提出视觉安全增强的预测控制框架，确保手术机器人摄像头在不确定性下始终可见目标。
VisionSafeEnhanced VPC: Cautious Predictive Control with Visibility Constraints under Uncertainty for Autonomous Robotic Surgery
- 用高斯过程量化模型误差与外部干扰等不确定性
- 实现99.9%以上目标可见率，减少冗余相机运动
- 适合需要高安全性与鲁棒性的自主手术系统研究者
机器人辅助微创手术中，自主操控腹腔镜受到广泛关注，因其有望提升手术安全。尽管像素级基于图像的视觉伺服（IBVS）取得进展，但持续可见性要求及复杂扰动（如参数误差、测量噪声、负载不确定性）仍会降低术者视觉体验并危及操作安全。为此，本文提出视觉安全增强的视觉预测控制（VisionSafeEnhanced VPC）框架，可在不确定性下保障视野（FoV）安全。首先，利用高斯过程回归（GPR）对残余模型不确定性、随机不确定性及外部扰动进行混合（确定性+随机）量化。基于此，设计一种带概率保证的安全轨迹优化框架，结合不确定性传播构建自适应安全控制屏障函数（CBF）条件，并以概率近似形式制定机会约束。该框架实现自适应控制努力分配，在保持鲁棒性的同时最小化不必要的相机运动。方法在商用手术机器人平台（MicroPort MedBot Toumai）上通过对比仿真与实验验证，完成连续多目标淋巴结清扫任务。相较于基线方法，本框架维持超过99.9%的目标可见率，跟踪误差显著降低，且大幅减少相机无效移动。
原文摘要 · Abstract (English)
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
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