Luke A. Reisner

This paper describes the development of a new method to control the camera arm of a surgical robot and create a better sense of remote presence for the surgeon. The current surgical systems are entirely controlled by the surgeon, using hand controllers and foot pedals to manipulate either the instrument or the camera arms. The surgeon must pause the operation to move the camera arm to obtain a desired view and then resume the operation. The camera and tools cannot be moved simultaneously, leading to interrupted and unnatural movements. These interruptions can lead to medical errors and extended operation times. In our system, the surgeon controls the camera arm by his natural head movements while being immersed in a 3D-stereo view of the scene with a head-mounted display (HMD). The novel approach enables the camera arm to be maneuvered based on sensors of the HMD. We implemented this method on a da Vinci Standard Surgical System using the HTC Vive headset along with the Unity engine and the Robot Operating System framework. This paper includes the result of a subjective six-participant usability study that compares the workload of the traditional clutched camera control method against the HMD-based control. Initial results indicate that the system is usable, stable, and has a lower physical and mental workload when using the HMD control method.

This paper describes a recording and playback system developed using a da Vinci Standard Surgical System and research kit. The system records stereo laparoscopic videos, robot arm joint angles, and surgeon–console interactions in a synchronized manner. A user can then, on-demand and at adjustable speeds, watch stereo videos and feel recorded movements on the hand controllers of entire procedures or sub procedures. Currently, there is no reported comprehensive ability to capture expert surgeon movements and insights and reproduce them on hardware directly. This system has important applications in several areas: (1) training of surgeons, (2) collection of learning data for the development of advanced control algorithms and intelligent autonomous behaviors, and (3) use as a “black box” for retrospective error analysis. We show a prototype of such an immersive system on a clinically-relevant platform along with its recording and playback fidelity. Lastly, we convey possible research avenues to create better systems for training and assisting robotic surgeons.

Camera positioning is critical for all telerobotic surgical systems. Inadequate visualization of the remote site can lead to serious errors that can jeopardize the patient. An autonomous camera algorithm has been developed on a medical robot (da Vinci) simulator. It is found to be robust in key scenarios of operation. This system behaves with predictable and expected actions for the camera arm with respect to the tool positions. The implementation of this system is described herein. The simulation closely models the methodology needed to implement autonomous camera control in a real hardware system. The camera control algorithm follows three rules: (1) keep the view centered on the tools, (2) keep the zoom level optimized such that the tools never leave the field of view, and (3) avoid unnecessary movement of the camera that may distract/disorient the surgeon. Our future work will apply this algorithm to the real da Vinci hardware.

Complex teleoperative tasks, such as surgery, generally require human control. However, teleoperating a robot using indirect visual information poses many technical challenges because the user is expected to control the movement(s) of the camera(s) in addition to the robot’s arms and other elements. For humans, camera positioning is difficult, error-prone, and a drain on the user’s available resources and attention. This paper reviews the state of the art of autonomous camera control with a focus on surgical applications. We also propose potential avenues of research in this field that will support the transition from direct slaved control to truly autonomous robotic camera systems.

Image guidance is a technique that often uses virtual reality to provide accurate localization and real-time surgical navigation. Combining image guidance with a biosensor based on Raman spectroscopy, a powerful laser-based analysis technique, would provide a surgeon both a diagnosis of tissue being analyzed (e.g. cancer) and localization information displayed within an imaging modality of choice. A virtual reality-based presentation of this type of mutual and registered information could lead to faster diagnoses and enable more accurate tissue resections. For our system, a portable Raman probe was attached to a passively articulated mechanical arm and used to scan and classify objects within a phantom skull. We discuss the implementation of the integrated system, its accuracy, its visualization techniques, and the future steps for its development and eventual application.