Articles | Volume 13, issue 1
Mech. Sci., 13, 593–602, 2022
Mech. Sci., 13, 593–602, 2022
Research article
28 Jun 2022
Research article | 28 Jun 2022

Automatic adjustment of laparoscopic pose using deep reinforcement learning

Lingtao Yu et al.

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Cited articles

Ali, S. M., Reisner, L. A., King, B., Cao, A., Auner, G., Klein, M., and Pandya, A. K.: Eye Gaze Tracking for Endoscopic Camera Positioning: An Application of a Hardware/Software Interface Developed to Automate Aesop, in: Studies in Health Technology and Informatics, 16th Conference on Medicine Meets Virtual Reality, Long Beach, United States, 30–1 January 2008, 4–7, (last access: 8 March 2021), 2008. 
Bohez, S., Verbelen, T., De Coninck, E., Vankeirsbilck, B., Simoens, P., and Dhoedt, B.: Sensor Fusion for Robot Control through Deep Reinforcement Learning, in: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vancouver, Canada, 24–28 September 2017, 2365–2370,, 2017. 
Cao, Y., Miura, S., Kobayashi, Y., Kawamura, K., Sugano, S., and Fujie, M. G.: Pupil Variation Applied to the Eye Tracking Control of an Endoscopic Manipulator, IEEE Robotics and Automation Letters, 1, 531–538,, 2016. 
Chang, J. and Rattner, D. W.: History of Minimally Invasive Surgical Oncology, Surg. Oncol. Clin. N. Am., 28, 1–9,, 2019. 
Comaniciu, D. and Meer, P.: Mean shift: A robust approach toward feature space analysis, IEEE T. Pattern Anal., 24, 603–619,, 2002. 
Short summary
We designed a motion controller through deep reinforcement learning. The laparoscopic visual field image is input to the controller, and the controller outputs the joint motion of the laparoscopic arm to adjust the visual field. During the movement of the surgical instrument in different trajectories, the controller keeps the surgical instrument in an appropriate position in the visual field. It proves that the method is widely effective and can meet the needs of doctors during surgery.