-compatible Hydraulically Needle Insertion Robot

Huang Fang, Qiu Yu-fu, Guo Jing   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2021-08-10 Online:2023-01-25 Published:2023-01-12

HTML

PDF (PC)

1628

Abstract: Brain tumors are a major problem affecting the health status of the nation. To determine the extent of brain tumor in order to determine the next step in treatment, a puncture biopsy procedure of brain tumor tissue is often required. Magnetic resonance imaging (MRI) is commonly used to detect brain tumors due to its better soft-tissue resolution. Therefore, research related to MRI-compatible robots is necessary. Based on an MRI-compatible hydraulically driven puncture surgery robot, the kinematic model of the robot is derived based on the principle of hydraulic linker, and the dynamic model of the robot is obtained based on the relevant theory of fluid dynamics. In order to realize the accurate control of the designed robot system, the state feedback H∞ control rate of this hydraulically driven system is designed according to the H∞ control theory, which enables the robot to track the target signal quickly and stably. Finally, the average positioning accuracy of the designed robot system in x-axis, y-axis, z-axis, pitch-axis and roll-axis are obtained through experimental studies, and they are, respectively, 0.41 mm, 0.6 mm, 0.67 mm, 0.886° and 1.17°. Experimental results verify the performance of robot-assisted positioning puncture needles, and the dynamic model and control method of the robot have been given, which provide certain reference value for the research of the control algorithm of puncture robots.

Key words: MRI-compatible, needle biopsy, robotic system, H∞ control

CLC Number: 

  • TP242
[1] KHIEWVAN B, TORIGIAN D A, EMAMZADEHFARD S, et al. An update on the role of PET/CT and PET/MRI in ovarian cancer [J]. European Journal of Nuclear Medicine & Molecular Imaging, 2017, 44(6): 1079-1091.
[2] MOREIRA P, BOSKMA K J, MISRA S. Towards MRI-guided flexible needle steering using fiber Bragg grating-based tip tracking[C]// 2017 IEEE International Conference on Robotics and Automation (ICRA) . Singapore: IEEE, 2017: 4849-4854.
[3] BALL M W, ROSS A E, GHABILI K, et al. Safety and feasibility of direct magnetic resonance imaging-guided transperineal prostate biopsy using a novel magnetic resonance imaging-safe robotic device [J]. Urology, 2017, 109: 216-221.
[4] ZHANG T X, WEN Y S, LIU Y H. Developing a parallel robot for MRI-guided breast intervention [J]. IEEE Transactions on Medical Robotics and Bionics, 2020, 2(1): 17-27.
[5] GROENHUIS V, SIEPEL F J, VELTMAN J, et al. Design and characterization of Stormram 4: an MRI-compatible robotic system for breast biopsy[C]// 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vancouver: IEEE, 2017: 928-933.
[6] HE Z, DONG Z, FANG G, et al. Design of a percutaneous MRI-guided needle robot with soft fluid-driven actuator [J]. IEEE Robotics and Automation Letters, 2020, 5(2): 2100-2107.
[7] LI G, NIRAVKUMAR A P, LIU W Q, et al. A fully actuated body-mounted robotic assistant for MRI-guided low back pain injection[C]// 2020 IEEE International Conference on Robotics and Automation (ICRA) . Paris: IEEE, 2020: 5495-5501.
[8] NIRAVKUMAR A P, CHRISTOPHER J N, PAULO A C, et al. An integrated robotic system for MRI-guided neuroablation: preclinical evaluation [J]. IEEE Transactions on Biomedical Engineering, 2020, 67(10): 2990-2999.
[9] GUO Z Y, DONG Z Y, KIT-HANG L, et al. Compact design of a hydraulic driving robot for intra-operative MRI-guided bilateral stereotactic neurosurgery [J]. IEEE Robotics & Automation Letters, 2018, 3(3): 2515-2522.
[10] GYEONG HU K, NIRAVKUMAR P, YAN J W, et al. Shoulder-mounted robot for MRI-guided arthrography: clinically optimized system[C]// 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) . Berlin: IEEE, 2019: 1977-1980.
[11] NIRAVKUMAR A P, YAN J W, DAVID L, et al. Body-mounted robot for image-guided percutaneous interventions: mechanical design and preliminary accuracy evaluation[C]// 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems. Madrid: IEEE, 2018: 1443-1448.
[12] NYCZ C J, GONDOKARYONO R, CARVALHO P, et al. Mechanical validation of an MRI compatible stereotactic neurosurgery robot in preparation for pre-clinical trials[C]// 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Vancouver: IEEE, 2017: 875-881.
[13] FRANCO E, BRUJIC D, REA M, et al. Needle-guiding robot for laser ablation of liver tumors under MRI guidance [J]. IEEE/ASME transactions on mechatronics:A joint publication of the IEEE Industrial Electronics Society and the ASME Dynamic Systems and Control Division, 2016, 21(2): 931-944.
[14] DONG Z Y, GUO Z Y, KIT-HANG L, et al. High-performance continuous hydraulic motor for MR safe robotic teleoperation [J]. IEEE Robotics & Automation Letters, 2019, 4(2): 1964-1971.
[15] FARIMANI F S, MISRA S. Introducing pneuact: parametrically-designed MRI-compatible pneumatic stepper actuator[C]// 2018 IEEE International Conference on Robotics and Automation (ICRA) . Brisbane: IEEE, 2018: 4523-4536.
[16] FRISHMAN S, KIGHT A, PIROZZI I, et al. Enabling in-bore MRI-guided biopsies with force feedback [J]. IEEE Transactions on Haptics, 2020, 13(1): 159-166.
[1] Hu Xiao-min, Long Zu-tao, Li Min. A Multi-objective Recommendation Algorithm Based on User Stratification [J]. Journal of Guangdong University of Technology, 2023, 40(01): 10-18.
[2] Liu Yang, Peng Shi-guo, Ma Hong-zhi, Liao Wei-xin. Dynamic Parameter Identification and Gait Tracking of Lower Limb Exoskeleton Robot [J]. Journal of Guangdong University of Technology, 2022, 39(06): 44-52.
[3] Wang Dong, Huang Rui-yuan, Li Wei-zheng, Huang Zhi-feng. A Research on Docking Position Optimization Method of Mobile Robot for Grasping Task [J]. Journal of Guangdong University of Technology, 2021, 38(06): 53-61.
[4] Guo Xin-de, Chris Hong-qiang Ding. An AGV Path Planning Method for Discrete Manufacturing Smart Factory [J]. Journal of Guangdong University of Technology, 2021, 38(06): 70-76.
[5] Huang Jian-hang, Wang Zhen-you. A Research on Deep Learning Object Detection Algorithm Based on Feature Fusion [J]. Journal of Guangdong University of Technology, 2021, 38(04): 52-58.
[6] Lin Lin, Li Xiao-tao, Xu Yong-li. Hot Spots and Development Trend of TRIZ Theory and Method in China [J]. Journal of Guangdong University of Technology, 2021, 38(02): 10-19.
[7] Jie Yun-fei, Everett Wang, Zhong You-dong, Zhi Kai-xuan, Xiong Chao-wei. An Indoor Positioning Method of Monocular Vision Robot Based on Floor Features [J]. Journal of Guangdong University of Technology, 2020, 37(05): 31-37.
[8] Ye Wei-jie, Gao Jun-li, Jiang Feng, Guo Jing. A Research on a Training Model to Improve the Development Efficiency of Robot Reinforcement Learning [J]. Journal of Guangdong University of Technology, 2020, 37(05): 46-50.
[9] Chen Yu-peng, Gao Wei-qiang, Lu Yi-guang. A Teaching Data Optimization Method of Hand by Hand Teaching Spray Robot [J]. Journal of Guangdong University of Technology, 2020, 37(04): 21-26.
[10] Zeng Han-mei, Huang Zhi-feng, Lin Zhong-xiu, Kou Xiao-dong, Zhang Yun. An Investigation into Human Anticipatory Postural Adjustments on Cooperative Task Motion Control and Posture Control [J]. Journal of Guangdong University of Technology, 2019, 36(05): 25-32.
[11] Liu Biao, Huang Zhi-feng, Zhang Yun. A Research on Biped Robot's Obstacle Stepping Over and Avoidance Assisted by External Forces [J]. Journal of Guangdong University of Technology, 2019, 36(02): 62-69.
[12] Wu Yun-xiong, Zeng Bi. Trajectory Tracking and Dynamic Obstacle Avoidance of Mobile Robot Based on Deep Reinforcement Learning [J]. Journal of Guangdong University of Technology, 2019, 36(01): 42-50.
[13] Wang Sheng-min, Lin Wei, Zeng Bi. Path Planning of Opposite Q Learning Robot Based on Virtual Sub-Target in Unknown Environment [J]. Journal of Guangdong University of Technology, 2019, 36(01): 51-56,62.
[14] Yang Meng-jun, Su Cheng-yue, Chen Jing, Zhang Jie-xin. Loop Closure Detection for Visual SLAM Using Convolutional Neural Networks [J]. Journal of Guangdong University of Technology, 2018, 35(05): 31-37.
[15] Chi Peng-ke, Su Cheng-yue. A Research on Monocular Visual Odometry for Mobile Robots [J]. Journal of Guangdong University of Technology, 2017, 34(05): 40-44.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © 2017 Journal of Guangdong University of Technology
Add：729 East Dongfeng RD., Guangzhou PRC. Postcode: 510090
Tel：020-37626653,020-37626139,020-37626396
Email：xbzrb@gdut.edu.cn
Supported:Beijing Magtech