自 20 世纪 80 年代中期机器人技术被引入到手术室以来,医生和研究人员就一直寻求把更高智能化 的技术与机器人系统进行结合。与常规手术相比,具有更高智能化的手术机器人系统往往需要具备更高的安全性和 准确性,并能够通过配套的感知系统和当前所处的手术阶段来进行决策调整。虽然完全自主的手术机器人系统距真 正的临床使用还有一定距离,但随着技术的积累和发展,具备半自主和部分医生参与决策的机器人智能技术会逐渐 被引入到手术室,并为临床手术的开展提供了更好的平台。本文主要对当前机器人辅助手术及相关智能化技术的进 展进行总结和展望。
Since the first introduction of robotic system into the operating room in the mid-1980s, surgeons and scientific researchers have been trying to integrate higher intelligent technologies with the robot-assisted surgery. Compared with traditional surgeries, the intelligent robotic surgical system should possess higher accuracy and safety, and be able to make decisions based on the results of matched sensing system and current surgical stage. Although fully autonomous robotic surgical systems are still far from real operating room, it is envisioned that the development of related technologies will enable the clinical application of semi-autonomous and partially surgeonscollaborated robotic systems, which would eventually lead to enhanced surgical platforms. In this paper, the current development of autonomy technologies in robot-assisted surgery was introduced and discussed.
收稿日期:2021-08-24 录用日期:2022-05-26
Received Date: 2021-08-24 Accepted Date: 2022-05-26
基金项目:国家自然科学基金青年项目(61803103)
Foundation Item: National Natural Science Foundation of China (61803103)
通讯作者:李长胜,Email:lics@bit.edu.cn
Corresponding Author: LI Changsheng, Email: lics@bit.edu.cn
引用格式:郭靖,吴迪,成卓奇,等 . 机器人辅助手术自主性技术的进展 [J]. 机器人外科学杂志(中英文),2023,4(4): 281-298.
Citation: GUO J, WU D, CHENG Z Q, et al. Progress of autonomous technology on robot-assisted surgery: a review [J]. Chinese Journal of Robotic Surgery, 2023, 4 (4): 281-298.
注:吴迪,原单位为德国慕尼黑工业大学机械工程学院,现单位为比利时鲁汶大学机械工程系
[1] Yip M, Das N. Robot autonomy for surgery. In the encyclopedia of MEDICAL ROBOTICS: volume 1 minimally invasive surgical robotics[M]. New Jersey: World Scientific, 2019: 281-313.
[2] Yang G Z, Cambias J, Cleary K, et al. Medical robotics—regulatory, ethical, and legal considerations for increasing levels of autonomy[J]. Sci Robot, 2017, 2(4): 8638.
[3] Haidegger T. Autonomy for surgical robots: concepts and paradigms[J]. IEEE Trans Med Robot Bionics, 2019, 1(2): 65-76.
[4] 沈桐 , 宋成利 , 徐兆红 . 新型混联腹腔镜手术机器 人的运动学建模与优化 [J]. 机械科学与技术 , 2016, 35(1): 56-62.
[5] Degani A, Choset H, Wolf A, et al. Highly articulated robotic probe for minimally invasive surgery[C]// Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. Piscataway: IEEE, 2006: 4167-4172.
[6] Ota T, Degani A, Schwartzman D, et al. A highly articulated robotic surgical system for minimally invasive surgery[J]. Ann Thorac Surg, 2009, 87(4): 1253-1256.
[7] XU K, ZHAO J R, FU M X. Development of the SJTU unfoldable robotic system (SURS) for single port laparoscopy[J]. IEEE ASME Trans Mechatron, 2014, 20(5): 2133-2145.
[8] Kim Y H, Park Y J, In H K, et al. Design concept of hybrid instrument for laparoscopic surgery and its verification using scale model test[J]. IEEE ASME Trans Mechatron, 2015, 21(1): 142-153.
[9] Breedveld P, Stassen H G, Meijer D W, et al. Manipulation in laparoscopic surgery: overview of impeding effects and supporting aids[J]. J Laparoendosc Adv Surg Tech A, 1999, 9(6): 469-480.
[10] Manzey D, Strauss G, Trantakis C, et al. Automation in surgery: a systematic approach[J]. Surg Technol Int, 2009, PMID: 19579188.
[11] Harris S J, Arambula-Cosio F, Mei Q, et al. The Probot—an active robot for prostate resection[J]. Proc Inst Mech Eng H, 1997, 211(4): 317-325.
[12] Mei Q, Harris S J, Arambula-Cosio F, et al. PROBOT— a computer integrated prostatectomy system[C]// In International Conference on Visualization in Biomedical Computing. Berlin, Heidelberg: Springer, 1996: 581-590.
[13] Rodriguez Y, Baena F, Davies B. Robotic surgery: from autonomous systems to intelligent tools[J]. Robotica, 2010, 28(2): 163-170.
[14] Jakopec M, Harris S J, Baena y, et al. Acrobot: a “handson” robot for total knee replacement surgery[C]// In 7th International Workshop on Advanced Motion Control. Proceedings (Cat. No. 02TH8623). Piscataway: IEEE. 2002: 116-120.
[15] Hagag B, Abovitz R, Kang H, et al. RIO: Roboticarm interactive orthopedic system MAKOplasty: user interactive haptic orthopedic robotics[M]. Boston, MA: Springer, 2011: 219-246.
[16] Eggers G, Mühling J, Marmulla R. Image-to-patient registration techniques in head surgery[J]. Int J Oral Maxillofac Surg, 2006, 35(12): 1081-1095.
[17] Moustris G P, Hiridis S C, Deliparaschos K M, et al. Evolution of autonomous and semi-autonomous robotic surgical systems: a review of the literature[J]. Int J Med Robot, 2011, 7(4): 375-392.
[18] Sayeh S, Wang J, Main W T, et al. Respiratory motion tracking for robotic radiosurgery. In Treating tumors that move with respiration[M]. Berlin, Heidelberg: Springer, 2007: 15-29.
[19] Chen Z H, Deguet A, Taylor R, et al. An open-source hardware and software platform for telesurgical robotics research[J]. SACAI Workshop at MICCAI 2013, 2013. DOI: 10.54294/2dcog6.
[20] Murali A, Sen S, Kehoe B, et al. Learning by observation for surgical subtasks: multilateral cutting of 3D viscoelastic and 2D orthotropic tissue phantoms[C]// In 2015 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE. 2015: 1202- 1209.
[21] Hannaford B, Rosen J, Friedman D W, et al. Raven-II: an open platform for surgical robotics research[J]. IEEE Trans Biomed Eng, 2012, 60(4): 954-959.
[22] Hu D, Gong Y, Hannaford B, et al. Semi-autonomous simulated brain tumor ablation with RAVEN Ⅱ surgical robot using behavior tree[C]// In 2015 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, 2015: 3868-3875.
[23] Kehoe B, Kahn G, Mahler J, et al. Autonomous multilateral debridement with the raven surgical robot[C]// In 2014 IEEE International Conference on Robotics and Automation (ICRA) . Piscataway: IEEE, 2014: 1432-1439.
[24] Su H, Danioni A, Mira R M, et al. Experimental validation of manipulability optimization control of a 7-DoF serial manipulator for robot-assisted surgery[J]. Int J Med Robot, 2021, 17(1): 1-11.
[25] Maris B, Tenga C, Vicario R, et al. Toward autonomous robotic prostate biopsy: a pilot study[J]. Int J Comput Assist Radiol Surg, 2021. DOI: 10.1007/s11548-021- 02437-7.
[26] TANG A, CAO Q, TAN H, et al. Motion Control of a Master-Slave Minimally Invasive Surgical Robot based on the Hand-Eye-Coordination. In Computer Aided Surgery[M]. Tokyo: Springer, 2016: 57-71. [27] LUO D, LIU Y, ZHU H, et al. The MicroHand S robotic-assisted versus Da Vinci robotic-assisted radical resection for patients with sigmoid colon cancer: a single-center retrospective study[J]. Surg Endosc, 2020, 34(8): 3368-3374.
[28] LI C, GU X, XIAO X, et al. Flexible robot with variable stiffness in transoral surgery[J]. IEEE ASME Trans Mechatron, 2019, 25(1): 1-10.
[29] FENG M, FU Y, PAN B, et al. Development of a medical robot system for minimally invasive surgery[J]. Int J Med Robot Comp, 2012, 8(1): 85-96.
[30] Sun L W, Van Meer F, Bailly Y, et al. Design and development of a da vinci surgical system simulator[C]//2007 International Conference on Mechatronics and Automation. Piscataway: IEEE, 2007: 1050-1055.
[31] Kim D K, Park D W, Rha K H. Robot-assisted partial nephrectomy with the REVO-I robot platform in porcine models[J]. Eur Urol, 2016, 69(3): 541-542.
[32] YUAN X, LIU D, GONG M. Design and research on a shape memory alloy-actuated single-port laparoscopic surgical robot[C]// 2014 IEEE International Conference on Mechatronics and Automation. Piscataway: IEEE, 2014: 1654-1658.
[33] Eastwood K, Looi T, Naguib H E, et al. Fluidic actuators for minimally invasive neurosurgical instruments[C]// In The Hamlyn Symposium on Medical Robotics. 2014: 75.
[34] Garbin N, Di Natali C, Buzzi J, et al. Laparoscopic tissue retractor based on local magnetic actuation[J]. J Med Device, 2015, 9(1): 011005.
[35] ZHANG L, YE M, CHAN P L, et al. Real-time surgical tool tracking and pose estimation using a hybrid cylindrical marker[J]. Int J Comput Assist Radiol Surg, 2017, 12(6): 921-930.
[36] Pratt P, Jaeger A, Hughes-Hallett A, et al. Robust ultrasound probe tracking: initial clinical experiences during robot-assisted partial nephrectomy[J]. Int J Comput Assist Radiol Surg, 2015, 10(12): 1905-1913.
[37] ZHAO Z, CAI T, CHANG F, et al. Real-time surgical instrument detection in robot-assisted surgery using a convolutional neural network cascade[J]. Healthc Technol Lett, 2019, 6(6): 275.
[38] Zhao Z, Voros S, Weng Y, et al. Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learningbased method[J]. Comput Assist Surg (Abingdon), 2017, 22(sup1): 26-35.
[39] CLaina I, Rieke N, Rupprecht C, et al. Concurrent segmentation and localization for tracking of surgical instruments[C]// In International conference on medical image computing and computer-assisted intervention. Cham, Switzerland: Springer, 2017: 664-672.
[40] Nwoye C I, Mutter D, Marescaux J, et al. Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos[J]. Int J Comput Assist Radiol Surg, 2019, 14(6): 1059-1067.
[41] Krupa A, Gangloff J, Doignon C, et al. Autonomous 3-D positioning of surgical instruments in robotized laparoscopic surgery using visual servoing[J]. IEEE Trans Rob Autom, 2003, 19(5): 842-853.
[42] Kranzfelder M, Schneider A, Fiolka A, et al. Real-time instrument detection in minimally invasive surgery using radiofrequency identification technology[J]. J Surg Res, 2013, 185(2): 704-710.
[43] Ha X T, Ourak M, Al-Ahmad O, et al. Robust catheter tracking by fusing electromagnetic tracking, fiber bragg grating and sparse fluoroscopic images[J].IEEE Sens J, 2021, 21( 20): 23422-23434.
[44] Penza V, Ortiz J, Mattos L S, et al. Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery[J]. Int J Comput Assist Radiol Surg, 2016, 11(2): 197-206. [45] Moreira P, Zemiti N, Liu C, et al. Viscoelastic model based force control for soft tissue interaction and its application in physiological motion compensation[J]. Comput Methods Programs Biomed, 2014, 116(2): 52- 67.
[46] Bebek O, Cavusoglu M C. Intelligent control algorithms for robotic-assisted beating heart surgery[J]. IEEE Trans Robot, 2007, 23(3): 468-480.
[47] Kim U, Lee D H, Yoon W J, et al. Force sensor integrated surgical forceps for minimally invasive robotic surgery[J]. IEEE Trans Robot, 2015, 31(5): 1214-1224.
[48] Suzuki H, Masuda H, Hongo K, et al. Development and testing of force-sensing forceps using FBG for bilateral micro-operation system[J]. IEEE Robot Autom Lett, 2018, 3(4): 4281-4288.
[49] Back J, Dasgupta P, Seneviratne L, et al. September. Feasibility study-novel optical soft tactile array sensing for minimally invasive surgery[C]// In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Piscataway: IEEE, 2015: 1528-1533.
[50] Trejos A L, Jayender J, Perri M T, et al. Robotassisted tactile sensing for minimally invasive tumor localization[J]. Int J Rob Res, 2009, 28(9): 1118-1133.
[51] Moccia S, De Momi E, Mattos, et al. Supervised tissue classification in optical images: towards new applications of surgical data science[M]. 2018, https: // nearlab.polimi.it/wp-content/uploads/2019/02/thesis_ moccia_compressed.pdf.
[52] Dell’Oglio P, Meershoek P, Maurer T, et al. A DROPIN gamma probe for robot-assisted radioguided surgery of lymph nodes during radical prostatectomy[J]. Eur Urol, 2021, 79(1): 124-132.
[53] Cheng Z, Dall’Alba D, Foti S, et al. Design and integration of electrical bio-impedance sensing in surgical robotic tools for tissue identification and display[J]. Front Robot AI, 2019. DOI: 10.3389/ frobt.2019.00055.
[54] Cheng Z Q, Schwaner K L, Dall’Alba D, et al. An electrical bioimpedance scanning system for subsurface tissue detection in robot assisted minimally invasive surgery[J]. IEEE Trans Biomed Eng, 2021. DOI: 10.1109/TBME.2021.3091326.
[55] Salman H, Ayvali E, Srivatsan R A, et al. Trajectoryoptimized sensing for active search of tissue abnormalities in robotic surgery[C]// In 2018 IEEE International Conference on Robotics and Automation (ICRA) . Piscataway: IEEE, 2018: 5356-5363.
[56] Garg A, Sen S, Kapadia R, et al. Tumor localization using automated palpation with gaussian process adaptive sampling[C]// In 2016 IEEE International Conference on Automation Science and Engineering (CASE), Piscataway: IEEE, 2016: 194-200.
[57] YAN Y, PAN J. Fast Localization and Segmentation of Tissue Abnormalities by Autonomous Robotic Palpation[J]. IEEE Robot Autom Lett, 2021, 6(2): 1707- 1714.
[58] Li Y, Richter F, Lu J, et al. Super: A surgical perception framework for endoscopic tissue manipulation with surgical robotics[J]. IEEE Robot Autom Lett, 2020, 5(2): 2294-2301.
[59] Ren H, Rank D, Merdes M, et al. Multisensor data fusion in an integrated tracking system for endoscopic surgery[J]. IEEE Trans Inf Technol Biomed, 2011, 16(1): 106-111.
[60] Rosen J, Hannaford B, Satava R M. Medical devices: surgical and image guided technologies[M]. Wiley, 2011: 301-306.
[61] CHEN Y, WU Z, YANG B, et al. Review of surgical robotic systems for keyhole and endoscopic procedures: state of the art and perspectives[J]. Front Med, 2020, 14(4): 382-403.
[62] Simorov A, Otte R S, Kopietz C M, et al. Review of surgical robotics user interface: what is the best way to control robotic surgery?[J]. Surg Endosc, 2012, 26(8): 2117-2125.
[63] ZHAO Y, XING H, GUO S, et al. A novel noncontact detection method of surgeon’s operation for a masterslave endovascular surgery robot[J]. Med Biol Eng Comput, 2020, 58(4): 871-885.
[64] Attanasio A, Scaglioni B, De Momi E, et al. Autonomy in surgical robotics[J]. Annu Rev Control Robot Auton Syst, 2020, 4(1): 441-463.
[65] Ruszkowski A, Mohareri O, Lichtenstein S, et al. On the feasibility of heart motion compensation on the daVinci® surgical robot for coronary artery bypass surgery: Implementation and user studies[C]// In 2015 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, 2015: 4432- 4439.
[66] Yuen S G, Kettler D T, Novotny P M, et al. Robotic motion compensation for beating heart intracardiac surgery[J]. Int J Rob Res, 2009, 28(10): 1355-1372.
[67] 肖晶晶 , 杨洋 , 沈丽君 , 等 , 视网膜显微手术机器 人的约束运动规划及仿真 [J]. 机器人 , 2018, 40(6): 870-877.
[68] Fontanelli G A, Yang G Z and Siciliano B. A comparison of assistive methods for suturing in MIRS[C]// In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE, 2018: 4389-4395.
[69] Azizian M, Khoshnam M, Najmaei N, et al. Visual servoing in medical robotics: a survey. Part I: endoscopic and direct vision imaging-techniques and applications[J]. Int J Med Robot Comp, 2014, 10(3): 263-274.
[70] Krupa A, Gangloff J, Doignon C, et al. Autonomous 3-D positioning of surgical instruments in robotized laparoscopic surgery using visual servoing[J]. IEEE Trans Rob Autom, 2003, 19(5): 842-853.
[71] Hoeckelmann M, Rudas I J, Fiorini P, et al. Current capabilities and development potential in surgical robotics[J]. Int J Adv Robot Syst, 2015, 12(5): 61.
[72] Münzer B, Schoeffmann K, Böszörmenyi L. Content-based processing and analysis of endoscopic images and videos: a survey[J]. Multimed Tools Appl, 2018, 77(1): 1323-1362.
[73] Wei G Q, Arbter K, Hirzinger G. Real-time visual servoing for laparoscopic surgery. Controlling robot motion with color image segmentation[J]. IEEE Eng Med Biol Mag, 1997, 16(1): 40-45.
[74] Voros S, Long J A, Cinquin P. Automatic detection of instruments in laparoscopic images: a first step towards high-level command of robotic endoscopic holders[J]. Int J Rob Res, 2007, 26(11-12): 1173-1190.
[75] Pezzementi Z, Voros S, Hager G D. Articulated object tracking by rendering consistent appearance parts[C]// In 2009 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2009: 3940-3947.
[76] Nageotte F, Zanne P, Doignon C, et al. Visual servoing-based endoscopic path following for robotassisted laparoscopic surgery[C]// In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE, 2006: 2364-2369.
[77] Frangi A F, Schnabel J A, Davatzikos C, et al. Medical image computing and computer assisted interventionMICCAI 2018[M]. Cham, Switzerland: Springer, 2018. DOI: 10.1007/978-3-030-00931-1.
[78] Ye M L, Zhang L, Giannarou S, et al. Real-time 3d tracking of articulated tools for robotic surgery[C]// In International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham, Switzerland: Springer, 2016: 386-394.
[79] Wolf R, Duchateau J, Cinquin P, et al. 3D tracking of laparoscopic instruments using statistical and geometric modeling[C]// In International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Heidelberg: Springer, 2011: 203-210.
[80] Allan M, Ourselin S, Thompson S, et al. Toward detection and localization of instruments in minimally invasive surgery[J]. IEEE Trans Biomed Eng, 2012, 60(4): 1050-1058.
[81] Reiter A, Allen P K, Zhao T. Appearance learning for 3D tracking of robotic surgical tools[J]. Int J Rob Res, 2014, 33(2): 342-356.
[82] Reiter A, Goldman R E, Bajo A, et al. A learning algorithm for visual pose estimation of continuum robots[C]// In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE, 2011: 2390-2396.
[83] Garcia-Peraza-Herrera L C, Li W, Fidon L, et al. Toolnet: holistically-nested real-time segmentation of robotic surgical tools[C]// In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE, 2017: 5717-5722.
[84] Shvets A A, Rakhlin A, Kalinin A A, et al. Automatic instrument segmentation in robot-assisted surgery using deep learning[C]// In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Piscataway: IEEE, 2018: 624-628.
[85] Shademan A, Decker R S, Opfermann J D, et al. Supervised autonomous robotic soft tissue surgery[J]. Sci Transl Med, 2016. DOI: 10.1126/scitranslmed.aad9398.
[86] Nakamoto M, Ukimura O, Gill I S, et al. Realtime organ tracking for endoscopic augmented reality visualization using miniature wireless magnetic tracker[C]// In International Workshop on Medical Imaging and Virtual Reality. Berlin, Heidelberg: Springer, 2008: 359-366.
[87] Nosrati M S, Peyrat J M, Abinahed J, et al. Efficient multi-organ segmentation in multi-view endoscopic videos using pre-operative priors[C]// In International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham, Switzerland: Springer, 2014: 324-331.
[88] Bilodeau G A, Shu Y, Cheriet F. Multistage graph-based segmentation of thoracoscopic images[J]. Comput Med Imaging Graph, 2006, 30(8): 437-446.
[89] Tjoa M P, Krishnan S M, Kugean C, et al. Segmentation of clinical endoscopic image based on homogeneity and hue[C]// In 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway: IEEE, 2001, 3: 2665-2668.
[90] Figueiredo I N, Moreno J C, Prasath V S, et al. A segmentation model and application to endoscopic images[C]// In International Conference Image Analysis and Recognition. Berlin, Heidelberg: Springer, 2012: 164-171.
[91] Wu S Q, Nakao M, Matsuda T. Continuous lung region segmentation from endoscopic images for intra-operative navigation[J]. Comput Biol Med, 2017. DOI: 10.1016/ j.compbiomed.2017.05.029.
[92] Bodenstedt S, Wagner M, Mayer B, et al. Image-based laparoscopic bowel measurement[J]. Int J Comput Assist Radiol Surg, 2016, 11(3): 407-419.
[93] Chhatkuli A, Bartoli A, Malti A, et al. Live image parsing in uterine laparoscopy[C]// In 2014 IEEE 11th international symposium on biomedical imaging (ISBI). Piscataway: IEEE, 2014: 1263-1266.
[94] Moccia S, Foti S, Rossi S M, et al. FCNN-based segmentation of kidney vessels-Towards constraints definition for safe robot-assisted nephrectomy[C]// In Joint Workshop on New Technologies for Computer/ Robot Assisted Surgery. 2018: 1-2.
[95] Rosen J, Hannaford B, Richards C G, et al. Markov modeling of minimally invasive surgery based on tool/ tissue interaction and force/torque signatures for evaluating surgical skills[J]. IEEE Trans Biomed Eng, 2001, 48(5): 579-591.
[96] Lin H C, Shafran I, Yuh D, et al. Towards automatic skill evaluation: detection and segmentation of robotassisted surgical motions[J]. Comput Aided Surg, 2006, 11(5): 220-230.
[97] Padoy N, Blum T, Ahmadi S A, et al. Statistical modeling and recognition of surgical workflow[J]. Med Image Ana, 2012, 16(3): 632-641.
[98] Ahmidi N, Tao L, Sefati S, et al. A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery[J]. IEEE Trans Biomed Eng, 2017, 64(9): 2025-2041.
[99] Tao L, Elhamifar E, Khudanpur S, et al. Sparse hidden markov models for surgical gesture classification and skill evaluation[C]// In International conference on information processing in computer-assisted interventions. Heidelberg, Berlin: Springer, 2012: 167- 177.
[100] Zappella L, Béjar B, Hager G, et al. Surgical gesture classification from video and kinematic data[J]. Med Image Ana, 2013, 17(7): 732-745.
[101] Despinoy F, Bouget D, Forestier G, et al. Unsupervised trajectory segmentation for surgical gesture recognition in robotic training[J].IEEE Trans Biomed Eng, 2015, 63(6): 1280-1291.
[102] Murali A, Garg A, Krishnan S, et al. Tsc-dl: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with deep learning[C]// In 2016 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, 2016: 4150-4157.
[103] Krishnan S, Garg A, Patil S, et al. Transition state clustering: unsupervised surgical trajectory segmentation for robot learning[J]. Int J Rob Res, 2017, 36(13-14): 1595-1618.
[104] Kang H, Wen J T. Endobot: a robotic assistant in minimally invasive surgeries[C]// In Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164). Piscataway: IEEE, 2001, 2: 2031-2036.
[105] Nageotte F, Zanne P, Doignon C, et al. Stitching planning in laparoscopic surgery: towards robot-assisted suturing[J]. Int J Med Robot, 2009, 28(10): 1303-1321.
[106] Jackson R C, Çavuşoğlu M C. Needle path planning for autonomous robotic surgical suturing[C]// In 2013 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2013: 1669-1675.
[107] Sen S, Garg A, Gealy D V, et al. Automating multithrow multilateral surgical suturing with a mechanical needle guide and sequential convex optimization[C]// In 2016 IEEE international conference on robotics and automation (ICRA). Piscataway: IEEE, 2016: 4178- 4185.
[108] Staub C, Osa T, Knoll A, et al. Automation of tissue piercing using circular needles and vision guidance for computer aided laparoscopic surgery[C]// In 2010 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2010: 4585-4590.
[109] Leonard S, Wu K L, Kim Y, et al. Smart tissue anastomosis robot (STAR): a vision-guided robotics system for laparoscopic suturing[J]. IEEE Trans Biomed Eng, 2014, 61(4): 1305-1317.
[110] ZHONG F X, WANG Y Q, WANG Z R, et al. Dual-arm robotic needle insertion with active tissue deformation for autonomous suturing[J]. IEEE Robot Autom Lett, 2019, 4(3): 2669-2676.
[111] Mayer H, Gomez F, Wierstra D, et al. A system for robotic heart surgery that learns to tie knots using recurrent neural networks[J]. Adv Robotics, 2008, 22(13-14): 1521-1537.
[112] Mayer H, Nagy I, Burschka D, et al. Automation of manual tasks for minimally invasive surgery[C]// In Fourth International Conference on Autonomic and Autonomous Systems (ICAS’08). Piscataway: IEEE, 2008: 260-265.
[113] Van Den Berg J, Miller S, Duckworth D, et al. Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations[C]// In 2010 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2010: 2074-2081.
[114] Schulman J, Gupta A, Venkatesan S, et al. A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario[C]// In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE, 2013: 4111-4117.
[115] Kehoe B, Kahn G, Mahler J, et al. Autonomous multilateral debridement with the raven surgical robot[C]// In 2014 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, 2014: 1432-1439.
[116] Le H N, Opfermann J D, Kam M, et al. Semi-autonomous laparoscopic robotic electro-surgery with a novel 3D endoscope[C]// In 2018 IEEE International Conference on Robotics and Automation (ICRA). Piscataway: IEEE, 2018: 6637-6644.
[117] Nichols K A, Okamura A M. Autonomous robotic palpation: Machine learning techniques to identify hard inclusions in soft tissues[C]// In 2013 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2013: 4384-4389.
[118] Nagy D Á, Nagy T D, Elek R, et al. Ontology-based surgical subtask automation, automating blunt dissection[J]. J Med Robot Res, 2018. DOI: 10.1142/ S2424905X18410052.
[119] Marbán A, Casals A, Fernández J, et al. Haptic feedback in surgical robotics: Still a challenge[C]// In ROBOT2013: First Iberian Robotics Conference. Cham, Switzerland: Springer. 2014: 245-253.
[120] Van den Dobbelsteen J J, Lee R A, van Noorden M, et al. Indirect measurement of pinch and pull forces at the shaft of laparoscopic graspers[J]. Med Biol Eng Comput, 2012, 50(3): 215-221.
[121] Wu D, Zhang Y, Ourak M, et al. Hysteresis modeling of robotic catheters based on long short-term memory network for improved environment reconstruction[J]. IEEE Robot Autom Lett, 2021, 6(2): 2106-2113.
[122] Aviles A I, Casals A. On genetic algorithms optimization for heart motion compensation[C]// In ROBOT2013: First Iberian Robotics Conference. Cham, Switzerland: Springer. 2014, 252: 237-244.
[123] Francis P, Eastwood K W, Bodani V, et al. Miniaturized instruments for the da Vinci research kit: design and implementation of custom continuum tools[J]. IEEE Robot Autom Mag, 2017, 24(2): 24-33. [124] Wu D, Li G, Patel N, et al. Remotely actuated needle driving device for mri-guided percutaneous interventions[C]// In 2019 International Symposium on Medical Robotics (ISMR) . Piscataway: IEEE, 2019: 1-7.
[125] Ha X T, Ourak M, Al-Ahmad O, et al. Robust catheter tracking by fusing electromagnetic tracking, fiber bragg grating and sparse fluoroscopic images[J]. IEEE Sens J, 2021, 21(20): 23422-23434.
[126] Graur F, Frunza M, Elisei R, et al. Ethics in Robotic Surgery and Telemedicine[C]// Pisla D, Ceccarelli M, Husty M, et al. In New Trends in Mechanism Science. Berlin: Springer, 2010: 457-465.
[127] O’Sullivan S, Nevejans N, Allen C, et al. Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery[J]. Int J Med Robot Comp, 2019, 15(1): e1968.
[128] YANG C, GUO S X, BAO X Q, et al. A vascular interventional surgical robot based on surgeon’s operating skills[J]. Med Biol Eng Comput, 2019, 57(9): 1999-2010.
[129] Su H, Mariani A, Ovur S E, et al. Toward teaching by demonstration for robot-assisted minimally invasive surgery[J]. IEEE Trans Autom Sci Eng, 2021, 18(2): 484-494.
[130] GUO S X, WANG Y X, ZHAO Y, et al. A surgeon’s operating skills-based non-interference operation detection method for novel vascular interventional surgery robot systems[J]. IEEE Sens J, 2019, 20(7): 3879-3891.
[131] Shafiei S B, Hussein A A, Guru K A. Cognitive learning and its future in urology: surgical skills teaching and assessment[J]. Curr Opin Urol, 2017, 27(4): 342-347.
[132] Kinross J M, Mason S E, Mylonas G, et al. Nextgeneration robotics in gastrointestinal surgery[J]. Nat Rev Gastroenterol Hepatol, 2020, 17(7): 430-440.
[133] De Rossi G, Minelli M, Sozzi A, et al. Cognitive robotic architecture for semi-autonomous execution of manipulation tasks in a surgical environment[C]// In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE, 2019: 7827-7833.