  Assistive robots for healthcare have seen a growing demand due to the great
potential of relieving medical practitioners from routine jobs. In this paper,
we investigate the development of an optimization-based control framework for
an ultrasound-guided assistive robot to perform scoliosis assessment. A
conventional procedure for scoliosis assessment with ultrasound imaging
typically requires a medical practitioner to slide an ultrasound probe along a
patient's back. To automate this type of procedure, we need to consider
multiple objectives, such as contact force, position, orientation, energy,
posture, etc. To address the aforementioned components, we propose to formulate
the control framework design as a quadratic programming problem with each
objective weighed by its task priority subject to a set of equality and
inequality constraints. In addition, as the robot needs to establish constant
contact with the patient during spine scanning, we incorporate variable
impedance regulation of the end-effector position and orientation in the
control architecture to enhance safety and stability during the physical
human-robot interaction. Wherein, the variable impedance gains are retrieved by
learning from the medical expert's demonstrations. The proposed methodology is
evaluated by conducting real-world experiments of autonomous scoliosis
assessment with a robot manipulator xArm. The effectiveness is verified by the
obtained coronal spinal images of both a phantom and a human subject.
