Posture Detection of Dual-Hemisphere Capsule Robot Based on Magnetic Tracking Effects and ORB-AEKF Algorithm

Area of Science:

• Robotics
• Medical Imaging
• Control Systems

Background:

• Accurate posture detection is vital for capsule robots operating within the gastrointestinal (GI) tract.
• Current methods may lack the precision required for complex medical interventions.

Purpose of the Study:

• To propose and validate a novel posture detection technique for magnetic-actuated dual-hemisphere capsule robots (DHCR).
• To enhance the maneuverability and operational capabilities of capsule robots in closed environments.

Main Methods:

• Utilizing the ORB (Oriented FAST and Rotated BRIEF) algorithm for feature point recognition and matching from GI images.
• Developing a system model based on dynamic principles and feature point data.
• Employing an Adaptive Extended Kalman Filter (AEKF) algorithm for posture optimization.

Main Results:

• A robust posture detection method combining tracking effects with the ORB-AEKF algorithm was established.
• Experimental validation demonstrated the effectiveness and superiority of the proposed technique.
• The method achieved accurate fixed-point posture adjustment for the DHCR.

Conclusions:

• The developed ORB-AEKF based posture detection method provides a reliable foundation for precise closed-loop control of DHCRs.
• This advancement is critical for enabling sophisticated medical operations using capsule endoscopy.
• The technique shows significant promise for improving the safety and efficacy of internal medical procedures.