This article contributes a novel method to assess robot dexterity. Existing Jacobian-based dexterity metrics, such as the manipulability index or the condition number, do not allow for comparisons between robot architectures, are local in nature, and are affected by robot dimensions (robot size). On the contrary, the introduced metric is global and allows for quantitative comparisons of robot architectures as it explicitly incorporates the orientational and positional coverage of a robot's end-effector. Experiments presented show that the proposed dexterity metric can improve the computational and precision performance of numerical inverse kinematics and showcase its suitability for use in computational dexterous robot design and, in particular, for designing concentric tube robots with high orientational and positional dexterity.

Recent advances in teleoperation-based robotic-assisted Minimally Invasive Surgery (MIS) have made significant inroads in clinical adoption. However, such master-slave surgical systems create a physical separation between the surgeon and the patient. The concept of Active Constraints (ACs) provides guidance and sensory information to surgical robot operators in a form of haptic, visual or audible cues. This work proposes a novel ACs approach to avoid surgical tool-clashing and collision of the tool-shaft with delicate anatomy using elasto-plastic frictional force control. The presented framework is designed to reduce the occurence of direct coupling during electrocautery and to protect high-risk regions in Minimally Invasive Partial Nephrectomy (MIPN). Moreover, we combine aforementioned ACs methods and propose a solution when simultaneous penetration of both constraints occurs. The proposed methodology is implemented on the teleoperated da Vinci Surgical System using the da Vinci Research Kit (dVRK) and its performance is demonstrated through three types of user experiments. The experimental results show that the developed algorithms are of significant benefit in performing the tasks with ACs assistance.

The use of snake-like robots for surgery is a popular choice for intra-luminal procedures. In practice, the requirements for strength, flexibility and accuracy are difficult to be satisfied simultaneously. This paper presents a computational approach for optimizing the design of a snake-like robot using serial rolling-joints and tendons as the base architecture. The method optimizes the design in terms of joint angle range and tendon placement to prevent the tendons and joints from colliding during bending motion. The resulting optimized joints were manufactured using 3D printing. The robot was characterized in terms of workspace, dexterity, precision and manipulation forces. The results show a repeatability as low as 0.9mm and manipulation forces of up to 5.6N.

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.

Effective in situ, in vivo tumour margin assessment is an important, yet unmet, clinical demand in surgical oncology. Recent advances in probe-based optical imaging tools such as confocal endomicroscopy is making inroads in clinical applications. In practice, maintaining consistent tissue contact whilst ensuring large area surveillance is crucial for its practical adoption and for this reason there is a great demand for robotic assistance so that high-speed endomicroscopes can be combined with autonomous scanning, thus simplifying its incorporation in routine surgical workflows. In this paper, a cooperatively controlled robotic manipulator is developed, which provides a stable mechatronically-enhanced platform for micro-scanning tools to perform local high resolution mosaics over 3D undulating moving surfaces. Detailed kinematic and overall system performance analyses are provided and the results demonstrate the adaptability in terms of both contact force and orientation control of the system, and thus its simplicity in practical deployment and value for clinical adoption.

The use of concentric tube robots has recognized advantages for accessing target lesions while conforming to certain anatomical constraints. However, their complex kinematics makes their safe telemanipulation in convoluted anatomy a challenging task. Collaborative control schemes, which guide the operator through haptic and visual feedback, can simplify this task and reduce the cognitive burden of the operator. Guaranteeing stable, collision-free robot configurations during manipulation, however, is computationally demanding and, until now, either required long periods of pre-computation time or distributed computing clusters. Furthermore, the operator is often presented with guidance paths which have to be followed approximately. This paper presents a heterogeneous (CPU/GPU) computing approach to enable rapid workspace analysis on a single computer. The method is used in a new navigation scheme that guides the robot operator towards locations of high dexterity or manipulability of the robot. Under this guidance scheme, the user can make informed decisions and maintain full control of the path planning and manipulation processes, with intuitive visual feedback on when the robot's limitations are being reached.

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.

Motor channelling is a concept to provide navigation and sensory feedback to operators in master-slave surgical setups. It is beneficial since the introduction of robotic surgery creates a physical separation between the surgeon and patient anatomy. Active Constraints/Virtual Fixtures are proposed which integrate Guidance and Forbidden Region Constraints into a unified control framework. The developed approach provides guidance and safe manipulation to improve precision and reduce the risk of inadvertent tissue damage. Online three-degree-of-freedom motion prediction and compensation of the target anatomy is performed to complement the master constraints. The presented Active Constraints concept is applied to two clinical scenarios; surface scanning for in situ medical imaging and vessel manipulation in cardiac surgery. The proposed motor channelling control strategy is implemented on the da Vinci Surgical System using the da Vinci Research Kit (dVRK) and its effectiveness is demonstrated through a detailed user study.

Safe and effective telemanipulation of concentric tube robots is hindered by their complex, non-intuitive kinematics. Guidance schemes in the form of attractive and repulsive constraints can simplify task execution and facilitate natural operation of the robot by clinicians. The real-time seamless calculation and application of guidance, however, requires computationally efficient algorithms that solve the non-linear inverse kinematics of the robot and guarantee that the commanded robot configuration is stable and sufficiently away from the anatomy. This paper presents a multi-processor framework that allows on-the-fly calculation of optimal safe paths based on rapid workspace and roadmap pre-computation The real-time nature of the developed software enables complex guidance constraints to be implemented with minimal computational overhead. A user study on a simulated challenging clinical problem demonstrated that the incorporated guiding constraints are highly beneficial for fast and accurate navigation with concentric tube robots.

This paper introduces the design and characterization of a robotic surgical instrument produced mainly with rapid prototyping techniques. Surgical robots have generally complex structures and have therefore an elevated cost. The proposed instrument was designed to incorporate minimal number of components to simplify the assembly process by leveraging the unique strength of rapid prototyping for producing complex, assemble-free components. The modularity, cost-effectiveness and fast manufacturing and assembly features offer the possibility of producing patient or task specific instruments. The proposed robot incorporates an integrated force measurement system, thus allowing the determination of the force exchanged between the instrument and the environment. Detailed experiments were performed to validate the functionality and force sensing capability of the instrument.

The use of conventional surgical tool holders requires an assistant during positioning and adjustment due to the lack of weight compensation. In this paper, we introduce a robotic arm system with hands-on control approach. The robot incorporates a force sensor at the end effector which realises tool weight compensation as well as hands-on manipulation. On the operating table, the required workspace can be tight due to a number of instruments required. There are situations where the surgical tool is at the desired location but the holder arm pose is not ideal due to space constraints or obstacles. Although the arm is a non-redundant robot because of the limited degrees of freedom, the pseudo-null-space inverse kinematics can be used to constrain a particular joint of the robot to a specific angle while the other joints compensate in order to minimise the tool movement. This allows operator to adjust the arm configuration conveniently together with the weight compensation. Experimental results demonstrated that our robotic arm can maintain the tool position during reconfiguration significantly more stably than a conventional one.

Ongoing technological advances in the areas of computation, sensing, and mechatronics enable robotic-based systems to interact with humans in the real world. To succeed against a human in a competitive scenario, a robot must anticipate the human behavior and include it in its own planning framework. Then it can predict the next human move and counter it accordingly, thus not only achieving overall better performance but also systematically exploiting the opponent's weak spots. Pool is used as a representative scenario to derive a model-based planning and control framework where not only the physics of the environment but also a model of the opponent is considered. By representing the game of pool as a Markov decision process and incorporating a model of the human decision-making based on studies, an optimized policy is derived. This enables the robot to include the opponent's typical game style into its tactical considerations when planning a stroke. The results are validated in simulations and real-life experiments with an anthropomorphic robot playing pool against a human.

Concentric tube robots are catheter-sized robots that are ideally suited for navigating along natural anatomical pathways and treating deep-seated pathologies. Their telemanipulation in dynamic environments requires on-line computation of inverse kinematics with simultaneous avoidance of anatomical obstacles. Moreover, unstable configurations, which arise for elongated curved robots that navigate extremely tortuous paths, must be avoided. To achieve on-line computations, existing work has investigated Jacobian approximations and configuration-space precomputation. This paper leverages the state-of-the-art multi-core computer architectures to deliver real-time local inverse kinematics solutions using the established concentric tube robot mechanics models while avoiding both instabilities and anatomical collisions. Furthermore, it considers frictional active constraints for concentric tube robots, i.e. viscoelastic force fields that guide the operator away from obstacles and towards safe configurations. The value of the proposed framework is demonstrated on realistic clinical scenarios.

Active constraints are high-level control algorithms providing software-generated force feedback from virtual environments. When applied to surgery, they can assist surgeons in performing complex tasks by guiding their navigation pathways along narrow, possibly convoluted, surgical trajectories. This paper presents a method to generate concave tubular constraints implicitly from pre- or intra-operative data. Patient-specific constraints may be generated efficiently with the proposed scheme and readily deployed in various surgical scenarios. Furthermore, a five degree-of-freedom active constraint framework is proposed, which accounts for the entire tool shaft rather than just the end-effector, and is applicable to both static and dynamic active constraint scenarios. Experimental results on simulated surgical tasks show that this framework can improve safety and accuracy as well as reduce the perceived workload during complex surgical tasks.

Precise and repetitive positional control of surgical robots is important to reduce time and risks of surgical procedures. These factors become particularly important when deploying the surgical system through a flexible path to areas with a tight workspace such as the stomach or oesophagus where high dexterity, flexibility, accuracy and stability are required. This paper presents a flexible access robot combining articulated joints and continuum flexible section for both transluminal and endoluminal surgeries. Kinematic model and control strategy for the flexible robot are described in the paper. The experiment simulating a transoral gastric procedure demonstrates great flexibility and dexterity of the device. The results show that good accuracy and repetitive control of the device are achieved, which demonstrate the potential application of the device for transluminal or endoluminal surgery.