Robots have become the top-choice technology for Minimally Invasive Surgery (MIS) procedures
and due to the rapid advancement of these robotic platforms, it is expected that these robots
will play an essential role in the future of surgery. These surgical robots are expected to
evolve through higher levels of automation, as these platforms become more sophisticated and
capable. One important feature to be added to these robots is the ability to scan tissue for in
situ characterization to guide tumour resection. The main advantage of doing tissue scanning
autonomously is that it reduces surgical workload, allowing the surgeon to focus on more
crucial tasks while the robot scans the tissue automatically. In this thesis, an autonomous
tissue scanning framework is presented which allows the robot to capture ultrasound images
of the target scanning region. Throughout this thesis, different works contribute to my tissue
scanning framework. I have written the first technical review of the da Vinci (dVRK) surgical
robot, which describes the essential calibrations required to acquire good-quality data; I created
simulations of robotic surgeries to test algorithms before deploying them on the real robot; I
designed a cylindrical marker to estimate the pose of surgical instruments. I have found that
this cylindrical marker is a practical tool for the hand-eye calibration of the robot; I have created
an autonomous scanning framework which improves the previous works by being able to follow
moving tissue without assuming periodic breathing motions; Finally, I organized a soft-tissue
tracking challenge, which allows researchers to develop tissue trackers using the benchmarking
tool and the dataset that I have created.