Colonoscopy is a medical procedure used to examine the inside of the colon for abnormalities, such as polyps or cancer. Traditionally, this is done by manually inserting a long, flexible tube called a colonoscope into the colon. However, this method can cause pain, discomfort, and even the risk of perforation. To address these shortcomings, advancements in technology are needed to develop safer, more intelligent colonoscopes. This paper presents the design, control and evaluation of a self-growing soft robotic colonoscope, leveraging the evertion principle. The device features a tube with an 18 mm diameter, constructed from stretchable fabric, which grows 1.6 m at the tip under pressurization. A pneumatically driven, elastomer-based manipulator enables omni-directional steering over$180^\circ$at the tip. An airtight base houses motors and spools that control the material and regulate growth speed. The robot operates in two modes: teleoperation via joysticks and autonomous navigation using sensor inputs, such as a tip-mounted camera. Thoroughin-vitroexperiments are conducted to assess the system's functionality and performance. Results illustrate that the robot can achieve locomotion in confined spaces such as a colon phantom, while exerting contact forces averaging less than 0.3 N. Our soft robot shows potential for improving the safety and autonomy of colonoscopies, while reducing discomfort to patients.

While surgical robots, such as the da Vinci Surgical System, have become prevalent in minimally invasive surgery, they are predominantly used by the human operator to directly teleoperate the tools. This paper aims to analyse the different methods of human robot shared control in the surgical domain. We propose a reinforcement learning algorithm, transverse generative adversarial imitation learning (tGAIL), which is employed to train the robot from the expert’s demonstration and show competitive generalization ability compared to inverse reinforcement learning and conventional GAIL. We then propose a priority-changing shared control method to effectively combine the surgeon and robot’s strengths by dynamically adjusting control priority based on the deviation distance. We show that using this method in a supervision framework boosts the performance of the human operator when completing the peg transfer task. By learning from the expert and collaborating with the human during the task, the intelligent agent can help to reduce operation time by 31.7% and the human input by 60.5% compared to direct teleoperation.

This work proposes a state-of-the-art transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences. The network architecture was inspired by the Attention in Attention mechanism, temporal tracking networks, and introduced a novel 3D segmentation head that performs 3D deconvolution across time. To train the network, we introduce a new data synthesis pipeline that uses physics-based catheter insertion simulations, along with a convolutional ray-casting ultrasound simulator to produce synthetic ultrasound images of endovascular interventions. The proposed method is validated on a hold-out validation dataset, thus demonstrated robustness to ultrasound noise and a wide range of scanning angles. It was also tested on data collected from silicon aorta phantoms, thus demonstrated its potential for translation from sim-to-real. This work represents a significant step towards safer and more efficient endovascular surgery using interventional ultrasound.

This paper presents a novel pneumatic pouch sensor, designed to mount on a colonoscope, that can effectively estimate the contact forces with the environment. The pouch sensor was designed to maximize the sensing range, and it was fabricated using a 2D laser welding technique from our track record. A flow compensation (FC) algorithm was introduced to improve the accuracy of the sensor in the presence of static load. The proposed system can reliably measure external forces up to 9.5 N with high repeatability. The system allows discriminating between different levels of force which are typically associated with increasing patient discomfort in colonoscopy: low (0-4 N), medium (4-6 N), and high (>6 N). This system achieves over 80% accuracy in comparison to the ground truth under steady state conditions (P ≤ 0.05) and maintains over 68% accuracy in dynamic scenarios.

In minimally invasive endovascular procedures, contrast-enhanced angiography remains the most robust imaging technique, but exposes patients and surgeons to prolonged radiation. Alternatives such as ultrasound are difficult to interpret, are highly prone to artifacts and noise, and vary in quality, depending on the experience of the interventional radiologist and machine settings. In this work, we seek to address both problems by introducing a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images, without demanding any labeled data. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism, and is capable of learning feature changes across time and space. To facilitate training, we used synthetic ultrasound data based on physics-driven catheter insertion simulations, and translated the data into a unique CT-Ultrasound common domain, CACTUSS, to improve the segmentation performance. We generated ground truth segmentation masks by computing the optical flow between adjacent frames using FlowNet2, and performed thresholding to obtain a binary mask estimate. Finally, we validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms, thus demonstrating its potential for applications to clinical data in the future.

Cochlear implants (CIs) can improve hearing in patients suffering from sensorineural hearing loss via an electrode array (EA) carefully inserted in the scala tympani. Current EAs can cause trauma during insertion, threatening hearing preservation; hence we proposed a pre-curved thermally drawn EA that curls into the cochlea under the influence of body temperature. However, the additional surgical skill required to insert pre-curved EAs usually produces worse surgical outcomes. Medical robots can offer an effective solution to assist surgeons in improving surgical outcomes and reducing outliers. This work proposes a collaborative approach to insert our EA where manageable tasks are automated using a vision-based system. The insertion strategy presented allowed us to insert our EA successfully. The feasibility study showed that we can insert EAs following the defined control strategy while keeping the exerted contact forces within safe levels. The teleoperated robotic system and robotic vision approach to control a self-shaping CI has thus shown potential to provide the tools for a more delicate and atraumatic approach.

In this article we investigate the discrete-time model based control of a planar soft continuum manipulator with proprioceptive sensing provided by fiber Bragg gratings. A control algorithm is designed with a discrete-time energy shaping approach which is extended to account for control-related lag of digital nature. A discrete-time nonlinear observer is employed to estimate the uncertain bending stiffness of the manipulator and to compensate constant matched disturbances. Simulations and experiments demonstrate the effectiveness of the controller compared to a continuous time implementation.

In this article, we investigate the model based position control of soft hydraulic actuators arranged in an an-tagonistic pair. A dynamical model of the system is constructed by employing the port-Hamiltonian formulation. A control algorithm is designed with an energy shaping approach, which accounts for the pressure dynamics of the fluid. A nonlinear observer is included to compensate the effect of unknown external forces. Simulations demonstrate the effectiveness of the proposed approach, and experiments achieve positioning accuracy of 0.043 mm with a standard deviation of 0.033 mm in the presence of constant external forces up to 1 N.

Augmented reality (AR) has the potential to improve the immersion and efficiency of computer-assisted orthopaedic surgery (CAOS) by allowing surgeons to maintain focus on the operating site rather than external displays in the operating theatre. Successful deployment of AR to CAOS requires a calibration that can accurately calculate the spatial relationship between real and holographic objects. Several studies attempt this calibration through manual alignment or with additional fiducial markers in the surgical scene. We propose a calibration system that offers a direct method for the calibration of AR head-mounted displays (HMDs) with CAOS systems, by using infrared-reflective marker-arrays widely used in CAOS. In our fast, user-agnostic setup, a HoloLens 2 detected the pose of marker arrays using infrared response and time-of-flight depth obtained through sensors onboard the HMD. Registration with a commercially available CAOS system was achieved when an IR marker-array was visible to both devices. Study tests found relative-tracking mean errors of 2.03 mm and 1.12° when calculating the relative pose between two static marker-arrays at short ranges. When using the calibration result to provide in-situ holographic guidance for a simulated wire- insertion task, a pre-clinical test reported mean errors of 2.07 mm and 1.54° when compared to a pre-planned trajectory.

This paper presents a new 6 DOF soft robotic manipulator intended for colorectal surgery. The manipulator, based on a novel design that employs an inextensible tube to limit axial extension, is shown to maximize the force exerted at its tip and the bending angle, the latter being measured with a soft sensing skin. Manufacturing of the prototype is achieved with a lost-wax silicone-casting technique. The kinematic model of the manipulator, its workspace, and its manipulability are discussed. The prototype is evaluated with extensive experiments, including pressure-deflection measurement with and without tip load, and lateral force measurements with and without the soft sensing skin to assess hysteresis. The experimental results indicate that the prototype fulfils the key design requirements for colorectal surgery: (i) it can generate sufficient force to perform a range of laparoscopic tasks; (ii) the workspace is commensurate with the dimensions of the large intestine; (iii) the soft sensing skin only results in a marginal reduction of the maximum tip rotation within the range of pressures and external loads relevant for the chosen application.

Visual augmented reality (AR) has the potential to improve the accuracy, efficiency and reproducibility of computer-assisted orthopaedic surgery (CAOS). AR Head-mounted displays (HMDs) further allow non-eye-shift target observation and egocentric view. Recently, a markerless tracking and registration (MTR) algorithm was proposed to avoid the artificial markers that are conventionally pinned into the target anatomy for tracking, as their use prolongs surgical workflow, introduces human-induced errors, and necessitates additional surgical invasion in patients. However, such an MTR-based method has neither been explored for surgical applications nor integrated into current AR HMDs, making the ergonomic HMD-based markerless AR CAOS navigation hard to achieve. To these aims, we present a versatile, device-agnostic and accurate HMD-based AR platform. Our software platform, supporting both video see-through (VST) and optical see-through (OST) modes, integrates two proposed fast calibration procedures using a specially designed calibration tool. According to the camera-based evaluation, our AR platform achieves a display error of 6.31$\pm$2.55 arcmin for VST and 7.72$\pm$3.73 arcmin for OST. A proof-of-concept markerless surgical navigation system to assist in femoral bone drilling was then developed based on the platform and Microsoft HoloLens 1. According to the user study, both VST and OST markerless navigation systems are reliable, with the OST system providing the best usability. The measured navigation error is 4.90$\pm$1.04 mm, 5.96$\pm$2.22$^\circ$for the VST system, and 4.36$\pm$0.80 mm, 5.65$\pm$1.42$^\circ$for the OST system.

Steerable needles are a promising technology to provide safe deployment of tools through complex anatomy in minimally invasive surgery, including tumor-related diagnoses and therapies. For the 3-D localization of these instruments in soft tissue, fiber Bragg gratings (FBGs) based reconstruction methods have gained in popularity because of the inherent advantages of optical fibers in a clinical setting, such as flexibility, immunity to electromagnetic interference, nontoxicity, and the absence of line-of-sight issues. However, methods proposed thus far focus on shape reconstruction of the steerable needle itself, where accuracy is susceptible to errors in interpolation and curve fitting methods used to estimate the curvature vectors along the needle. In this study, we propose reconstructing the shape of the path created by the steerable needle tip based on the follow-the-leader nature of many of its variants. By assuming that the path made by the tip is equivalent to the shape of the needle, this novel approach paves the way for shape reconstruction through a single set of FBGs at the needle tip, which provides curvature information about every section of the path during navigation. We propose a Kalman-filter-based sensor fusion method to update the curvature information about the sections as they are continually estimated during the insertion process. The proposed method is validated through simulation,in vitroandex vivoexperiments employing a programmable bevel-tip steerable needle (PBN). The results show clinically acceptable accuracy, with 2.87-mm mean PBN tip position error, and a standard deviation of 1.63 mm for a 120-mm 3-D insertion.

The inaccessibility of user-perceived reality remains an open issue in pursuing the accurate calibration of optical see-through (OST) head-mounted displays (HMDs). Manual user alignment is usually required to collect a set of virtual-to-real correspondences, so that a default or an offline display calibration can be updated to account for the user’s eye position(s). Current alignment-based calibration procedures usually require point-wise alignments between rendered image point(s) and associated physical landmark(s) of a target calibration tool. As each alignment can only provide one or a few correspondences, repeated alignments are required to ensure calibration quality. This work presents an accurate and tool-less online OST calibration method to update an offline-calibrated eye-display model. The user’s bare hand is markerlessly tracked by a commercial RGBD camera anchored to the OST headset to generate a user-specific cursor for correspondence collection. The required alignment is object-wise, and can provide thousands of unordered corresponding points in tracked space. The collected correspondences are registered by a proposed rotation-constrained iterative closest point (rcICP) method to optimise the viewpoint-related calibration parameters. We implemented such a method for the Microsoft HoloLens 1. The resiliency of the proposed procedure to noisy data was evaluated through simulated tests and real experiments performed with an eye-replacement camera. According to the simulation test, the rcICP registration is robust against possible user-induced rotational misalignment. With a single alignment, our method achieves 8.81 arcmin (1.37 mm) positional error and 1. 76° rotational error by camera-based tests in the arm-reach distance, and 10.79 arcmin (7.71 pixels) reprojection error by user tests.

We present an interactive robotic platform for teleoperated grasping as an educational tool. With this open-source robot application, we engage children and young adults with robotics and make computer science education more vivid. Our teleoperation method uses the Leap Motion optical gesture tracker to simultaneously control each of the four degrees-of-freedom (DOF) of a robotic hand and the six-DOF tool pose of a serial manipulator. A control algorithm is developed to relate the operator’s palm pose to the manipulator’s tool pose. The operator commands the robotic hand with relative finger movements of the thumb, index, and middle finger. We present preliminary results from a pick-and-place demonstration show-cased at a public science fair held at Imperial College London.

Robotic minimally invasive surgery has been a subject of intense research and development over the last three decades, due to the clinical advantages it holds for patients and doctors alike. Particularly for drug delivery mechanisms, higher precision and the ability to follow complex trajectories in three dimensions (3D), has led to interest in flexible, steerable needles such as the programmable bevel-tip needle (PBN). Steering in 3D, however, holds practical challenges for surgeons, as interfaces are traditionally designed for straight line paths. This work presents a pilot study undertaken to evaluate a novel human-machine visual interface for the steering of a robotic PBN, where both qualitative evaluation of the interface and quantitative evaluation of the performance of the subjects in following a 3D path are measured. A series of needle insertions are performed in phantom tissue (gelatin) by the experiment subjects. User could adequately use the system with little training and low workload, and reach the target point at the end of the path with millimeter range accuracy.

We present a model for the steering of programmable bevel-tip needles, along with a set of experiments demonstrating the three-dimensional steering performance of a new, clinically viable, 4-segment, preproduction prototype. A multibeam approach based on Euler-Bernoulli beam theory is used to model the novel multisegment design of these needles. Finite element (FE) simulations for known loads are used to validate the multibeam deflection model. A clinically sized (2.5 mm outer diameter), 4-segment programmable bevel-tip needle, manufactured by extrusion of a medical-grade polymer, is used to conduct an extensive set of experimental trials to evaluate the steering model. For the first time, we demonstrate the ability of the 4-segment needle design to steer in any direction with a maximum achievable curvature of (0.0192 ± 0.0014 mm-1). FE simulations confirm that the multibeam approach produces a good model fit for tip deflections, with a root-mean-square deviation (RMSD) in modeled tip deflection of 0.2636 mm. We perform a parameter optimization to produce a best-fit steering model for experimental trials with a RMSD in curvature prediction of 1.12 × 10-3mm-1.

In this paper, an automatic planner for minimally invasive neurosurgery is presented. The solution provides the neurosurgeon with the best path to connect a user-defined entry point with a target in accordance with a specific cost function. The approach guarantees the avoidance of obstacles which can be found along the insertion pathway. The method is tailored to the EDEN2020* programmable bevel-tip needle, a multisegment steerable probe intended to be used to perform drug delivery for the treatment of glioblastomas. A sample-based heuristic search inspired by the BIT* algorithm is used to define the asymptotically-optimal solution in terms of path length, followed by a smoothing phase to meet the required kinematic constraints of the needle. To account for inaccuracies in catheter modeling, which could determine unexpected control errors over the insertion procedure, an uncertainty margin is defined in order to increase the algorithm's safety. The feasibility of the proposed solution was demonstrated by testing the method in simulated neurosurgical scenarios with different degrees of obstacle occupancy and against other sample-based algorithms present in literature: RRT, RRT* and an enhanced version of the RRT-Connect.

During percutaneous interventions in the brain, puncturing a vessel can cause life threatening complications. To avoid such a risk, current research has been directed towards the development of steerable needles. However, there is a risk that vessels of a size which is close to or smaller than the resolution of commonly used preoperative imaging modalities (0.59 × 0.59 × 1 mm) would not be detected during procedure planning, with a consequent increase in risk to the patient. In this work, we present a novel ensemble of forward looking sensors based on laser Doppler flowmetry, which are embedded within a biologically inspired steerable needle to enable vessel detection during the insertion process. Four Doppler signals are used to classify the pose of a vessel in front of the advancing needle with a high degree of accuracy (2° and 0.1 mm RMS errors), where relative measurements between sensors are used to correct for ambiguity. By using a robotic assisted needle insertion process, and thus a precisely controlled insertion speed, we also demonstrate how the setup can be used to discriminate between tissue bulk motion and vessel motion. In doing so, we describe a sensing apparatus applicable to a variety of needle steering systems, with the potential to eliminate the risk of hemorrhage during percutaneous procedures.

There are many applications and tasks in which the precise, high-fidelity haptic display of deforming objects is required. A crucial element in haptic rendering is the definition of a proxy pose that follows the motion of the user, while respecting the geometry of the object being displayed. Conventional methods for computing the dynamics of a proxy interacting with a deforming object suffer from several issues relating to numerical instabilities when the proxy becomes over-constrained and high computational demands. This paper presents a novel hybrid proxy that combines modified versions of constraint-based and penalty-based proxies together to give high fidelity rendering with reduced computational requirements and enhanced robustness to situations where the proxy becomes enclosed. Experimental analysis of the proposed method shows that it can efficiently compute proxy dynamics that faithfully render the required object. This research forms a basis for further development of novel hybrid dynamic proxies for haptics and allows for increasingly complex deforming geometries to be rendered.

In hands-on robotic surgery, the surgical tool is mounted on the end-effector of a robot and is directly manipulated by the surgeon. This simultaneously exploits the strengths of both humans and robots, such that the surgeon directly feels tool–tissue interactions and remains in control of the procedure, while taking advantage of the robot's higher precision and accuracy. A crucial challenge in hands-on robotics for delicate manipulation tasks, such as surgery, is that the user must interact with the dynamics of the robot at the end-effector, which can reduce dexterity and increase fatigue. This paper presents a null-space-based optimization technique for simultaneously minimizing the mass and friction of the robot that is experienced by the surgeon. By defining a novel optimization technique for minimizing the projection of the joint friction onto the end-effector, and integrating this with our previous techniques for minimizing the belted mass/inertia as perceived by the hand, a significant reduction in dynamics felt by the user is achieved. Experimental analyses in both simulation and human user trials demonstrate that the presented method can reduce the user-experienced dynamic mass and friction by, on average, 44% and 41%, respectively. The results presented robustly demonstrate that optimizing a robots pose can result in a more natural tool motion, potentially allowing future surgical robots to operate with increased usability, improved surgical outcomes, and wider clinical uptake.

Percutaneous intervention is a commonly used surgical procedure for many diagnostic and therapeutic operations. Target motion in soft tissue during an intervention caused by tissue deformation is a common problem, along with needle displacement. In this work, we present a deformation planner that generates continuous curvature paths with a bounded curvature derivative that can be used on-line to reach a moving target. This planner is computationally inexpensive and can be used for any robotic system, which has finite angular velocity, to reach a mobile target. The deformation planner, is integrated into a needle steering system using a novel, biologically inspired needle, STING, to track a simulated moving target. In-vitro results in gelatin demonstrate accurate 2D tracking of a moving target (mean 0.27 mm end positional error and 0.80° approach angle error) over 3 target movement rates.

Physical human–robot interaction is fundamental to exploiting the capabilities of robots in tasks and environments where robots have limited cognition or comprehension and is virtually ubiquitous for robotic manipulation in highly unstructured environments, as are found in surgery. A critical aspect of physical human–robot interaction in these cases is controlling the robot so that the individual human and robot competencies are maximized, while guaranteeing user, task, and environment safety. Dissipative control precludes dangerous forcing of a shared tool by the robot, ensuring safety; however, it typically suffers from poor control fidelity, resulting in reduced task accuracy. In this study, a novel, rigorously formalized,$n$-dimensional dissipative control strategy is proposed that employs a new technique called “energy redirection” to generate control forces with increased fidelity while remaining dissipative and safe. Experimental validation of the method, for complete pose control, shows that it achieves a 90% reduction in task error compared with the current state of the art in dissipative control for the tested applications. The findings clearly demonstrate that the method significantly increases the fidelity and efficacy of dissipative control during physical human–robot interaction. This advancement expands the number of tasks and environments into which safe physical human–robot interaction can be employed effectively.

Active constraints and virtual fixtures are popular control strategies used within human-robot collaborative manipulation tasks, particularly in the field of robot-assisted surgery. Recent research has shown how active constraints, which robotically regulate the motion of a tool that is primarily manipulated by a human, can be implemented in dynamic environments which change and deform throughout a procedure. In a dynamic environment, movement of the constraint boundary can cause active forcing of the surgical tools, potentially reducing the surgeon's control and jeopardising patient safety. Dynamic frictional constraints have been proposed as a method for enforcing dynamic active constraints which do not generate energy of their own, and simply dissipate or redirect the energy of the surgeon to provide assistance. In this paper, dynamic frictional constraints are reformulated to allow formal proof that they are indeed dissipative, and hence also passive. This new formulation is then extended such that dynamic frictional constraints can simultaneously constrain the position and orientation of a tool. Experimental results show that the method is of significant benefit in performing a dynamic task when compared to cases without any assistance; with position and orientation constraints individually and with a conventional frictional constraint without energy redirection.

In hands-on robotic surgery, the surgeon controls the motion of a tool mounted on the end effector by applying forces directly to the robot. The mass and inertia properties of the robot at the end effector thus contribute to the ability of the surgeon to move the tool and consequently, the performance of the surgery. As redundant robots have varying mass/inertia properties for different configurations at the same position and orientation of the end effector, we present optimizations which affect the inertial properties to improve the surgeon's movement capabilities. A method for optimizing the overall belted mass/inertia ellipsoids based on the determinant of the inverse pseudo kinetic energy matrices is presented, along with a method for optimizing the effective mass/inertia in a particular direction. Using a gradient based controller operating in the null-space of the end effector position and orientation, the measures are optimized to the local optima without affecting the surgeon's desired tool pose. Through simulation, the efficacy of the method is demonstrated and a comparison with two standard approaches to redundancy resolution is performed. Lastly, a pre-optimized solution is shown to be effective for heavily constrained environments which prevent active optimization.

Active constraints, also known as virtual fixtures, are high-level control algorithms which can be used to assist a human in man-machine collaborative manipulation tasks. The active constraint controller monitors the robotic manipulator with respect to the environment and task, and anisotropically regulates the motion to provide assistance. The type of assistance offered by active constraints can vary, but they are typically used to either guide the user along a task-specific pathway or limit the user to within a “safe” region. There are several diverse methods described within the literature for applying active constraints, and these are surveyed within this paper. The active constraint research is described and compared using a simple generalized framework, which consists of three primary processes: 1) constraint definition, 2) constraint evaluation, and 3) constraint enforcement. All relevant research approaches for each of these processes, found using search terms associated to “virtual fixture,” “active constraint” and “motion constraint,” are presented.

Collaborative, as opposed to autonomous, control strategies are used within the majority of commercially available, surgical robotic systems. Amongst these, active constraints and virtual fixtures, where assistance is in the form of regulation applied to the motion of surgical tools, offer an effective means to maximise both user and robot capabilities. Conventional active constraint approaches, however, are likely to result in active forcing of the tools when used within a dynamically changing surgical environment. It is posited that such behaviour inherently reduces a surgeon's control over the procedure, and therefore compromises patient safety and clinical acceptance. Utilising a friction model to enforce constraints ensures that energy is never introduced into the system; however frictional constraints suffer from problems once penetration of a constrained region has occurred. A frictional constraint formulation is proposed which eliminates this by redirecting a user's motion, guiding him towards the surface. Experimental validation shows that the proposed constraint significantly improves a user's path-following performance over unassisted cases, while approaching the performance benchmark of a viscoelastic active constraint.

Percutaneous intervention has become a topic of interest in recent years, due to the many potential advantages for the patient. To date, several novel needle steering systems have been developed to improve both the accuracy and applicability of this type of surgery, but many of these can still only provide limited control of the trajectory between an entry site and a deep seated target. Our previous work describes the first prototype of a bio-inspired multi-part needle, codenamed STING, which can steer along planar trajectories within a compliant medium by means of a novel programmable bevel, where the steering angle is a function of the offset between interlocked needle segments. This paper presents our first attempt to model a bio-inspired 4-part needle, an extension of the planar steering system with the potential to steer along three-dimensional (3D) trajectories within a compliant medium. This paper introduces a 3D kinematic model and closed-loop controller for the needle, which is inspired by the modeling strategy employed for under-actuated underwater vehicles, followed by simulation results which demonstrate that 3D trajectory tracking can be completed successfully.

Toward the goal of developing a hands-on robotic surgery control strategy which simultaneously utilizes the various strengths of both the surgeon and robot, we present a dynamic active constraint approach tailored for hands-on surgery. Forbidden region active constraints are used to prevent motion into areas which have been deemed dangerous by the surgeon, helping to overcome some of the disadvantages of fully active systems such as loss of tactile feedback, limited workspace, and limited field-of-view. The computer graphics technique of metaballs is used to represent point cloud data from an imaging system with an analytical, differentiable surface and a dynamics-based controller is proposed which controls the robot to lie on the zero set of the generated time-varying implicit function for which the motion is either known or unknown. This controller has been incorporated into a recursive null-space approach to allow for unimpeded motion along the surface and for further extension to joint optimization in the future. This methodology is demonstrated in simulation and on a lightweight, seven-degree-of-freedom serial manipulator.

Safety is an important factor for human-machine interface devices. Brake actuated devices are potentially safer than those that rely on motors for force-feedback generation. However, manipulators using conventional frictional brakes do have limitations. This paper presents the experimental evaluation of a newly developed four-state rotary programmable brake in a 2DOF manipulator. The experimental results show improved performance compared to the results obtained when manipulators with conventional frictional brakes are used.

This paper outlines work on the development of a novel programmable rotary brake which can restrict motion of a mechanism moving in one direction whilst allowing free motion in other directions. The design, implementation and performance of a fully functional prototype are described along the work on incorporating the prototype into a 3 Degrees-Of-Freedom (DOF) haptic device. The ability of the haptic device to constrain the motion of the end-effector to point-constraint was investigated and the experiments have shown that the haptic device can implement virtual constraints without the need for a force sensor. The experiments also show that when an advanced control scheme is used the virtual wall is not felt as “sticky”.

Percutaneous intervention has attracted significant interest in recent years, but many of today's needles and catheters can only provide limited control of the trajectory between an entry site and soft tissue target. In order to address this fundamental shortcoming in minimally invasive surgery, we describe the first prototype of a bioinspired multipart probe that can steer along planar trajectories within a compliant medium by means of a novel “programmable bevel,” where the steering angle becomes a function of the offset between interlocked probe segments. A kinematic model of the flexible probe and programmable bevel arrangement is derived. Several parameters of the kinematic model are then calibrated experimentally with a fully functional scaled-up prototype, which is 12 mm in diameter. A closed-loop control strategy with feed-forward and feedback components is then derived and implemented in vitro using an approximate linearization strategy that was first developed for car-like robots. Experimental results demonstrate satisfactory 2-D trajectory following of the prototype (0.68 mm tracking error, with 1.45 mm standard deviation) using an electromagnetic position sensor that is embedded at the tip of the probe.

Percutaneous interventions have attracted significant interest in recent years, but most approaches still rely on straight line trajectories between an entry site and a soft tissue target. Thus, to this day, a flexible probe able to bend along predefined curvilinear trajectories within a highly compliant medium without buckling is still an open research challenge. In this paper, we describe the concept of a “programmable bevel” tip, which is inspired by the ovipositor of certain wasps: the offset between two parts of a probe determines the steering direction of the tip thanks to a set of bevels included at the tip of each segment. A kinematic model of the flexible probe and programmable bevel arrangement is derived. Several parameters of the kinematic model are calibrated experimentally using our first prototype of the flexible probe, codenamed STING. Open- (feed-forward) and closed-loop (feedback) control strategies are then derived and implemented in simulation using the chained form representation, originally developed to control car-like robots. Simulated results demonstrate accurate two-dimensional needle steering in the presence of velocity, position, and initial posture disturbances.

The concepts and benefits of hands-on robotic surgery and active-constraint robotics are introduced. The argument is made for systems to be cost effective and simple in order that they can be justified for a large range of surgical procedures. The case is made for robotic systems to have a clear justification, with benefits compared to those from cheaper navigation systems. The need to have robust systems, that require little surgical training and no technical presence in the operating room, is also discussed. An active constraint medical robot, the Acrobot System, is described together with its use in a prospective randomized controlled trial of unicondylar knee arthroplasty (UKA), comparing the performance of the Acrobot System with conventional surgery. Twenty-eight patients awaiting UKA were randomly allocated to have the operation performed conventionally or with the assistance of the Acrobot. The results of the trial are presented together with a discussion of the need for measures of accuracy to be introduced so that the efficacy of the robotic surgery can be immediately identified, rather than having to wait for a number of years before long-term clinical improvements can be demonstrated.

A "hands-on" robotic system for total knee replacement (TKR) surgery is presented. A computer tomography-based preoperative planning software is used to accurately plan the procedure. Intraoperatively, the surgeon guides a small special-purpose robot, called Acrobot, which is mounted on a gross positioning device. The Acrobot uses active constraint control, which constrains the motion to a predefined region, and thus allows the surgeon to safely cut the knee bones to fit a TKR prosthesis with high precision. A noninvasive anatomical registration method is described. The system has been successfully used in seven clinical trials with encouraging results.