The field of surgery is continually evolving as there is always room for improvement in the post-operative health of the patient as well as the comfort of the Operating Room (OR) team. While the success of surgery is contingent upon the skills of the surgeon and the OR team, the use of specialized robots has shown to improve surgery-related outcomes in some cases. These outcomes are currently measured using a wide variety of metrics that include patient pain and recovery, surgeon’s comfort, duration of the operation and the cost of the procedure. There is a need for additional research to better understand the optimal criteria for benchmarking surgical performance. Presently, surgeons are trained to perform robot-assisted surgeries using interactive simulators. However, in the absence of well-defined performance standards, these simulators focus primarily on the simulation of the operative scene and not the complexities associated with multiple inputs to a real-world surgical procedure. Because interactive simulators are typically designed for specific robots that perform a small number of tasks controlled by a single user, they are inflexible in terms of their portability to different robots and the inclusion of multiple operators (e.g., nurses, medical assistants). Additionally, while most simulators provide high-quality visuals, simplification techniques are often employed to avoid stability issues for physics computation, contact dynamics and multi-manual interaction. This study addresses the limitations of existing simulators by outlining various specifications required to develop techniques that mimic real-world interactions and collaboration. Moreover, this study focuses on the inclusion of distributed control, shared task allocation and assistive feedback -- through machine learning, secondary and tertiary operators -- alongside the primary human operator.

The field of surgery is continually evolving as there is always room for improvement in the post-operative health of the patient as well as the comfort of the Operating Room (OR) team. While the success of surgery is contingent upon the skills of the surgeon and the OR team, the use of specialized robots has shown to improve surgery-related outcomes in some cases. These outcomes are currently measured using a wide variety of metrics that include patient pain and recovery, surgeon’s comfort, duration of the operation and the cost of the procedure. There is a need for additional research to better understand the optimal criteria for benchmarking surgical performance. Presently, surgeons are trained to perform robot-assisted surgeries using interactive simulators. However, in the absence of well-defined performance standards, these simulators focus primarily on the simulation of the operative scene and not the complexities associated with multiple inputs to a real-world surgical procedure. Because interactive simulators are typically designed for specific robots that perform a small number of tasks controlled by a single user, they are inflexible in terms of their portability to different robots and the inclusion of multiple operators (e.g., nurses, medical assistants). Additionally, while most simulators provide high-quality visuals, simplification techniques are often employed to avoid stability issues for physics computation, contact dynamics and multi-manual interaction. This study addresses the limitations of existing simulators by outlining various specifications required to develop techniques that mimic real-world interactions and collaboration. Moreover, this study focuses on the inclusion of distributed control, shared task allocation and assistive feedback -- through machine learning, secondary and tertiary operators -- alongside the primary human operator.

Today, soft body simulations are essential for a wide range of applications. They are for instance used for medical training in virtual reality and in video games to simulate clothes and hair. These kinds of interactive applications rely on real-time simulations, which entails very strict requirements. The simulation has to be fast enough and must never break, regardless of what deformation might occur. Two methods that perform well with regard to these requirements are the position-based dynamics (PBD) method and the shape matching method. Even though these methods have been used for years, it is still unclear when you should use either method. This thesis has compared the two methods with regard to the mentioned requirements. More specifically, the thesis has evaluated the performance of the simulation loop as well as the simulated objects’ ability to restore their shape after deformation. The performance results clearly show that the PBD method is the fastest. But the results of the simulated objects’ ability to restore their shape were not as conclusive. Overall, the PBD method seemed to perform the best again, but there were cases the method could not handle. Although the shape matching method performed slightly worse, it did manage to restore the shape of every deformed object. In conclusion, for most applications, the PBD method is likely the better option, but if the application relies on the fact that simulated objects can restore their shape, then the shape matching method may be preferable. / Idag är simulering av mjuka kroppar viktiga för en mängd olika tillämpningar. De används exempelvis för medicinsk träning i virtuell verklighet och i datorspel för att simulera kläder och hår. Dessa typer av interaktiva applikationer förlitar sig på realtidssimuleringar, vilket medför många stränga krav. Simuleringen måste vara tillräckligt snabb och får aldrig gå sönder, oavsett vad för slags deformation som kan uppstå. Två metoder som presterar bra med avseende på dessa krav är position-based dynamics (PBD) och shape matching. Trots att dessa metoder har använts i många år, så är det fortfarande oklart när vilken metod är mest lämplig. Denna avhandling har jämfört de två metoderna med hänsyn till de nämnda kraven. Mer specifikt har avhandlingen utvärderat metodernas prestanda samt de simulerade objektens förmåga att återställa sin form efter deformation. Resultaten för prestanda visar tydligt att PBD-metoden är snabbast. Men resultaten av de simulerade objektens förmåga att återställa sin form var inte lika enhälliga. Sammantaget verkade PBD-metoden prestera bäst igen, däremot fanns det fall som metoden inte kunde hantera. Fastän shape matching metoden presterade något sämre, så lyckades den återställa formen för varje deformerat objekt. Sammanfattningsvis, för de flesta applikationer är PBD-metoden troligen det bättre alternativet, men om applikationen förlitar sig på att de simulerade objekten kan återställa sina former, så kan shape matching metoden vara att föredra.