Experimental Trials with a Shared Autonomy Controller Framework and the da Vinci Research Kit: Pattern Cutting Tasks using Thin Elastic Materials

A technical challenge in robotic soft material cutting is to avoid large local deformations that result in inaccuracies or failure of the task. Additionally, reducing procedure time and human errors that occur due to fatigue and monotony are two of the most anticipated advantages of using robots in execution of repetitive subtasks for minimally invasive surgery. In our paper, we evaluate pattern cutting tasks of 2D elastic materials with shared control using the da Vinci Research Kit (dVRK). For this purpose, we developed a shared autonomy motion generator framework for pattern cutting. The framework registers user-defined Cartesian positions, creates smooth splines, interpolates the Cartesian positions, and generates a trajectory with Cartesian and joint constraints. While a pre-planned trajectory is being executed, the user may provide Cartesian offsets to modify the trajectory. We repeatedly cut shapes on 3 materials with different elasticity. Our shared control method achieved 100% success rate while performing a circular cutting task in a sheet of gauze. The user input compensated for deformations due to tearing. Task completion time of those experiments was 86 seconds (median) / 88 seconds (mean). Median and mean errors were 3.1 mm and 3 mm, respectively. Our work improves the success rate and time of completion of published pattern cutting tasks.