Open-Source Multi-Viewpoint Surgical Telerobotics

• URL: http://arxiv.org/abs/2505.11142v1
• Date: Fri, 16 May 2025 11:41:27 GMT
• Title: Open-Source Multi-Viewpoint Surgical Telerobotics
• Authors: Guido Caccianiga, Yarden Sharon, Bernard Javot, Senya Polikovsky, Gökce Ergün, Ivan Capobianco, André L. Mihaljevic, Anton Deguet, Katherine J. Kuchenbecker,
• Abstract summary: We conjecture that introducing one or more adjustable viewpoints in the abdominal cavity would unlock novel visualization and collaboration strategies for surgeons.<n>We are building a synchronized multi-viewpoint, multi-sensor robotic surgery system by integrating high-performance vision components and upgrading the da Vinci Research Kit control logic.
• Score: 6.763309989418208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As robots for minimally invasive surgery (MIS) gradually become more accessible and modular, we believe there is a great opportunity to rethink and expand the visualization and control paradigms that have characterized surgical teleoperation since its inception. We conjecture that introducing one or more additional adjustable viewpoints in the abdominal cavity would not only unlock novel visualization and collaboration strategies for surgeons but also substantially boost the robustness of machine perception toward shared autonomy. Immediate advantages include controlling a second viewpoint and teleoperating surgical tools from a different perspective, which would allow collaborating surgeons to adjust their views independently and still maneuver their robotic instruments intuitively. Furthermore, we believe that capturing synchronized multi-view 3D measurements of the patient's anatomy would unlock advanced scene representations. Accurate real-time intraoperative 3D perception will allow algorithmic assistants to directly control one or more robotic instruments and/or robotic cameras. Toward these goals, we are building a synchronized multi-viewpoint, multi-sensor robotic surgery system by integrating high-performance vision components and upgrading the da Vinci Research Kit control logic. This short paper reports a functional summary of our setup and elaborates on its potential impacts in research and future clinical practice. By fully open-sourcing our system, we will enable the research community to reproduce our setup, improve it, and develop powerful algorithms, effectively boosting clinical translation of cutting-edge research.

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