首个可安全剥离创口敷料的机器人系统，减少患者痛苦与医疗风险。
AutoPeel: Adhesion-aware Safe Peeling Trajectory Optimization for Robotic Wound Care
- 基于可微物理仿真与模型预测控制优化剥离路径
- 在仿生皮肤与真人实验中实现精准且低损伤剥离
- 适合慢性伤口护理机器人研发与医疗自动化领域
慢性伤口（包括糖尿病溃疡、压疮及静脉高压相关溃疡）在美国影响超过650万患者，年治疗成本超250亿美元。当前治疗仍为人工操作，我们设想未来通过机器人与自动化技术降低费用并提升照护质量。本文提出首个用于创口敷料移除的机器人系统，该过程被广泛认为是慢性伤口患者最痛苦的环节。方法结合可微物理仿真与梯度优化的模型预测控制（MPC），引入断裂力学建模敷料粘附的剥离行为。通过精心设计的目标函数，兼顾效率与安全性，有效降低组织损伤风险。在合成皮肤假体和真实人体受试者上开展多组实验验证，结果表明系统能生成精确、安全的敷料剥离轨迹，为自动化医疗操作提供可行方案。
原文摘要 · Abstract (English)
Chronic wounds, including diabetic ulcers, pressure ulcers, and ulcers secondary to venous hypertension, affects more than 6.5 million patients and a yearly cost of more than $25 billion in the United States alone. Chronic wound treatment is currently a manual process, and we envision a future where robotics and automation will aid in this treatment to reduce cost and improve patient care. In this work, we present the development of the first robotic system for wound dressing removal which is reported to be the worst aspect of living with chronic wounds. Our method leverages differentiable physics-based simulation to perform gradient-based Model Predictive Control (MPC) for optimized trajectory planning. By integrating fracture mechanics of adhesion, we are able to model the peeling effect inherent to dressing adhesion. The system is further guided by carefully designed objective functions that promote both efficient and safe control, reducing the risk of tissue damage. We validated the efficacy of our approach through a series of experiments conducted on both synthetic skin phantoms and real human subjects. Our results demonstrate the system's ability to achieve precise and safe dressing removal trajectories, offering a promising solution for automating this essential healthcare procedure.
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