Robotic-assisted partial nephrectomy (RAPN) is a surgi-
cal procedure that employs robotics to remove a portion
of diseased kidney. During the procedure, a drop-in
Ultrasound (US) probe is used to identify the resec-
tion margins. Although the robot facilitates the task,
the scanning of the kidney proves challenging due to
slippage and requires a highly skilled surgeon. In this work, we investigate autonomous control
during the US scanning using the PAF rails, specifically
using fibre-optic shape-sensing data as the input for path-
planning. First, we present the design and fabrication of
the sensorized PAF rail; then we assess the performance
of real-time curvature sensing with the sensorized PAF
rail system on rigid and soft phantoms; finally, we
demonstrate how the PAF rail local shape data can be
used to plan a trajectory and autonomously guide an
intraoperative US probe. We autonomously guided an ultrasound probe during the
scan of a kidney phantom from the sensed curvature of
the PAF rails. Here, we present promising results but,
further study is needed to improve the US trajectory
accuracy as we made several assumptions. Namely,
curvature uniformity along the width of the rail, we
manually positioned the probe perpendicular to the rail
to reduce the problem from 3-D to 2-D planning, and
we did not account for the offset between the US probe
and the PSM1 tip. Overall, we demonstrate the applicability of shape sens-
ing in soft robotics to automate an intraoperative robotic
US scan. In a further study, we aim to perform multiple
US swipe sequences to enhance the US scan quality
and compare the robot performance against clinical
standards.