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Hands-Free Camera Assistant: Autonomous Laparoscope Manipulation in Robot-Assisted Surgery
Jinze Shi1,2, Chunlin Zhou1,3, Luming Wang4
1College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Summary
This study introduces a deep learning framework for robotic camera holders in laparoscopic surgery, enabling autonomous and efficient laparoscope control. The system ensures rapid, safe manipulation, improving surgical procedures and reducing specialist workload.
Area of Science:
- Robotics in Medicine
- Surgical Technology
- Artificial Intelligence in Healthcare
Background:
- Robotic camera holders enhance surgical efficiency and reduce specialist burden in laparoscopic procedures.
- Current systems face challenges in maintaining constraints and autonomous control.
Purpose of the Study:
- To develop a multi-task compliant control framework for robotic camera holders.
- To integrate deep learning with robot kinematics for improved surgical manipulation.
Main Methods:
- Proposed a multi-task compliant control framework using deep learning and robot kinematics.
- Addressed remote center of motion (RCM) constraint maintenance.
- Enabled autonomous field of view (FOV) adjustment.
Main Results:
- Framework achieved mean response time < 2s, max RCM error < 5mm, mean tracking error < 20 pixels, mean depth error < 2.5mm.
- Demonstrated successful integration of virtual fixtures to prevent tissue collisions.
- Framework showed high accuracy and responsiveness in trajectory following.
Conclusions:
- The developed framework enables autonomous, rapid, and safe laparoscope manipulation.
- Enhanced continuity and efficiency of surgical procedures.
- Conserves specialist healthcare resources through automation.
