A digital twin framework for robust control of robotic-biological systems

Area of Science:

• Biomechanical Engineering
• Medical Device Technology
• Computational Modeling

Background:

• Medical device regulatory standards increasingly require computational modeling and simulation for advanced manufacturing and personalization.
• Robust testing methods are needed for engineered soft tissue products, especially with increasing device complexity.

Purpose of the Study:

• To develop and validate a digital twin framework for calibrating and controlling robotic-biological systems.
• To demonstrate the framework's accuracy in reproducing experimental data and controlling mechanical elements.
• To facilitate advanced medical device testing and biomechanics research.

Main Methods:

• Developed and validated a digital twin framework for robotic-biological systems.
• Created and calibrated a forward dynamics model for a robotic manipulator.
• Demonstrated displacement control using a spring and kinematic control of a digital knee twin.

Main Results:

• The digital twin framework demonstrated improved accuracy in reproducing experimental data post-calibration.
• Accurate displacement control was achieved for a spring element (0.09 mm RMSE).
• Accurate passive knee flexion kinematics were simulated in silico (2.00°, 0.57°, 1.75° RMSE for key rotations).

Conclusions:

• The validated digital twin framework enables accurate control of robotic-biological systems.
• This method can be applied to testing with poorly represented specimens, like human or animal tissues.
• The framework facilitates medical device testing and innovative biomechanics research, with potential for tracking internal parameters like tissue strain.