Abstract
The advent of transesophageal ultrasound robots has provided a new idea to simplify relevant clinical procedures. However, the existing add-on robots often lack the ability to predict the contact force between the probe tip and the tissue. This makes the control of this robot under teleoperation lacking in tactile feedback and difficult to obtain effective safety. In this study, we propose a neural network-based internal resistance modeling method. Based on this, we experimentally calibrated the relationship between the tip contact force and handwheel torque through a self-learning idea. The experimental results show that a microcontroller-deployable lightweight neural network can achieve a good result on the fitting of the internal resistance, with its standard deviation being less than 3%. Moreover, a good linear correlation between the tip contact force and the handwheel torque was demonstrated in the case of passively applied forces. Independent experiments with actively applied forces further demonstrated the feasibility of the prediction method, especially in the forward bending process, with the prediction error mostly within 20% of the baseline force. Therefore, we believe that the proposed method has good potential to improve the safe use of transesophageal ultrasound robots.
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References
Wang, S., Housden, J., Singh, D., Althoefer, K., Rhode, K.: Design, testing and modelling of a novel robotic system for trans-oesophageal ultrasound. Int. J. Med. Robot. Comput. Assist. Surg. 12(3), 342–354 (2016)
Wang, S., Housden, J., Zar, A., Gandecha, R., Singh, D., Rhode, K.: Strategy for monitoring cardiac interventions with an intelligent robotic ultrasound device. Micromachines 9(2), 65 (2018)
Wang, S., et al.: Robotic intra-operative ultrasound: virtual environments and parallel systems. IEEE/CAA J. Automatica Sinica 8(5), 1095–1106 (2021)
Pahl, C., Ebelt, H., Sayahkarajy, M., Supriyanto, E., Soesanto, A.: Towards robot-assisted echocardiographic monitoring in catheterization laboratories usability-centered manipulator for transesophageal echocardiography. J. Med. Syst. 41(10), 148 (2017)
Sajadi, S.M., Mathiassen, K., Brun, H., Elle, O.J.: Design, kinematic modeling, and validation of a robotic-assisted transesophageal echocardiography system. In: 2022 IEEE/SICE International Symposium on System Integration, pp. 250–257 (2022)
Loschak, P.M., Brattain, L.J., Howe, R.D.: Automated pointing of cardiac imaging catheters. In: 2013 IEEE International Conference on Robotics and Automation, 5794–5799 (2013)
Loschak, P.M., Degirmenci, A., Tenzer, Y., Howe, R.D.: A 4-DOF robot for positioning ultrasound imaging catheters. In: Proceedings of the ASME Design Engineering Technical Conferences: 5A, V05AT08A046 (2015)
Dehghani, M., Moosavian, S.A.A.: Static modeling of continuum robots by circular elements. In: 2013 21st Iranian Conference on Electrical Engineering, pp. 1–6 (2013)
Rucker, D.C., Webster, R.J.: Statics and dynamics of continuum robots with general tendon routing and external loading. IEEE Trans. Robot. 27(6), 1033–1044 (2011)
Ashwin, K.P., Mahapatra, S.K., Ghosal, A.: Profile and contact force estimation of cable-driven continuum robots in presence of obstacles. Mech. Mach. Theory 164, 104404 (2021)
Feng, F., Hong, W., **e, L.: A learning-based tip contact force estimation method for tendon-driven continuum manipulator. Sci. Rep. 11(1) (2021)
Shu, X., Hua, P., Wang, S., Zhang, L., **e, L.: Safety enhanced surgical robot for flexible ureteroscopy based on force feedback. Int. J. Med. Robot. Comput. Assist. Surg. (2022)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 62003339 and in part by the InnoHK program.
