Visual Positioning of Nasal Swab Robot Based on Hierarchical Decision

Area of Science:

• Robotics
• Artificial Intelligence
• Medical Technology

Background:

• The COVID-19 pandemic highlighted the need for safe and efficient diagnostic sampling methods.
• Manual nasal swab collection poses infection risks to healthcare personnel.
• Automated solutions are required to mitigate large-scale public health impacts.

Purpose of the Study:

• To develop and validate a robot vision localization method for automatic nasal swab sampling.
• To enhance safety and stability in sample collection procedures.
• To provide technical support for managing future public health emergencies.

Main Methods:

• Utilized a hierarchical decision network to address infectious characteristics and robot behavior constraints.
• Implemented a visual navigation and positioning strategy for a single-arm robot.
• Developed an artificial intelligence-driven robot visual servo control system.
• Incorporated risk factor assessment for potential contact infection during sampling.

Main Results:

• The proposed method achieved effective vision positioning for robots during nasal swab sampling.
• Demonstrated stable and safe operation of the automated sampling system.
• The system successfully considered operational characteristics of medical staff.

Conclusions:

• The developed robot vision system offers a viable solution for automated nasal swab sampling.
• This technology can significantly reduce infection transmission risks in healthcare settings.
• The method provides crucial technical support for managing novel public health situations.