  Intraoperative ultrasound facilitates localisation of tumour boundaries
during minimally invasive procedures. Autonomous ultrasound scanning systems
have been recently proposed to improve scanning accuracy and reduce surgeons'
cognitive load. However, current methods mainly consider static scanning
environments typically with the probe pressing against the tissue surface. In
this work, a motion-compensated autonomous ultrasound scanning system using the
da Vinci Research Kit (dVRK) is proposed. An optimal scanning trajectory is
generated considering both the tissue surface shape and the ultrasound
transducer dimensions. A robust vision-based approach is proposed to learn the
underlying tissue motion characteristics. The learned motion model is then
incorporated into the visual servoing framework. The proposed system has been
validated with both phantom and ex vivo experiments using the ground truth
motion data for comparison.
