Autonomous Ultrasound Scanning Towards Standard Plane Using Interval Interaction Probabilistic Movement Primitives

Learning from demonstrations is the paradigm where robots acquire new skills demonstrated by an expert and alleviate the physical burden on experts to perform repetitive tasks. Ultrasound scanning is one of the ways to view the anatomical structures of soft tissues, but it is repetitive for some tissue scanning tasks. In this study, an autonomous ultrasound scanning towards a standard plane framework is proposed. Interaction probabilistic movement primitives (iProMP) was proposed for the collaborative tasks for human and robot movement. Inspired by the interval type-2 fuzzy system, an interval iProMP is proposed to learn the ultrasound scanning navigation strategy from scanning demonstrations and the collaborative agents are the robot movement and ultrasound image information. The proposed interval iProMP improves the capacity of dealing with uncertainties due to insufficient observations during reproduction. U-Net is applied to recognize the desired ultrasound image shown during demonstrations and a confidence map is used to evaluate the ultrasound image quality. Breast seroma scanning is chosen as the ultrasound scanning task to validate the performance of the proposed autonomous ultrasound scanning framework. Ultrasound navigation is to realize autonomous ultrasound scanning for localizing the breast seroma. The simulation comparison result shows the better performance of the proposed interval iProMP under insufficient observation, compared to traditional iProMP. The experiment result validates the feasibility and generality of the proposed autonomous ultrasound scanning framework using interval iProMP with a higher success rate than that with traditional iProMP.