A key component to the success of master-slave surgical systems is the quality of the master interface used to relay the surgeon's instructions to the slave robot. In previous work the authors developed a gaze-assisted intention recognition scheme, allowing the system to dynamically adapt the motion scaling based on where the user is trying to reach. This allowed users to perform tasks significantly more quickly and with less need for clutching. However, the system possessed a number of core parameters that were manually optimized, potentially providing a non-optimal solution depending on the user. This paper presents a Bayesian approach to the problem of optimizing a human-robot interface in a user-specific manner. Two Bayesian optimization methods are studied: one in which users are asked to grade robot behavior for a given set of parameters, and one where only preference relative to other parameter sets is expressed. The performance of these optimizations is evaluated in a blind comparison user study, demonstrating that the optimized parameters are preferred to the manually optimized ones in over 90 % of cases after only 12 test samples. These parameters are further shown to perform at least as well as the manually optimized ones in all cases, and showing statistically significant improvement in the case of the graded optimization.

While minimally invasive surgery offers great benefits in terms of reduced patient trauma, bleeding, as well as faster recovery time, it still presents surgeons with major ergonomic challenges. Laparoscopic surgery requires the surgeon to bimanually control surgical instruments during the operation. A dedicated assistant is thus required to manoeuvre the camera, which is often difficult to synchronise with the surgeon's movements. This article introduces a robotic system in which a rigid endoscope held by a robotic arm is controlled via the surgeon's eye movement, thus forgoing the need for a camera assistant. Gaze gestures detected via a series of eye movements are used to convey the surgeon's intention to initiate gaze contingent camera control. Hidden Markov Models (HMMs) are used for real-time gaze gesture recognition, allowing the robotic camera to pan, tilt, and zoom, whilst immune to aberrant or unintentional eye movements. A novel online calibration method for the gaze tracker is proposed, which overcomes calibration drift and simplifies its clinical application. This robotic system has been validated by comprehensive user trials and a detailed analysis performed on usability metrics to assess the performance of the system. The results demonstrate that the surgeons can perform their tasks quicker and more efficiently when compared to the use of a camera assistant or foot switches.

Traditional robotic surgical systems rely entirely on robotic arms to triangulate articulated instruments inside the human anatomy. This configuration can be ill-suited for working in tight spaces or during single access approaches, where little to no triangulation between the instrument shafts is possible. The control of these instruments is further obstructed by ergonomic issues: The presence of motion scaling imposes the use of clutching mechanics to avoid the workspace limitations of master devices, and forces the user to choose between slow, precise movements, or fast, less accurate ones. This paper presents a bi-manual system using novel self-triangulating 6-degrees-of-freedom (DoF) tools through a flexible elbow, which are mounted on robotic arms. The control scheme for the resulting 9-DoF system is detailed, with particular emphasis placed on retaining maximum dexterity close to joint limits. Furthermore, this paper introduces the concept of gaze-assisted adaptive motion scaling. By combining eye tracking with hand motion and instrument information, the system is capable of inferring the user's destination and modifying the motion scaling accordingly. This safe, novel approach allows the user to quickly reach distant locations while retaining full precision for delicate manoeuvres. The performance and usability of this adaptive motion scaling is evaluated in a user study, showing a clear improvement in task completion speed and in the reduction of the need for clutching.

Suturing is an essential requirement for surgical robots. Current designs of needle drivers require significant forces to be applied so that the needle will not flip or slip between the jaws when inserted into the tissue. The required force implies that most designs are based on rigid instruments with a straight shaft that induces less dexterity. Flexible robotics provides great dexterity to achieve the task, although often it lacks the capability to apply large forces and therefore hold the surgical needle firmly. Solutions can be found in how the needle driver's jaws are designed. This paper presents improved designs for needle grasping tools for flexible robotic instruments, to be produced directly with selective laser melting. Features on the gripping surface were embedded to increase the gripping effectiveness even in the presence of limited grasping force. A detailed characterisation has been performed for each design showing improvements with respect to the state of the art.

The performance of a master-slave robotic system depends significantly on the ergonomics and the capability of its master device to correctly interface the user with the slave robot. Master manipulators generating commands in task space represent a commonly adopted solution for controlling a range of slave robots while retaining an ergonomic design. However, these devices present several drawbacks, such as requiring the use of clutching mechanics to compensate for the mismatch between slave and master workspaces, and the lack of capability to intuitively transmit important information such as specific joint limits to the user. In this paper, a novel joint-space master manipulator is presented. This manipulator emulates the kinematic structure of highly flexible surgical instruments which it is designed to control. This system uses 6 active degrees of freedom to compensate for its own weight, as well as to provide force feedback corresponding to the slave robot's joint limits. A force/torque sensor integrated at the end effector is used to relay user-generated forces and torques directly to specific joints. This is performed to counteract the friction stemming from structural constraints imposed by the kinematic design of the instruments. Finally, a usability study is carried out to test the validity of the system, proving that the instruments can be intuitively controlled even at the extremities of the workspace.

Eye tracking technology has shown promising results for allowing hands-free control of robotically-mounted cameras and tools. However existing systems present only limited capabilities in allowing the full range of camera motions in a safe, intuitive manner. This paper introduces a framework for the recognition of surgeon intention, allowing activation and control of the camera through natural gaze behaviour. The system is resistant to noise such as blinking, while allowing the surgeon to look away safely at any time. Furthermore, this paper presents a novel approach to control the translation of the camera along its optical axis using a combination of eye tracking and stereo reconstruction. Combining eye tracking and stereo reconstruction allows the system to determine which point in 3D space the user is fixating, enabling a translation of the camera to achieve the optimal viewing distance. In addition, the eye tracking information is used to perform automatic laser targeting for laser ablation. The desired target point of the laser, mounted on a separate robotic arm, is determined with the eye tracking thus removing the need to manually adjust the laser's target point before starting each new ablation. The calibration methodology used to obtain millimetre precision for the laser targeting without the aid of visual servoing is described. Finally, a user study validating the system is presented, showing clear improvement with median task times under half of those of a manually controlled robotic system.

The surgical robotics community have developed many different flexible robot designs to address the access problems of minimally invasive surgery. In this paper, we present a hand-held mechatronic tool with a miniaturized distal flexible manipulator incorporating a microscopy probe, camera and a light source for diagnostic arthroscopy. Extensive characterization of the flexible manipulator is provided, including an optimization of the manipulator workspace, hysteresis characteristics and repeatability of the instrument. The lateral stiffness of the flexible manipulator for different bending conditions is assessed along with the overall robustness of the platform. A cadaveric study was performed to demonstrate the potential clinical value of the device.

In recent years, robotic systems have been playing an increasingly important role in physiotherapy. The aim of these platforms is to aid the recovery process from strokes or muscular damage by assisting patients to perform a number of controlled tasks, thus effectively complementing the role of the physiotherapist. In this paper, we present a novel learning from demonstration framework for cooperative control in robotic-assisted physiotherapy. Unlike other approaches, the aim of the proposed system is to guide the patients to optimally execute a task based on previously learned demonstrations. This allows the generation of patient-specific gestures under the supervision of the expert physiotherapist. The guidance is performed through stiffness control of a compliant manipulator, where the stiffness profile of the generalized trajectory is determined according to the relative importance of each section of the task. In contrast with the traditional learning approach, where the execution of the generalized trajectory by the robot is automated, this cooperative control architecture allows the patients to perform the task at their own pace, while ensuring the movements are executed correctly. Increased performance of the learning framework is accomplished through a novel fast, low-cost multi-demonstration dynamic time warping algorithm used to build the model. Experimental validation of the framework is carried out using an interactive setup designed to provide further guidance through additional visual and sensory feedback based on the task model. The results demonstrate the potential of the proposed framework, showing a significant improvement in the performance of guided tasks compared to unguided ones.