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Autonomous RCM-less endoscope control: integrating force-based pivoting with deep learning visual servoing
Carlos Fontúrbel1, Ana Cisnal2, Diego Benavides-Cobos1
1Institute of Advanced Production Technologies (ITAP), School of Industrial Engineering, University of Valladolid, Paseo Prado de la Magdalena 3-5, 47011, Valladolid, Spain.
Scientific Reports
|May 22, 2026
Summary
This study introduces an autonomous robotic camera guidance system that uses deep learning and force feedback to track surgical instruments. This novel approach significantly reduces unwanted interaction forces, enhancing safety and adaptability in robotic surgery.
Area of Science:
- Robotics
- Surgical Technology
- Artificial Intelligence
Background:
- Robotic endoscope holders enhance visual stability during surgery.
- Fixed remote center of motion (RCM) can cause unwanted lateral forces due to fulcrum drift.
Purpose of the Study:
- To develop an autonomous guidance framework for robotic camera assistance.
- To minimize interaction forces while tracking surgical instruments.
- To eliminate the need for a fixed geometric RCM.
Main Methods:
- A UR3e manipulator controlled a laparoscope in a pelvitrainer.
- Deep learning (YOLOv11n) was used for instrument localization.
- A combination of visual servoing and force-based pivoting was implemented.
Main Results:
- The YOLOv11n model achieved high precision (0.91-0.93) and mAP (0.83-0.90).
- Visual servoing successfully re-centered targets in 98.8% of transitions.
- Force-based pivoting reduced median interaction forces from 2.35 N to 0.19 N compared to fixed-RCM.
Conclusions:
- Integrating CNN-driven servoing with force-based pivoting provides stable visual guidance.
- This framework substantially reduces interaction forces without a fixed RCM.
- The system offers a safer and more adaptive alternative for robotic camera assistance.
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