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Simultaneous Estimation of Shape and Force along Highly Deformable Surgical Manipulators Using Sparse FBG Measurement (2404.16952v1)

Published 25 Apr 2024 in cs.RO

Abstract: Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270 degrees and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space and constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods.

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References (38)
  1. J. Kim, M. de Mathelin, K. Ikuta, and D.-S. Kwon, “Advancement of flexible robot technologies for endoluminal surgeries,” Proceedings of the IEEE, vol. 110, no. 7, pp. 909–931, 2022.
  2. J. Yan, J. Chen, J. Chen, W. Yan, Q. Ding, K. Yan, J. Du, C. P. Lam, G. K. C. Wong, and S. S. Cheng, “A continuum robotic cannula with tip following capability and distal dexterity for intracerebral hemorrhage evacuation,” IEEE Transactions on Biomedical Engineering, vol. 69, no. 9, pp. 2958–2969, 2022.
  3. Y. Lu, R. Wei, B. Li, W. Chen, J. Zhou, Q. Dou, D. Sun, and Y.-h. Liu, “Autonomous intelligent navigation for flexible endoscopy using monocular depth guidance and 3-d shape planning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 1–7.
  4. A. Alian, E. Zari, Z. Wang, E. Franco, J. P. Avery, M. Runciman, B. Lo, F. R. y Baena, and G. Mylonas, “Current engineering developments for robotic systems in flexible endoscopy,” Techniques and Innovations in Gastrointestinal Endoscopy, 2022.
  5. C. Shi, X. Luo, P. Qi, T. Li, S. Song, Z. Najdovski, T. Fukuda, and H. Ren, “Shape sensing techniques for continuum robots in minimally invasive surgery: A survey,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 8, pp. 1665–1678, 2016.
  6. Y. Lu, B. Lu, B. Li, H. Guo, and Y.-H. Liu, “Robust three-dimensional shape sensing for flexible endoscopic surgery using multi-core fbg sensors,” IEEE Robotics and Automation Letters, vol. 6, no. 3, 2021.
  7. C. Zhang, C. Hu, Z. He, Z. Fu, L. Xu, G. Ding, P. Wang, H. Zhang, and X. Ye, “Shape estimation of the anterior part of a flexible ureteroscope for intraoperative navigation,” International Journal of Computer Assisted Radiology and Surgery, vol. 17, no. 10, pp. 1787–1799, 2022.
  8. T. Li, L. Qiu, and H. Ren, “Distributed curvature sensing and shape reconstruction for soft manipulators with irregular cross sections based on parallel dual-fbg arrays,” IEEE/ASME Transactions on Mechatronics, vol. 25, no. 1, pp. 406–417, 2019.
  9. L. Zhang, C. Li, H. Dong, X. Liu, T. Sun, K. T. Grattan, and J. Zhao, “Fiber bragg grating-based sensor system for sensing the shape of flexible needles,” Measurement, vol. 206, p. 112251, 2023.
  10. N. J. Deaton, M. Sheft, and J. P. Desai, “Towards fbg-based shape sensing and sensor drift for a steerable needle,” IEEE/ASME Transactions on Mechatronics, 2023.
  11. J. Wei, S. Wang, J. Li, and S. Zuo, “Novel integrated helical design of single optic fiber for shape sensing of flexible robot,” IEEE Sensors Journal, vol. 17, no. 20, pp. 6627–6636, 2017.
  12. A. Donder and F. R. y Baena, “Kalman-filter-based, dynamic 3-d shape reconstruction for steerable needles with fiber bragg gratings in multicore fibers,” IEEE Transactions on Robotics, vol. 38, no. 4, pp. 2262–2275, 2021.
  13. Y. Lu, W. Chen, B. Li, B. Lu, J. Zhou, Z. Chen, and Y.-H. Liu, “A robust graph-based framework for 3-d shape reconstruction of flexible medical instruments using multi-core fbgs,” IEEE Transactions on Medical Robotics and Bionics, 2023.
  14. K. Wang, X. Wang, J. D.-L. Ho, G. Fang, B. Zhu, R. Xie, Y.-H. Liu, K. W. S. Au, J. Y.-K. Chan, and K.-W. Kwok, “A fast soft robotic laser sweeping system using data-driven modeling approach,” IEEE Transactions on Robotics, 2023.
  15. X. T. Ha, D. Wu, M. Ourak, G. Borghesan, A. Menciassi, and E. Vander Poorten, “Sensor fusion for shape reconstruction using electromagnetic tracking sensors and multi-core optical fiber,” IEEE Robotics and Automation Letters, 2023.
  16. T. Zhang, A. Gao, J. Zhu, B. Zhang, J. Lin, and Y. Ni, “A vascular shape reconstruction method based on multicore fbg sensing,” IEEE Sensors Journal, 2023.
  17. Y. Lu, W. Chen, B. Lu, J. Zhou, Z. Chen, Q. Dou, and Y.-H. Liu, “Adaptive online learning and robust 3-d shape servoing of continuum and soft robots in unstructured environments,” Soft Robotics, 2024.
  18. F. Khan, D. Barrera, S. Sales, and S. Misra, “Curvature, twist and pose measurements using fiber bragg gratings in multi-core fiber: A comparative study between helical and straight core fibers,” Sensors and Actuators A: Physical, vol. 317, p. 112442, 2021.
  19. K. Yang, C. Ke, J. Tian, J. Liu, Z. Guo, and D. Liu, “Three-dimensional curve reconstruction based on material frame and twisted multicore fiber,” IEEE Photonics Journal, vol. 14, no. 6, pp. 1–8, 2022.
  20. R. Xu, A. Yurkewich, and R. V. Patel, “Curvature, torsion, and force sensing in continuum robots using helically wrapped fbg sensors,” IEEE Robotics and Automation Letters, vol. 1, no. 2, pp. 1052–1059, 2016.
  21. T. Li, C. Shi, and H. Ren, “Three-dimensional catheter distal force sensing for cardiac ablation based on fiber bragg grating,” IEEE/ASME transactions on mechatronics, vol. 23, no. 5, pp. 2316–2327, 2018.
  22. Q. Jiang, J. Li, and D. Masood, “Fiber-optic-based force and shape sensing in surgical robots: a review,” Sensor Review, vol. 43, no. 2, pp. 52–71, 2023.
  23. F. Khan, R. J. Roesthuis, and S. Misra, “Force sensing in continuum manipulators using fiber bragg grating sensors,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2017, pp. 2531–2536.
  24. Q. Qiao, G. Borghesan, J. De Schutter, and E. Vander Poorten, “Force from shape—estimating the location and magnitude of the external force on flexible instruments,” IEEE Transactions on Robotics, vol. 37, no. 5, pp. 1826–1833, 2021.
  25. N. J. Deaton, T. A. Brumfiel, A. Sarma, and J. P. Desai, “Simultaneous shape and tip force sensing for the coast guidewire robot,” IEEE Robotics and Automation Letters, 2023.
  26. O. Al-Ahmad, M. Ourak, J. Vlekken, and E. Vander Poorten, “Fbg-based estimation of external forces along flexible instrument bodies,” Frontiers in Robotics and AI, vol. 8, p. 718033, 2021.
  27. F. Alambeigi, S. A. Pedram, J. L. Speyer, J. Rosen, I. Iordachita, R. H. Taylor, and M. Armand, “Scade: Simultaneous sensor calibration and deformation estimation of fbg-equipped unmodeled continuum manipulators,” IEEE Transactions on Robotics, vol. 36, no. 1, 2019.
  28. X. Wang, G. Fang, K. Wang, X. Xie, K.-H. Lee, J. D. Ho, W. L. Tang, J. Lam, and K.-W. Kwok, “Eye-in-hand visual servoing enhanced with sparse strain measurement for soft continuum robots,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2161–2168, 2020.
  29. S. Sefati, C. Gao, I. Iordachita, R. H. Taylor, and M. Armand, “Data-driven shape sensing of a surgical continuum manipulator using an uncalibrated fiber bragg grating sensor,” IEEE Sensors Journal, 2020.
  30. X. T. Ha, D. Wu, M. Ourak, G. Borghesan, J. Dankelman, A. Menciassi, and E. Vander Poorten, “Shape sensing of flexible robots based on deep learning,” IEEE Transactions on Robotics, vol. 39, no. 2, pp. 1580–1593, 2022.
  31. X. T. Ha, D. Wu, C.-F. Lai, M. Ourak, G. Borghesan, A. Menciassi, and E. Vander Poorten, “Contact localization of continuum and flexible robot using data-driven approach,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6910–6917, 2022.
  32. D. Wu, Y. Zhang, M. Ourak, K. Niu, J. Dankelman, and E. Vander Poorten, “Hysteresis modeling of robotic catheters based on long short-term memory network for improved environment reconstruction,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2106–2113, 2021.
  33. J. Hao, D. Song, C. Hu, and C. Shi, “Two-dimensional shape and distal force estimation for the continuum robot based on learning from the proximal sensors,” IEEE Sensors Journal, 2023.
  34. X. Wang, J. Dai, H.-S. Tong, K. Wang, G. Fang, X. Xie, Y.-H. Liu, K. W. S. Au, and K.-W. Kwok, “Learning-based visual-strain fusion for eye-in-hand continuum robot pose estimation and control,” IEEE Transactions on Robotics, 2023.
  35. J. Hao, Z. Zhang, S. Wang, and C. Shi, “2d shape estimation of a pneumatic-driven soft finger with a large bending angle based on learning from two sensing modalities,” Advanced Intelligent Systems, p. 2200324, 2023.
  36. B. Li, B. Lu, Z. Wang, F. Zhong, Q. Dou, and Y.-H. Liu, “Learning laparoscope actions via video features for proactive robotic field-of-view control,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6653–6660, 2022.
  37. X. Wang, Y. Fang, N. Yu, and J. Han, “Barrier function-based adaptive control of twisted tendon-sheath actuated system with unknown rigid–flexible coupling for robotic ureteroscopy,” IEEE/ASME Transactions on Mechatronics, 2023.
  38. B. Li, Y. Lu, W. Chen, B. Lu, F. Zhong, Q. Dou, and Y.-H. Liu, “Gmm-based heuristic decision framework for safe automated laparoscope control,” IEEE Robotics and Automation Letters, 2024.

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