Dynamic tracking effect of a magnetic navigated dual hemisphere capsule robot

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.

Gait multi-objectives optimization of lower limb exoskeleton robot based on BSO-EOLLFF algorithm

Aiming at problems of low optimization accuracy and slow convergence speed in the gait optimization algorithm of lower limb exoskeleton robot, a novel gait multi-objectives optimization strategy based on beetle swarm optimization (BSO)-elite opposition-based learning (EOL) levy flight foraging (LFF) algorithm was proposed. In order to avoid the algorithm from falling into the local optimum, the EOL strategy with global search capability, the LFF strategy with local search capability and the dynamic mutation strategy with high population diversity were introduced to improve optimization performance. The optimization was performed by establishing a multi-objectives optimization function with the robot’s gait zero moment point (ZMP) stability margin and driving energy consumption. The joint comparative tests were carried out in SolidWorks, ADAMS and MATLAB software. The simulation results showed that compared with the particle swarm optimization algorithm and the BSO algorithm, the ZMP stability margin obtained by the BSO-EOLLFF algorithm was increased, and the average driving energy consumption was reduced by 25.82% and 17.26%, respectively. The human-machine experiments were conducted to verify the effectiveness and superiority. The robot could realize stable and smooth walking with less energy consumption. This research will provide support for the application of exoskeleton robot.