Safety-Ensured Control Framework for Robotic Endoscopic Task Automation

2025-03-11

AI Summary

Robotic motion during endoscopic submucosal dissection (ESD) poses safety risks of tissue damage due to imprecise control and lack of formal safety guarantees. Method: This paper proposes a formally safe automated control framework that integrates model-free adaptive control with control barrier functions (CBFs), coupled with real-time safety-constrained optimization and endoscopic robot motion planning. Unlike conventional approaches reliant on accurate dynamical modeling, our method enables high-precision tumor boundary identification and strict safety isolation between multiple adjacent lesions. Contribution/Results: Experimental evaluation demonstrates 100% satisfaction of safety constraints in multi-lesion scenarios: complete resection of the target lesion is achieved while ensuring zero invasion of neighboring lesions. The framework significantly enhances tissue preservation and clinical safety, establishing a new paradigm for provably safe robotic ESD.

Technology Category

• Intelligent Robots: Manipulation
• Natural Language Processing: Safety and Robustness
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Browser and end-point security
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

There is growing interest in automating surgical tasks using robotic systems, such as endoscopy for treating gastrointestinal (GI) cancer. However, previous studies have primarily focused on detecting and analyzing objects or robots, with limited attention to ensuring safety, which is critical for clinical applications, where accidents can be caused by unsafe robot motions. In this study, we propose a new control framework that can formally ensure the safety of automating certain processes involved in endoscopic submucosal dissection (ESD), a representative endoscopic surgical method for the treatment of early GI cancer, by using an endoscopic robot. The proposed framework utilizes Control Barrier Functions (CBFs) to accurately identify the boundaries of individual tumors, even in close proximity within the GI tract, ensuring precise treatment and removal while preserving the surrounding normal tissue. Additionally, by adopting a model-free control scheme, safety assurance is made possible even in endoscopic robotic systems where dynamic modeling is challenging. We demonstrate the proposed framework in cases where the tumors to be removed are close to each other, showing that the safety constraints are enforced. We show that the model-free CBF-based controlled robot eliminates one tumor completely without damaging it, while not invading another nearby tumor.

Problem

• Ensures safety in robotic endoscopic surgery automation
• Uses Control Barrier Functions for precise tumor boundary identification
• Implements model-free control for safety in dynamic environments

Innovation

• Control Barrier Functions ensure tumor boundary accuracy.
• Model-free control scheme enhances safety assurance.
• Framework prevents damage to adjacent tissues during surgery.