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 Colonoscopy is limited by the outdated design of conventional endoscopes, which are associated with high complexity of use, cost and pain.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.

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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.

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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.

Magnetically actuated endoscopes are currently transitioning in to clinical use for procedures such as colonoscopy, presenting numerous benefits over their conventional counterparts. Intelligent and easy-to-use control strategies are an essential part of their clinical effectiveness due to the un-intuitive nature of magnetic field interaction. However, work on developing intelligent control for these devices has mainly been focused on general purpose endoscope navigation. In this work, we investigate the use of autonomous robotic control for magnetic colonoscope intervention via biopsy, another major component of clinical viability. We have developed control strategies with varying levels of robotic autonomy, including semi-autonomous routines for identifying and performing targeted biopsy, as well as random quadrant biopsy. We present and compare the performance of these approaches to magnetic endoscope biopsy against the use of a standard flexible endoscope on benchtop using a colonoscopy training simulator and silicone colon model. The semi-autonomous routines for targeted and random quadrant biopsy were shown to reduce user workload with comparable times to using a standard flexible endoscope.