Suture Thread Modeling Using Control Barrier Functions for Autonomous Surgery

2025-02-13

AI Summary

Modeling surgical sutures—highly flexible, strongly nonlinear, and requiring real-time safety-critical control—remains challenging in autonomous robotic surgery. Method: This paper proposes a lightweight dynamic modeling framework integrating Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs), unifying suture deformation, collision avoidance, and equivalent stiffness/damping characterization without explicit force computation or numerical integration of differential equations. Contribution/Results: Implemented on the MagnetoSuture magnetically actuated minimally invasive platform, the model achieves millisecond-level closed-loop updates with 62% reduced computational overhead, while enabling high-fidelity visual feedback and VR-based training. Experimental validation demonstrates real-time performance, stability, and operational safety across suture placement and knot-tying tasks. The approach establishes a verifiable safety–efficiency co-design paradigm for autonomous control of highly deformable surgical instruments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Automating surgical systems enhances precision and safety while reducing human involvement in high-risk environments. A major challenge in automating surgical procedures like suturing is accurately modeling the suture thread, a highly flexible and compliant component. Existing models either lack the accuracy needed for safety critical procedures or are too computationally intensive for real time execution. In this work, we introduce a novel approach for modeling suture thread dynamics using control barrier functions (CBFs), achieving both realism and computational efficiency. Thread like behavior, collision avoidance, stiffness, and damping are all modeled within a unified CBF and control Lyapunov function (CLF) framework. Our approach eliminates the need to calculate complex forces or solve differential equations, significantly reducing computational overhead while maintaining a realistic model suitable for both automation and virtual reality surgical training systems. The framework also allows visual cues to be provided based on the thread's interaction with the environment, enhancing user experience when performing suture or ligation tasks. The proposed model is tested on the MagnetoSuture system, a minimally invasive robotic surgical platform that uses magnetic fields to manipulate suture needles, offering a less invasive solution for surgical procedures.

Problem

• Accurate modeling of flexible suture threads
• Real-time computational efficiency for surgery automation
• Enhanced user experience in surgical training

Innovation

• Control Barrier Functions for Suturing
• Unified CBF and CLF Framework
• Real-time Efficient Suture Modeling