Robotic systems have an increasingly important role in facilitating minimally invasive surgical treatments. In robot-assisted minimally invasive surgery, surgeons remotely control instruments from a console to perform operations inside the patient. However, despite the advanced technological status of surgical robots, fully autonomous systems, with decision-making capabilities, are not yet available.
In 2017, a structure to classify the research efforts toward autonomy achievable with surgical robots was proposed by Yang et al. Six different levels were identified: no autonomy, robot assistance, task autonomy,
conditional autonomy, high autonomy, and full autonomy. All the commercially available platforms in robot-assisted
surgery is still in level 0 (no autonomy). Despite increasing the level of autonomy remains an open challenge, its adoption could potentially introduce multiple benefits, such as decreasing surgeons’ workload and fatigue and pursuing a consistent
quality of procedures. Ultimately, allowing the surgeons to interpret the ample
and intelligent information from the system will enhance the surgical outcome and
positively reflect both on patients and society. Three main aspects are required to
introduce automation into surgery: the surgical robot must move with high precision,
have motion planning capabilities and understand the surgical scene. Besides
these main factors, depending on the type of surgery, there could be other aspects
that might play a fundamental role, to name some compliance, stiffness, etc. This
thesis addresses three technological challenges encountered when trying to achieve
the aforementioned goals, in the specific case of robot-object interaction. First,
how to overcome the inaccuracy of cable-driven systems when executing fine and
precise movements. Second, planning different tasks in dynamically changing environments.
Lastly, how the understanding of a surgical scene can be used to solve
more than one manipulation task.
To address the first challenge, a control scheme relying on accurate calibration is
implemented to execute the pick-up of a surgical needle. Regarding the planning of
surgical tasks, two approaches are explored: one is learning from demonstration to
pick and place a surgical object, and the second is using a gradient-based approach
to trigger a smoother object repositioning phase during intraoperative procedures.
Finally, to improve scene understanding, this thesis focuses on developing a simulation
environment where multiple tasks can be learned based on the surgical scene
and then transferred to the real robot. Experiments proved that automation of the pick and place task of different surgical objects is possible. The robot was successfully
able to autonomously pick up a suturing needle, position a surgical device for
intraoperative ultrasound scanning and manipulate soft tissue for intraoperative organ
retraction. Despite automation of surgical subtasks has been demonstrated in
this work, several challenges remain open, such as the capabilities of the generated
algorithm to generalise over different environment conditions and different patients
