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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Autonomous Closed-Loop Control for Robotic Soft Tissue Electrosurgery Using RGB-D Image Guidance.

Michael Kam1, Jiawei Ge1, Naveed D Riaziat1

  • 1Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21211 USA.

IEEE Transactions on Medical Robotics and Bionics
|January 30, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a closed-loop robotic electrosurgery system that uses 3D tissue tracking to improve oral cavity cancer surgery precision. The system significantly reduces cutting errors, enhancing resection accuracy and potentially lowering cancer recurrence rates.

Keywords:
Electrosurgerymedical roboticsmotion planning

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Area of Science:

  • Robotics
  • Surgical Technology
  • Artificial Intelligence

Background:

  • Oral cavity cancer treatment relies on precise tumor excision, often using electrosurgery.
  • Current autonomous robotic electrosurgery struggles with tracking soft tissue deformation, compromising resection accuracy.
  • Accurate resection margins are crucial for minimizing cancer recurrence.

Purpose of the Study:

  • To develop an autonomous closed-loop robotic electrosurgery system for enhanced surgical precision.
  • To address challenges in tracking tissue deformation during robotic electrosurgery.
  • To improve resection margin accuracy in oral cavity cancer surgery.

Main Methods:

  • Utilized a Red Green Blue - Depth (RGB-D) sensor for 3D tissue tracking.
  • Employed CoTracker, a deep learning model, for markerless tissue deformation tracking.
  • Integrated a fuzzy logic controller for dynamic adjustment of cutting velocity based on real-time feedback.

Main Results:

  • Demonstrated a 55% reduction in average cutting error (1.2 mm to 0.54 mm, p < 0.001) in closed-loop versus open-loop control.
  • Successfully tracked tissue deformation without prior tissue modeling using CoTracker and tool-occlusion algorithms.
  • Validated system efficacy on ex vivo porcine tongues.

Conclusions:

  • The developed closed-loop system significantly improves electrosurgical cutting precision through 3D tissue tracking and feedback control.
  • Image-based feedback control is effective in enhancing margin accuracy, a critical factor in reducing cancer recurrence.
  • This technology holds promise for advancing robotic surgery in head and neck oncology.