基于集合贝叶斯交互基元的机械臂自主肝脏超声扫查OA

To advance liver ultrasound examination,this study proposes a fully autonomous robotic arm ultra-sound scanning method based on ensemble Bayesian interaction primitives(enBIP),and develops the correspond-ing experimental system.The proposed method consists of two sequential stages:Initial positioning and imitation learning.In the initial positioning stage,the system guides the probe to establish contact with the patient using RGB-D images and determines the transition to the imitation learning stage based on real-time ultrasound images.In the imitation learning stage,the system encodes expert scanning skills into ultrasound image state trajectories and probe motion trajectories,and learns to reproduce these skills using enBIP.Consequently,fully autonomous ro-botic arm liver ultrasound scanning is achieved.In addition,the proposed framework was experimentally validated on a human abdominal phantom.Experimental results demonstrate that the proposed method successfully per-forms the liver scanning task without human intervention,highlighting its potential for clinical application.