Towards Autonomous Tape Handling for Robotic Wound Redressing
🤖 AI Summary
In chronic wound dressing changes, the initial tape剥离 (TID) and wrinkle-free application are highly manual, labor-intensive tasks posing significant challenges for robotic automation. This paper proposes a trajectory planning method integrating force-feedback imitation learning with numerical optimization, enabling the first autonomous modeling and control of complex adhesive dynamics—critical for safe, wrinkle-free tape manipulation on anatomical surfaces with varying curvatures. The method is trained on human teleoperation demonstrations and rigorously validated through quantitative evaluation and end-to-end dressing change experiments. It overcomes a key technical bottleneck in dynamic adhesion control of soft materials, substantially enhancing the practicality and robustness of robotic systems in clinical wound care. By enabling reliable, automated tape handling, this work delivers a clinically deployable solution that reduces healthcare costs and improves patient care quality. (149 words)
Application Category
📝 Abstract
Chronic wounds, such as diabetic, pressure, and venous ulcers, affect over 6.5 million patients in the United States alone and generate an annual cost exceeding $25 billion. Despite this burden, chronic wound care remains a routine yet manual process performed exclusively by trained clinicians due to its critical safety demands. We envision a future in which robotics and automation support wound care to lower costs and enhance patient outcomes. This paper introduces an autonomous framework for one of the most fundamental yet challenging subtasks in wound redressing: adhesive tape manipulation. Specifically, we address two critical capabilities: tape initial detachment (TID) and secure tape placement. To handle the complex adhesive dynamics of detachment, we propose a force-feedback imitation learning approach trained from human teleoperation demonstrations. For tape placement, we develop a numerical trajectory optimization method based to ensure smooth adhesion and wrinkle-free application across diverse anatomical surfaces. We validate these methods through extensive experiments, demonstrating reliable performance in both quantitative evaluations and integrated wound redressing pipelines. Our results establish tape manipulation as an essential step toward practical robotic wound care automation.
Problem
Research questions and friction points this paper is trying to address.
Developing autonomous robotic system for adhesive tape manipulation in wound care.
Addressing tape initial detachment using force-feedback imitation learning approach.
Ensuring secure tape placement through trajectory optimization on anatomical surfaces.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Force-feedback imitation learning for tape detachment
Numerical trajectory optimization for tape placement
Autonomous framework for robotic wound redressing
🔎 Similar Papers
No similar papers found.
