Segmentation-informed sampling planning algorithm and dynamic simulation of a bronchial interventional diagnostic robot_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

GUO Chao ¹ , TANG Xiangrong ^2,3 , ZHAO Ke ¹ , LI Shanqing ¹ ,  LIU Jinchang ⁴

1. Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, P. R. China;
2. School of Automation, Beijing Information Science and Technology University, Beijing, 100192, P. R. China;
3. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, P. R. China;
4. High-tech Research Development Center of Ministry of Science and Technology, Beijing, 100044, P. R. China;

Corresponding author：

LIU Jinchang, Email: liujc@htrdc.com

Keywords：

Bronchial intervention; continuum robot; dynamic simulation; path planning

DOI：

10.7507/1007-4848.202206010

Video：

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Objective To propose a path planning method for precise robot-assisted bronchial intervention. Methods In the MuJoCo dynamic simulation environment, a simulation model and a simulated bronchus model which could accurately represent the motion process of the robot were built. Based on the Informed RRT* algorithm, the known spatial information was used to improve the path planning method and the motion characteristics of the robot were simulated to verify the ability of the robot algorithm to reach the target position. Results In the dynamic simulation environment, the robot could move as required, and could explore the target point of the planning task in a short time, and the position accuracy was improved by more than 50% compared with the existing electromagnetic navigation and other methods. Conclusion The established simulation model can restore the motion of the robot, and the robot has the ability to move in the bronchial environment. The proposed method can precisely control the simulated robot to enter the more peripheral airway position. It has the advantages of accuracy and faster speed than traditional manual interventional surgery, and can be used for the human-machine coordinated control task of robot-assisted bronchoscopy.

Citation： GUO Chao, TANG Xiangrong, ZHAO Ke, LI Shanqing, LIU Jinchang. Segmentation-informed sampling planning algorithm and dynamic simulation of a bronchial interventional diagnostic robot. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(10): 1260-1269. doi: 10.7507/1007-4848.202206010 Copy

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17.	Todorov E. Convex and analytically-invertible dynamics with contacts and constraints: Theory and implementation in MuJoCo. 2014 IEEE International Conference on Robotics and Automation (ICRA). 2014: 6054-6061.
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23.	Gammell JD, Barfoot TD, Srinivasa SS. Informed sampling for asymptotically optimal path planning. IEEE Trans Rob, 2018, 34(4): 966-984.
24.	Gammell JD, Srinivasa SS, Barfoot TD. Batch informed trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs. 2015 IEEE International Conference on Robotics and Automation (ICRA). 2015: 3067-3074.
25.	Strub MP, Gammell JD. Advanced BIT* (ABIT*): Sampling-based planning with advanced graph-search techniques. 2020 IEEE International Conference on Robotics and Automation (ICRA). 2020: 130-136.
26.	Strub MP, Gammell JD. Adaptively informed trees (AIT*): Fast asymptotically optimal path planning through adaptive heuristics. 2020 IEEE International Conference on Robotics and Automation (ICRA). 2020: 3191-3198.
27.	Holston AC, Kim DH, Kim JH. Fast-BIT*: Modified heuristic for sampling-based optimal planning with a faster first solution and convergence in implicit random geometric graphs. 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO). 2017: 1892-1899.
28.	Ataka A, Qi R, Liu H, et al. Real-time planner for multi-segment continuum manipulator in dynamic environments. 2016 IEEE International Conference on Robotics and Automation (ICRA). 2016: 3067-3074.
29.	Kuntz A, Torres LG, Feins RH, et al. Motion planning for a three-stage multilumen transoral lung access system. Rep U S, 2015, 2015: 3255-3261.
30.	Reynisson PJ, Hofstad EF, Leira HO, et al. A new visualization method for navigated bronchoscopy. Minim Invasive Ther Allied Technol, 2018, 27(2): 119-126.
31.	Jiang W, Zhou Y, Wang C, et al. Navigation strategy for robotic soft endoscope intervention. Int J Med Robot, 2020, 16(2): e2056.

1. World Health Organization. WHO report on cancer: Setting priorities, investing wisely and providing care for all. Geneva: World Health Organization, 2020.
2. Feng RM, Zong YN, Cao SM, et al. Current cancer situation in China: Good or bad news from the 2018 Global Cancer Statistics? Cancer Commun (Lond), 2019, 39(1): 22.
3. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin, 2019, 69(1): 7-34.
4. 陈少斌, 杨婕, 范秋玲, 等. 肺纯磨玻璃及混合磨玻璃结节(实性成分≤5 mm)为浸润性腺癌的MSCT预测因子分析. 福建医药杂志, 2022, 44(2): 13-18.
5. Chang L, Li J, Zhang R. Liquid biopsy for early diagnosis of non-small cell lung carcinoma: Recent research and detection technologies. Biochim Biophys Acta Rev Cancer, 2022, 1877(3): 188729.
6. Sorger H, Hofstad EF, Amundsen T, et al. A multimodal image guiding system for navigated ultrasound bronchoscopy (EBUS): A human feasibility study. PLoS One, 2017, 12(2): e0171841.
7. 钟长镐, 周子青, 马家骏, 等. 机器人支气管镜系统对比格犬肺外周预置弹簧圈定位及移除的有效性和安全性初步探究. 中华结核和呼吸杂志, 2021, 44(12): 1071-1077.
8. 张如美. 连续体手术机器人感知与路径规划技术研究. 中国科学院大学, 2017.
9. 梁云雷. 腔镜微创手术机器人力反馈主操作手设计与主从控制研究. 哈尔滨工业大学, 2021.
10. Wang W, Zuo L, Xu X. A learning-based multi-RRT approach for robot path planning in narrow passages. J Int Rob Sys, 2018, 90(1-2): 81-100.
11. Torres LG, Alterovitz R. Motion planning for concentric tube robots using mechanics-based models. Rep U S, 2011: 5153-5159.
12. Torres LG, Webster RJ, Alterovitz R. Task-oriented design of concentric tube robots using mechanics-based models. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2012: 4449-4455.
13. Webster RJ, Jones BR. Design and kinematic modeling of constant curvature continuum robots: A review. Intern J Rob Res, 2010, 29(13): 1661-1683.
14. Erez T, Tassa Y, Todorov E. Simulation tools for model-based robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX. 2015 IEEE International Conference on Robotics and Automation (ICRA). 2015: 4397-4404.
15. Hoelscher J, Fu M, Fried I, et al. Backward planning for a multi-stage steerable needle lung robot. IEEE Robot Autom Lett, 2021, 6(2): 3987-3994.
16. Gammell JD, Srinivasa SS, Barfoot TD. Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2014: 2997-3004.
17. Todorov E. Convex and analytically-invertible dynamics with contacts and constraints: Theory and implementation in MuJoCo. 2014 IEEE International Conference on Robotics and Automation (ICRA). 2014: 6054-6061.
18. Lyons LA, Webster RJ, Alterovitz R. Planning active cannula configurations through tubular anatomy. 2010 IEEE International Conference on Robotics and Automation (ICRA). 2010: 2082-2087.
19. Runciman M, Darzi A, Mylonas GP. Soft robotics in minimally invasive surgery. Soft Robot, 2019, 6(4): 423-443.
20. Kato T, King F, Takagi K, et al. Robotized catheter with enhanced distal targeting for peripheral pulmonary biopsy. IEEE-ASME Trans Mech, 2021, 26(5): 2451-2461.
21. Ma X, Song CZ, Chiu PW, et al. Visual servo of a 6-DOF robotic stereo flexible endoscope based on da Vinci research kit (dVRK) system. IEEE Rob Aut Let, 2020, 5(2): 820-827.
22. You XK, Zhang YX, Chen XT, et al. Model-free control for soft manipulators based on reinforcement learning. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2017: 2909-2915.
23. Gammell JD, Barfoot TD, Srinivasa SS. Informed sampling for asymptotically optimal path planning. IEEE Trans Rob, 2018, 34(4): 966-984.
24. Gammell JD, Srinivasa SS, Barfoot TD. Batch informed trees (BIT*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs. 2015 IEEE International Conference on Robotics and Automation (ICRA). 2015: 3067-3074.
25. Strub MP, Gammell JD. Advanced BIT* (ABIT*): Sampling-based planning with advanced graph-search techniques. 2020 IEEE International Conference on Robotics and Automation (ICRA). 2020: 130-136.
26. Strub MP, Gammell JD. Adaptively informed trees (AIT*): Fast asymptotically optimal path planning through adaptive heuristics. 2020 IEEE International Conference on Robotics and Automation (ICRA). 2020: 3191-3198.
27. Holston AC, Kim DH, Kim JH. Fast-BIT*: Modified heuristic for sampling-based optimal planning with a faster first solution and convergence in implicit random geometric graphs. 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO). 2017: 1892-1899.
28. Ataka A, Qi R, Liu H, et al. Real-time planner for multi-segment continuum manipulator in dynamic environments. 2016 IEEE International Conference on Robotics and Automation (ICRA). 2016: 3067-3074.
29. Kuntz A, Torres LG, Feins RH, et al. Motion planning for a three-stage multilumen transoral lung access system. Rep U S, 2015, 2015: 3255-3261.
30. Reynisson PJ, Hofstad EF, Leira HO, et al. A new visualization method for navigated bronchoscopy. Minim Invasive Ther Allied Technol, 2018, 27(2): 119-126.
31. Jiang W, Zhou Y, Wang C, et al. Navigation strategy for robotic soft endoscope intervention. Int J Med Robot, 2020, 16(2): e2056.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Segmentation-informed sampling planning algorithm and dynamic simulation of a bronchial interventional diagnostic robot

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