Efficient Steady-State Tissue Simulation for Surgical Robotics: A First-Order Position-Based Method
2025 International Symposium on Medical Robotics (ISMR)（2025）
Abstract
Soft tissue simulation can play an essential role in the automation of robotic surgery by providing contextual information during surgery and generating datasets for training. Any time tissue deformations are simulated, computational speed, accuracy, and stability are key concerns. State-of-theart tissue simulation resolves inertial dynamics solutions using position-based computational methods. However, existing methods fail to efficiently resolve steady-state solutions at surgical size scales because of transient inertial dynamics and the small time step required for stability at such size scales. We propose a position-based tissue simulation framework which is based on large-deformation Neo-Hookean elasticity and enables fast resolution to steady-state for efficient simulation. Our method replaces the inertial terms in the model with a virtual viscous damping term. This enables realistic tissue motion while eliminating the transient vibrations that require more computation. It also enables smooth and stable dynamic transitions between disparate static states. We detail the selection of parameters and step sizes for efficient steady-state simulation. We further compare our approach to a state-of-the-art position-based method and show significant improvements in stability and realtime performance at surgical size scales.
MoreTranslated text
Key words
Efficient Simulation,First-order Method,Steady-state Simulations,Steady-state Tissue,Position-based Methods,Time Step,Soft Tissue,Computational Speed,Size Scale,Steady-state Solution,Tissue Deformation,Transient Dynamics,Small Time Step,Inertial Term,Damping Term,Significant Improvement In Stability,Stiffness,Young’s Modulus,Small Scale,Large Time Step,Real-time Simulation,Surgical Simulation,Damping Matrix,Collision Detection,Equations Of Motion,Elastic Force,Newton’s Second Law,Vector Of Lagrange Multipliers,Second-order Method
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper