Robot Learning System Based on Target Localization and Human Demonstration for Medical Examination

Robot-assisted ultrasound scanning has been proven to be an effective solution for medical examination; however, most of the existing work is fully teleoperation or semiautonomous due to dynamic tasks and uncertain environments. In this article, we propose a novel robot learning system for autonomous ultrasound scanning that integrates dynamic target localization with motion generation based on learning from demonstration (LfD). First, a new LfD model, Gaussian process dynamic movement primitives (GPDMP), is introduced to encode human motion skills and generate motion trajectories online. The proposed GPDMP framework supports multiple demonstrations and guarantees convergence toward the target even in the presence of external disturbances, such as subject movement during ultrasound scanning. To further enhance generalization to moving subjects through robust target localization, a vision-based localization method is developed to identify and localize the neck region. Neck positioning across individuals with different body shapes is addressed by an improved AlphaPose network, while interference from other moving subjects in the scene is mitigated using an enhanced DeepSORT-based tracking algorithm. The proposed system is capable of automatically identifying and localizing the human neck, generating approach trajectories for the ultrasound probe, and achieving real-time interaction. A robot-assisted neck ultrasound scanning system is implemented to experimentally demonstrate the effectiveness of the proposed framework.