Spherical robotics is an emerging research field due to a ball’s characteristic to be holonomic, have a sealed internal environment, and rebound from collisions easily. As the research moves forward, individual groups have begun to develop unique methods of propulsion, each having distinctive engineering trade-offs: weight is sacrificed for power; speed is forfeited for control accuracy, etc. Early spherical robots operated similar to a hamster ball and had a limited torque and a high-energy loss due to internal friction. Researchers have begun to develop various novel concepts to maneuver and control this family of robot. This article is an overview of the current research directions that various groups have taken, the nomenclature used in this subdiscipline, and the various uses of the fundamental principles of physics for propelling a spherical robot.

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.

There is a substantial number of telerobotics and teleoperation applications ranging from space operations, ground/aerial robotics, drive-by-wire systems to medical interventions. Major obstacles for such applications include latency, channel corruptions, and bandwidth which limit teleoperation efficacy. This survey reviews the time delay problem in teleoperation systems. We briefly review different solutions from early approaches which consist of control-theory-based models and user interface designs and focus on newer approaches developed since 2014. Future solutions to the time delay problem will likely be hybrid solutions which include modeling of user intent, prediction of robot movements, and time delay prediction all potentially using time series prediction methods. Hence, we examine methods that are primarily based on time series prediction. Recent prediction approaches take advantage of advances in nonlinear statistical models as well as machine learning and neural network techniques. We review Recurrent Neural Networks, Long Short-Term Memory, Sequence to Sequence, and Generative Adversarial Network models and examine each of these approaches for addressing time delay. As time delay is still an unsolved problem, we suggest some possible future research directions from information-theory-based modeling, which may lead to promising new approaches to advancing the field.

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.

Positioning a camera during laparoscopic and robotic procedures is challenging and essential for successful operations. During surgery, if the camera view is not optimal, surgery becomes more complex and potentially error-prone. To address this need, we have developed a voice interface to an autonomous camera system that can trigger behavioral changes and be more of a partner to the surgeon. Similarly to a human operator, the camera can take cues from the surgeon to help create optimized surgical camera views. It has the advantage of nominal behavior that is helpful in most general cases and has a natural language interface that makes it dynamically customizable and on-demand. It permits the control of a camera with a higher level of abstraction. This paper shows the implementation details and usability of a voice-activated autonomous camera system. A voice activation test on a limited set of practiced key phrases was performed using both online and offline voice recognition systems. The results show an on-average greater than 94% recognition accuracy for the online system and 86% accuracy for the offline system. However, the response time of the online system was greater than 1.5 s, whereas the local system was 0.6 s. This work is a step towards cooperative surgical robots that will effectively partner with human operators to enable more robust surgeries. A video link of the system in operation is provided in this paper.