Master manipulators represent a key component of Robot-Assisted Minimally Invasive Surgery (RAMIS). In this paper, an Analytic Hierarchy Process (AHP) method is used to construct an effective workspace analysis framework, which can assist the configuration selection and design evaluation of a portable master manipulator for surgical robot control and training. The proposed framework is designed based on three criteria: 1) compactness, 2) workspace quality, and 3) mapping efficiency. A hardware prototype, called the Hamlyn Compact Robotic Master (Hamlyn CRM), is constructed following the proposed framework. Experimental verification of the platform is conducted on the da Vinci Research Kit (dVRK) with which a da Vinci robot is controlled as a slave. The proposed Hamlyn CRM is compared with Phantom Omni, a commercial portable master device, with results demonstrating the relative merits of the new platform in terms of task completion time, average control speed and number of clutching.

Estimating the three-dimensional (3D) position of microrobots is necessary in order to develop closed-loop control techniques and to improve the user's 3D perception in the micro-scale. This paper describes a depth estimation method based on supervised learning for optically transparent microrobots of known geometry. The proposed methodology uses Convolutional Neural Networks (CNNs) combined with a Recurrent Network, in particular a Long Short-Term Memory (LSTM) cell for depth regression. The proposed depth regression model is independent of the 3D orientation of the microrobot and is robust to varying illumination levels while it uses learned data-specific features. The model is trained and validated using microscope images and ground truth data generated from 3D-printed microrobots imaged in an Optical Tweezers (OT) setup. The validation results demonstrate that the proposed trained model can reconstruct the depth of the microrobot independently of its 3D orientation with submicron accuracy for the test set.

Task autonomy is an important consideration for the development of future surgical robots. For robot-assisted anastomosis, suture thread detection is a prerequisite for subsequent robot manipulation. Previous works on automatic thread detection are focused on the learning of the models with specific surgical settings that are poorly generalisable to generic settings. In this paper, we propose a joint feature learning framework that caters for the foreground and background adaptation for surgical suture thread detection. The proposed method is developed in the context of semi-supervised and unsupervised domain adaptation, leveraging the labelled training data from the source domain to learn the detection model for unlabelled or partially labelled target domain, which can also be from different types of threads or organs. Based on adversarial learning, we further preserve the semantic identity and introduce curriculum adaptation to generate synthetic data. Experiments on four domain adaptation tasks for suture thread detection demonstrate the strength of the proposed method being able to generate good quality synthetic data and transfer between specific domains with limited or even no labelled data of the target domain.

This paper presents a versatile robotic system for sewing a 3D structured object. Leveraging on using a customized robotic sewing device and closed-loop visual servoing control, an all-in-one solution for sewing personalized stent graft is demonstrated. Stitch size planning and automated knot tying are proposed as two key functions of the system. By using effective stitch size planning, sub-millimetre sewing accuracy is achieved for stitch sizes ranging from 2mm to 5mm. In addition, a thread manipulator for thread management and tension control is also proposed to perform successive knot tying to secure each stitch. Detailed laboratory experiments have been performed to evaluate the proposed instruments and allied algorithms. The proposed framework can be generalised to a wide range of applications including 3D industrial sewing, as well as transferred to other clinical areas such as surgical suturing.

Robot-assisted minimally invasive surgery can benefit from the automation of common, repetitive or well-defined but ergonomically difficult tasks. One such task is the scanning of a pick-up endomicroscopy probe over a complex, undulating tissue surface to enhance the effective field-of-view through video mosaicing. In this paper, the da Vinci®surgical robot, through the dVRK framework, is used for autonomous scanning and 2D mosaicing over a user-defined region of interest. To achieve the level of precision required for high quality mosaic generation, which relies on sufficient overlap between consecutive image frames, visual servoing is performed using a combination of a tracking marker attached to the probe and the endomicroscopy images themselves. The resulting sub-millimetre accuracy of the probe motion allows for the generation of large mosaics with minimal intervention from the surgeon. Images are streamed from the endomicroscope and overlaid live onto the surgeons view, while 2D mosaics are generated in real-time, and fused into a 3D stereo reconstruction of the surgical scene, thus providing intuitive visualisation and fusion of the multi-scale images. The system therefore offers significant potential to enhance surgical procedures, by providing the operator with cellular-scale information over a larger area than could typically be achieved by manual scanning.

Six degree-of-freedom (DoF) pose feedback is essential for the development of closed-loop control techniques for microrobotics. This paper presents two methods for depth estimation of transparent microrobots inside an Optical Tweezers (OT) setup using image sharpness measurements and model-based tracking. The x-y position and the 3D orientation of the object are estimated using online model-based template matching. The proposed depth estimation methodologies are validated experimentally by comparing the results with the ground truth.

This paper proposes a semi-autonomous navigated master-slave system, for robot assisted remote echography for early trauma assessment. Two RGB-D sensors are used to capture real-time 3D information of the scene at the slave side where the patient is located. A 3D statistical shape model is built and used to generate a customized patient model based on the point cloud generated by the RGB-D sensors. The customized patient model can be updated and adaptively fitted to the patient. The model is also used to generate a trajectory to navigate a KUKA robotic arm and safely conduct the ultrasound examination. Extensive validation of the proposed system shows promising results in terms of accuracy and robustness.