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Robot-assisted minimally invasive surgery has made a substantial impact in operating rooms over the past few decades with their high dexterity, small tool size, and impact on adoption of minimally invasive techniques. In recent years, intelligence and different levels of surgical robot autonomy have emerged thanks to the medical robotics endeavors at numerous academic institutions and leading surgical robot companies. To accelerate interaction within the research community and prevent repeated development, we propose the Collaborative Robotics Toolkit (CRTK), a common API for the RAVEN-II and da Vinci Research Kit (dVRK) - two open surgical robot platforms installed at more than 40 institutions worldwide. CRTK has broadened to include other robots and devices, including simulated robotic systems and industrial robots. This common API is a community software infrastructure for research and education in cutting edge human-robot collaborative areas such as semi-autonomous teleoperation and medical robotics. This paper presents the concepts, design details and the integration of CRTK with physical robot systems and simulation platforms. 

Robot-assisted minimally invasive surgery has made a substantial impact in operating rooms over the past few decades with their high dexterity, small tool size, and impact on adoption of minimally invasive techniques. In recent years, intelligence and different levels of surgical robot autonomy have emerged thanks to the medical robotics endeavors at numerous academic institutions and leading surgical robot companies. To accelerate interaction within the research community and prevent repeated development, we propose the Collaborative Robotics Toolkit (CRTK), a common API for the RAVEN-II and da Vinci Research Kit (dVRK) - two open surgical robot platforms installed at more than 40 institutions worldwide. CRTK has broadened to include other robots and devices, including simulated robotic systems and industrial robots. This common API is a community software infrastructure for research and education in cutting edge human-robot collaborative areas such as semi-autonomous teleoperation and medical robotics. This paper presents the concepts, design details and the integration of CRTK with physical robot systems and simulation platforms. 

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. 

Compressing soft-obstacles secondary to a con- trolled motion task is common for human beings. While these tasks are nearly trivial for teleoperated robots, they remain a challenging problem in robotic autonomy. Addressing the problem is significant. For example, in Minimally Invasive Surgeries (MISs), safely compressing soft tissues ensures the surgical safety and decreases tissue removal, thus dramatically decreases surgical trauma and operating room time, and leads to improved surgical outcomes. In this work, we define the problem of soft-obstacle avoidance and project the safety motion constraints into the task space and the velocity space. We illustrate the significance of addressing this problem in the robotic surgery scenario. We present a Recurrent Neural Networks (RNNs) based solution, which for- mulates the problem as an inequality constrained optimization problem and solves it in its dual space. The application of the proposed method was demonstrated in the Raven II surgical robot. Experimental results demonstrated that the proposed method is effective in addressing the soft-obstacle avoidance problem. 

Surgical robot research is driven by the desire of improving surgical outcomes. This paper proposed a Recurrent Neural Network based controller to address two problems: 1) improving control precision, 2) increasing adaptiveness for robot motion (explained in Section I). RNN was adopted in this work mainly because 1) the problem formulation naturally matches RNN structure, 2) RNN has advantages as an biologi- cally inspired method. The proposed method was explained in detail and analysis shows that the proposed method is able to dynamically regulate outputs to increase the adaptiveness and the control precision. This paper uses Raven II surgical robot as an example to show the application of the proposed method, and the numeral simulation results from the proposed method and three other controllers show that the proposed method has improved precision, improved high robustness against noise and increased movement smoothness, and it keeps the manipulator links as far away as possible from physical boundaries, which potentially increases surgical safety and leads to improved surgical outcomes.