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

Posture detection is essential for capsule robots to be manipulated in a relatively closed gastrointestinal (GI) tract and to fulfill some medical operations. In this paper, a posture detection technique for a magnetic-actuated dual-hemisphere capsule robot (DHCR) is proposed. In this method, the DHCR realizes fixed-point posture adjustment based on tracking effects, and feature points are recognized and matched with the help of the ORB algorithm on the GI image acquired by a vision sensor. The system model is derived from the dynamic model and feature point information. Then, the posture is optimized by using the adaptive extended Kalman filter (AEKF) algorithm. As a result, the posture detection method based on the tracking effects and the ORB-AEKF algorithm is formed. The effectiveness and superiority of the proposed method are verified through experiments, which provide a good foundation for the subsequent, accurate closed-loop control of the DHCR.