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Real-Time Soft Tissue Deformation Framework for Haptic-Enabled Robotic Surgical Training in Virtual Reality
Dhanya Menoth Mohan1, Bijan Shirinzadeh2, Yongmin Zhong3
1Robotics and Mechatronics Research Laboratory, Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Melbourne, Victoria, 3800, Australia. dhanya.menothmohan@monash.edu.
Annals of Biomedical Engineering
|July 8, 2026
Summary
This study introduces a new mass-spring-damper model for realistic soft tissue deformation in virtual reality surgical training. The framework achieves real-time performance on complex models, enhancing training simulations.
Area of Science:
- Robotics
- Computer Simulation
- Medical Training
Background:
- Virtual reality (VR) offers advantages for robotic surgery training, including enhanced safety and reduced costs.
- Realistic visual feedback in VR requires accurate soft tissue deformation models.
- Current models often struggle with balancing realism and real-time performance for complex simulations.
Purpose of the Study:
- To develop a modified mass-spring-damper framework for stable, realistic soft tissue deformation.
- To ensure real-time performance even with high-density mesh models in VR surgical training.
- To enhance the visual realism and user immersiveness of virtual reality-based robotic surgery training platforms.
Main Methods:
- A modified mass-spring-damper framework was developed, extending the conventional model with deformation and restoring spring-damper elements.
- Model parameters were optimized using analytical derivation and empirical tuning.
- Numerical simulations assessed restoring capability, numerical stability, and real-time performance.
Main Results:
- The model demonstrated physiologically realistic deformation and shape recovery after force removal.
- Real-time performance was achieved on high-density models (approx. 29,754 vertices) at 171.11 frames per second.
- The deformation solver maintained a high update frequency (2828.14 Hz) with a mean step time of 0.354 ms.
Conclusions:
- The proposed framework provides real-time performance on complex models, suitable for VR surgical training.
- The computational efficiency and visual realism meet the demands of haptic-enabled robotic surgical environments.
- This advancement contributes to more effective and immersive surgical training simulations.
