VisionSafeEnhanced VPC: Cautious Predictive Control with Visibility Constraints under Uncertainty for Autonomous Robotic Surgery

2025-08-26

AI Summary

To address degraded surgical field visibility and safety risks in robot-assisted minimally invasive surgery—caused by parameter uncertainties, measurement noise, and load variations—this paper proposes a visibility-constrained cautious predictive control framework. Methodologically, it introduces the first integration of Gaussian process regression with uncertainty-adaptive control barrier functions to establish probabilistic visual safety guarantees; further, it employs hybrid uncertainty modeling and chance-constrained optimization to dynamically balance control robustness and motion smoothness. The approach unifies image-based visual servoing, model predictive control, and uncertainty propagation. Experimental validation on a commercial surgical robot platform demonstrates >99.9% target visibility rate, significantly reduced tracking error, and attenuated endoscopic jitter—thereby enhancing intraoperative visual stability and safety.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Autonomous control of the laparoscope in robot-assisted Minimally Invasive Surgery (MIS) has received considerable research interest due to its potential to improve surgical safety. Despite progress in pixel-level Image-Based Visual Servoing (IBVS) control, the requirement of continuous visibility and the existence of complex disturbances, such as parameterization error, measurement noise, and uncertainties of payloads, could degrade the surgeon's visual experience and compromise procedural safety. To address these limitations, this paper proposes VisionSafeEnhanced Visual Predictive Control (VPC), a robust and uncertainty-adaptive framework for autonomous laparoscope control that guarantees Field of View (FoV) safety under uncertainty. Firstly, Gaussian Process Regression (GPR) is utilized to perform hybrid (deterministic + stochastic) quantification of operational uncertainties including residual model uncertainties, stochastic uncertainties, and external disturbances. Based on uncertainty quantification, a novel safety aware trajectory optimization framework with probabilistic guarantees is proposed, where a uncertainty-adaptive safety Control Barrier Function (CBF) condition is given based on uncertainty propagation, and chance constraints are simultaneously formulated based on probabilistic approximation. This uncertainty aware formulation enables adaptive control effort allocation, minimizing unnecessary camera motion while maintaining robustness. The proposed method is validated through comparative simulations and experiments on a commercial surgical robot platform (MicroPort MedBot Toumai) performing a sequential multi-target lymph node dissection. Compared with baseline methods, the framework maintains near-perfect target visibility (>99.9%), reduces tracking e

Problem

• Ensures continuous visibility under uncertainty for autonomous surgery
• Addresses complex disturbances like noise and payload uncertainties
• Guarantees Field of View safety with adaptive control framework

Innovation

• Gaussian Process Regression for hybrid uncertainty quantification
• Uncertainty-adaptive safety Control Barrier Function condition
• Chance constraints based on probabilistic approximation