Jacob Rosén

Sahba Aghajani Pedram, Peter Walker Ferguson, Changyeob Shin et al.

In this paper, we present a synergic learning algorithm to address the task of indirect manipulation of an unknown deformable tissue. Tissue manipulation is a common yet challenging task in various surgical interventions, which makes it a good candidate for robotic automation. We propose using a linear approximate Q-learning method in which human knowledge contributes to selecting useful yet simple features of tissue manipulation while the algorithm learns to take optimal actions and accomplish the task. The algorithm is implemented and evaluated on a simulation using the OpenCV and CHAI3D libraries. Successful simulation results for four different configurations which are based on realistic tissue manipulation scenarios are presented. Results indicate that with a careful selection of relatively simple and intuitive features, the developed Q-learning algorithm can successfully learn an optimal policy without any prior knowledge of tissue dynamics or camera intrinsic/extrinsic calibration parameters.

Yangming Li, Blake Hannaford, Jacob Rosen

The Raven I and the Raven II surgical robots, as open research platforms, have been serving the robotic surgery research community for ten years. The paper 1) briefly presents the Raven I and the Raven II robots, 2) reviews the recent publications that are built upon the Raven robots, aim to be applied to the Raven robots, or are directly compared with the Raven robots, and 3) uses the Raven robots as a case study to discuss the popular research problems in the research community and the trend of robotic surgery study. Instead of being a thorough literature review, this work only reviews the works formally published in the past three years and uses these recent publications to analyze the research interests, the popular open research problems, and opportunities in the topic of robotic surgery.

S. Aghajani Pedram, P. Ferguson, J. Ma et al.

Needle shape, diameter, and path are critical parameters that directly affect suture depth and tissue trauma in autonomous suturing. This paper presents an optimization-based approach to specify these parameters. Given clinical suturing guidelines, a kinematic model of needle-tissue interaction was developed to quantify suture parameters and constraints. The model was further used to formulate constant curvature needle path planning as a nonlinear optimization problem. The optimization results were confirmed experimentally with the Raven II surgical system. The proposed needle path planning algorithm guarantees minimal tissue trauma and complies with a wide range of suturing requirements.