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.

This work was supported in part by the National Natural Science Foundation of China under Grant 62003339 and in part by the InnoHK program.

Authors and Affiliations

1. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

   Yiping Xie, Hongbin Liu, Zeng-Guang Hou & Shuangyi Wang

2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

   Yiping Xie, Hongbin Liu, Zeng-Guang Hou & Shuangyi Wang

3. Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Pak Shek Kok, China

   Xilong Hou, Hongbin Liu & Shuangyi Wang

4. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

   James Housden & Kawal Rhode

Corresponding author

Correspondence toShuangyi Wang.

Editors and Affiliations

1. Kitware Inc., Carrboro, NC, USA

   Stephen Aylward

2. University of Oxford, Oxford, UK

   J. Alison Noble

3. University College London, London, UK

   Yipeng Hu

4. University College London, London, UK

   Su-Lin Lee

5. University College London, London, UK

   Zachary Baum

6. University College London, London, UK

   Zhe Min

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