Abstract-The use of technology for automation of surgical tasks has great potential in the field of robotic surgery. Automation of surgical robots essentially involves designing a control scheme for precise and automatic positioning of robotic manipulators. This objective can be achieved by the use of Visual servoing.We have developed a visual-servoing-based robotic laparoscopic surgery system. The visual servoing system enables autonomous control of a robot arm in a surgical set-up. In this paper, we propose algorithms for the automatic positioning of the laparoscope and the surgical instruments under small incisions of skin. We have verified the performance of the system theoretically and experimentally.

Abstract-Despite the fact that minimally invasive robotic surgery provides many advantages for patients, such as reduced tissue trauma and shorter hospitalization, complex tasks (e.g. tissue piercing or knot-tying) are still time-consuming, errorprone and lead to quicker fatigue of the surgeon. Automating these recurrent tasks could greatly reduce total surgery time for patients and disburden the surgeon while he can focus on higher level challenges. This work tackles the problem of autonomous tissue piercing in robot-assisted laparoscopic surgery with a circular needle and general purpose surgical instruments. To command the instruments to an incision point, the surgeon utilizes a laser pointer to indicate the stitching area. A precise positioning of the needle is obtained by means of a switching visual servoing approach and the subsequent stitch is performed in a circular motion.

To gain autonomy in the OR, a variety of assistance systems and methodologies need to be incorporated that endorse the surgeon autonomously as a first step toward the vision of cognitive surgery. Thus, we require establishment of model-based surgery and integration of procedural tasks. Structured knowledge is therefore indispensable.

Abstract-Surgical tool tracking is an important key functionality for many high-level tasks in both robot-assisted and conventional minimally invasive surgery. Though the fields of application are similar in both surgery techniques (i.e. visually servoed instruments, workflow analysis or augmented reality), the kind of available information about the position and orientation of the surgical tool differ. In conventional laparoscopic surgery additional information to the images provides by the endoscopic camera can only be obtained by an external tracking system. In contrast, robotic systems provide angular informations from encoder readings that allow for a sufficient pose estimation and initialization of an image-based tracking algorithm. Our approach utilizes both encoder readings and visual information, in order to stabilize tracking in image space. The image-based tracking is supervised by means of the kinematic information and reinitialized in case of conflicting results. As tracking modality we utilize the Contracting Curve Density (CCD) algorithm that looks for maximal separation of local color statistics along the contour of a model.

Background Transferring non-trivial human manipulation skills to robot systems is a challenging task. There have been a number of attempts to design research systems for skill transfer, but the level of the complexity of the actual skills transferable to the robot was rather limited, and delicate operations requiring a high dexterity and long action sequences with many sub-operations were impossible to transfer.