Early diagnosis of colorectal cancer substantially improves survival. However, over half of cases are diagnosed late due to the demand for colonoscopy-the 'gold standard' for screening-exceeding capacity. Colonoscopy is limited by the outdated design of conventional endoscopes, which are associated with high complexity of use, cost and pain. Magnetic endoscopes are a promising alternative and overcome the drawbacks of pain and cost, but they struggle to reach the translational stage as magnetic manipulation is complex and unintuitive. In this work, we use machine vision to develop intelligent and autonomous control of a magnetic endoscope, enabling non-expert users to effectively perform magnetic colonoscopy in vivo. We combine the use of robotics, computer vision and advanced control to offer an intuitive and effective endoscopic system. Moreover, we define the characteristics required to achieve autonomy in robotic endoscopy. The paradigm described here can be adopted in a variety of applications where navigation in unstructured environments is required, such as catheters, pancreatic endoscopy, bronchoscopy and gastroscopy. This work brings alternative endoscopic technologies closer to the translational stage, increasing the availability of early-stage cancer treatments.

This review examines the dichotomy between automatic and autonomous behaviors in surgical robots, maps the possible levels of autonomy of these robots, and describes the primary enabling technologies that are driving research in this field. It is organized in five main sections that cover increasing levels of autonomy. At level 0, where the bulk of commercial platforms are, the robot has no decision autonomy. At level 1, the robot can provide cognitive and physical assistance to the surgeon, while at level 2, it can autonomously perform a surgical task. Level 3 comes with conditional autonomy, enabling the robot to plan a task and update planning during execution. Finally, robots at level 4 can plan and execute a sequence of surgical tasks autonomously. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 3, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

No abstract

This paper presents a general approach to object-oriented modelling of flexible multibody systems, based on the floating frame of reference (FFR) formulation. The data describing a flexible body can be computed analytically, having defined its shape functions matrices, or calculated by several finite element method (FEM) packages as a result of a modal analysis. By the proposed approach, a modular model is then obtained in an object-oriented language, namely Modelica. This allows to integrate very realistic descriptions of distributed flexibility in multidomain models, with significant advantages for a variety of simulation studies. After describing the general methodology, the paper presents some simulation results, to validate the approach with respect to benchmark cases considered in the literature

SUMMARYIn this paper, a closed-form dynamic model of flexible manipulators is developed, based on the Newton–Euler formulation of motion equations of flexible links and on the adoption of the spatial vector notation. The proposed model accounts for two main innovations with respect to the state of the art: it is obtained in closed form with respect to the joints and modal coordinates (including the quadratic velocity terms) and motion equations of the whole manipulator can be computed for any arbitrary shape of the links and any possible link cardinality starting from the output of several commercial (finite element analysis) FEA codes. The Newton–Euler formulation of motion equations in terms of the joint and elastic variables greatly improves the simulation performances and makes the model suitable for real-time control and active vibration damping. The model has been compared with literature benchmarks obtained by the classical multibody approach and further validated by comparison with experiments collected on an experimental manipulator.

No abstract

Background and study aims Colonoscopy is a technically challenging procedure that requires extensive training to minimize discomfort and avoid trauma due to its drive mechanism. Our academic team developed a magnetic flexible endoscope (MFE) actuated by magnetic coupling under supervisory robotic control to enable a front-pull maneuvering mechanism, with a motion controller user interface, to minimize colon wall stress and potentially reduce the learning curve. We aimed to evaluate this learning curve and understand the user experience. Methods Five novices (no endoscopy experience), five experienced endoscopists, and five experienced MFE users each performed 40 trials on a model colon using 1:1 block randomization between a pediatric colonoscope (PCF) and the MFE. Cecal intubation (CI) success, time to cecum, and user experience (NASA task load index) were measured. Learning curves were determined by the number of trials needed to reach minimum and average proficiency—defined as the slowest average CI time by an experienced user and the average CI time by all experienced users, respectively. Results MFE minimum proficiency was achieved by all five novices (median 3.92 trials) and five experienced endoscopists (median 2.65 trials). MFE average proficiency was achieved by four novices (median 14.21 trials) and four experienced endoscopists (median 7.00 trials). PCF minimum and average proficiency levels were achieved by only one novice. Novices’ perceived workload with the MFE significantly improved after obtaining minimum proficiency. Conclusions The MFE has a short learning curve for users with no prior experience—requiring relatively few attempts to reach proficiency and at a reduced perceived workload.

In this paper, explicit model predictive control is applied in conjunction with nonlinear optimisation to a magnetically actuated flexible endoscope for the first time. The approach is aimed at computing the motion of the external permanent magnet, given the desired forces and torques. The strategy described here takes advantage of the nonlinear nature of the magnetic actuation and explicitly considers the workspace boundaries, as well as the actuation constraints. Initially, a simplified dynamic model of the tethered capsule, based on the Euler-Lagrange equations is developed. Subsequently, the explicit model predictive control is described and a novel approach for the external magnet positioning, based on a single step, nonlinear optimisation routine, is proposed. Finally, the strategy is implemented on the experimental platform, where bench-top trials are performed on a realistic colon phantom, showing the effectiveness of the technique. The work presented here constitutes an initial exploration for model-based control techniques applied to magnetically manipulated payloads, the techniques described here may be applied to a wide range of devices, including flexible endoscopes and wireless capsules. To our knowledge, this is the first example of advanced closed loop control of magnetic capsules.