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Related Experiment Video

Updated: Jan 9, 2026

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Stiffness-observation-based force feedforward compensation control for interactive robot-assisted surgical bone

Hao Ren1, Zhichao Li1, Zhaowei Liang1

  • 1Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.

Medical Engineering & Physics
|December 6, 2025
PubMed
Summary

This study introduces a novel controller for robot-assisted surgery that estimates tissue stiffness in real-time. This improves force control and safety during complex procedures like craniotomy skull milling.

Keywords:
Bone drillingCutting force modelingDrilling force predictionRobot-assisted surgery

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Surgical Technology

Background:

  • Robot-assisted surgery faces challenges in force control, especially during bone milling in craniotomies.
  • Existing compliance control struggles with varying tissue stiffness and abrupt boundary transitions.
  • Human-robot interaction complexities arise from unintuitive operator inputs.

Purpose of the Study:

  • To develop a stiffness-observation-based controller for enhanced force control in robot-assisted surgery.
  • To improve safety and adaptability during complex surgical milling tasks.
  • To enable shared autonomy through intuitive human-robot collaboration.

Main Methods:

  • A novel controller monitors the force-feedrate differential to estimate real-time tissue stiffness.
  • The controller is integrated into an active-constrained framework, replacing traditional compliance control.
  • A hierarchical force control architecture is established for autonomous safety steering and shared autonomy.

Main Results:

  • The controller effectively estimates tissue stiffness and discriminates between tissue types.
  • Numerical simulations and in vivo experiments demonstrated effective force tracking.
  • The system ensured safety during complex milling tasks at bone-tissue boundaries.

Conclusions:

  • Stiffness observation-based force feedforward compensation enhances surgical safety in robot-assisted bone milling.
  • The controller improves adaptability to diverse tissue properties and boundary transitions.
  • This approach facilitates intuitive human-robot collaboration and shared autonomy in surgery.