Model Based Analysis of Shimmy in a Racing Bicycle

In this paper we are presenting a model of a racing bicycle, developed in Modelica language within the Dymola environment. The main purpose is to investigate the dynamic response of the bicycle and its modes of vibration, referring in particular to shimmy. This phenomenon occurs at high speeds and consists of sudden oscillations of the front assembly around the steering axis. Lateral accelerations on the horizontal tube of the frame can reach 5-10 g with a frequency that varies between 5-10 Hz. Even if it is quite uncommon, shimmy is a topic of great relevance, because it may be extremely dangerous for the rider. Thanks to this model, we can show that the main elements which contribute to the rise of the oscillations are the lateral compliance of the frame and the tyres’ deformation

Autonomous Tissue Retraction in Robotic Assisted Minimally Invasive Surgery – A Feasibility Study

In this letter, we describe a novel framework for planning and executing semi-autonomous tissue retraction in minimally invasive robotic surgery. The approach is aimed at removing tissue flaps or connective tissue from the surgical area autonomously, thus exposing the underlying anatomical structures. First, a deep neural network is used to analyse the endoscopic image and detect candidate tissue flaps obstructing the surgical field. A procedural algorithm for planning and executing the retraction gesture is then developed from extended discussions with clinicians. Experimental validation, carried out on a DaVinci Research Kit, shows an average 25% increase of the visible background after retraction. Another significant contribution of this letter is a dataset containing 1,080 labelled surgical stereo images and the associated depth maps, representing tissue flaps in different scenarios. The work described in this letter is a fundamental step towards the autonomous execution of tissue retraction, and the first example of simultaneous use of deep learning and procedural algorithms. The same framework could be applied to a wide range of autonomous tasks, such as debridement and placement of laparoscopic clips

An Open Source Motion Planning Framework for Autonomous Minimally Invasive Surgical Robots

Planning and execution of autonomous tasks in minimally invasive surgical robotic are significantly more complex with respect to generic manipulators. Narrow abdominal cavities and limited entry points restrain the use of external vision systems and specialized kinematics prevent the straightforward use of standard planning algorithms. In this work, we present a novel implementation of a motion planning framework for minimally invasive surgical robots, composed of two subsystems: An arm-camera registration method only requiring the endoscopic camera and a graspable device, compatible with a 12mm trocar port, and a specialized trajectory planning algorithm, designed to generate smooth, non straight trajectories. The approach is tested on a DaVinci Research Kit obtaining an accuracy of 2.71±0.89 cm in the arm-camera registration and of 1.30±0.39 cm during trajectory execution. The code is organised into STORM Motion Library (STOR-MoLib), an open source library, publicly available for the research community

Toward Autonomous Robotic Colonoscopy: Motion Strategies for Magnetic Capsule Navigation

In this paper, a set of techniques aimed at autonomously navigating a tethered magnetic capsule for endoluminal inspection of the large bowel is presented. The manual navigation of magnetic capsules for colonoscopy can exhibit several challenges if the full control of the capsule pose is left to the clinician. Tight bends, tissue folds and large diverticula can obstruct the motion of the capsule, yielding the control system to apply greater forces and torques, with no substantial effect. For this reason, a supervisory system, capable of influencing the capsule motion to avoid obstruction and to overcome obstacles is here described. The adopted approach is based on the 'surfing' principle, where the capsule is navigated in such a way to slide on the tissue folds rather than against them. The proposed technique has been validated by means of experiments in a colon phantom, experiments have shown that the adoption of this approach allows the navigation of 350mm in the phantom, while a fully-manual teleoperation of the capsule only reaches a depth of 75mm

Active Stabilization of Interventional Tasks Utilizing a Magnetically Manipulated Endoscope

Magnetically actuated robots have become increasingly popular in medical endoscopy over the past decade. Despite the significant improvements in autonomy and control methods, progress within the field of medical magnetic endoscopes has mainly been in the domain of enhanced navigation. Interventional tasks such as biopsy, polyp removal, and clip placement are a major procedural component of endoscopy. Little advancement has been done in this area due to the problem of adequately controlling and stabilizing magnetically actuated endoscopes for interventional tasks. In the present paper we discuss a novel model-based Linear Parameter Varying (LPV) control approach to provide stability during interventional maneuvers. This method linearizes the non-linear dynamic interaction between the external actuation system and the endoscope in a set of equilibria, associated to different distances between the magnetic source and the endoscope, and computes different controllers for each equilibrium. This approach provides the global stability of the overall system and robustness against external disturbances. The performance of the LPV approach is compared to an intelligent teleoperation control method (based on a Proportional Integral Derivative (PID) controller), on the Magnetic Flexible Endoscope (MFE) platform. Four biopsies in different regions of the colon and at two different system equilibria are performed. Both controllers are asked to stabilize the endoscope in the presence of external disturbances (i.e. the introduction of the biopsy forceps through the working channel of the endoscope). The experiments, performed in a benchtop colon simulator, show a maximum reduction of the mean orientation error of the endoscope of 45.8% with the LPV control compared to the PID controller

Independent Control of Multiple Degrees of Freedom Local Magnetic Actuators with Magnetic Cross-coupling Compensation

This letter tackles the problem of independent control of multiple degrees of freedom (DoF) systems based on local magnetic actuation (LMA). This is achieved by means of a modular disturbance rejection scheme, with the aim of enhancing the range of use of multiple-DoF LMAs in dexterous surgical manipulators. An LMA actuation unit consists of a pair of permanent magnets, characterized by diametrical magnetization, acting as magnetic gears across the abdominal wall. In this study, the model of the LMA and the time-varying magnetic disturbances owing to the proximity of multiple units are discussed. Subsequently, the developed model is capitalized in order to establish a Kalman state observer for the purpose of developing a sensor-free endoscopic manipulator suited to infer the state of the internal side of the LMA. Afterwards, the same model is used to develop an adaptive feedforward compensator system, with the aim of balancing the magnetic torques acting on the LMAs from the neighboring units in the case of unknown and frequency-varying sinusoidal disturbances. The effect of a magnetic shield, realized by means of MuMetal is also analyzed. The overall control system is modular with respect to the number of units and requires no centralized intelligence. The proposed scheme is subsequently validated by means of experiments performed on a benchtop platform, showing the effectiveness of the proposed approach. In particular, the proposed state observer presents a root mean square error (RMSE) ranging from 28 to 47 rpmin the estimation of the rotational velocity of the internal magnet and an RMSE of 1.18 to 1.41 mNm in the estimation of a load torque. The disturbance compensation system provides a reduction of 40% to 50% in the disturbance caused by interacting LMA units

Explicit Model Predictive Control of a Magnetic Flexible Endoscope

In this letter, explicit model predictive control is applied in conjunction with nonlinear optimization 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

Towards Autonomous Robotic Minimally Invasive Ultrasound Scanning and Vessel Reconstruction on Non-Planar Surfaces

Autonomous robotic Ultrasound (US) scanning has been the subject of research for more than 2 decades. However, little work has been done to apply this concept into a minimally invasive setting, in which accurate force sensing is generally not available and robot kinematics are unreliable due to the tendon-driven, compliant robot structure. As a result, the adequate orientation of the probe towards the tissue surface remains unknown and the anatomy reconstructed from scan may become highly inaccurate. In this work we present solutions to both of these challenges: an attitude sensor fusion scheme for improved kinematic sensing and a visual, deep learning based algorithm to establish and maintain contact between the organ surface and the US probe. We further introduce a novel scheme to estimate and orient the probe perpendicular to the center line of a vascular structure. Our approach enables, for the first time, to autonomously scan across a non-planar surface and navigate along an anatomical structure with a robotically guided minimally invasive US probe. Our experiments on a vessel phantom with a convex surface confirm a significant improvement of the reconstructed curved vessel geometry, with our approach strongly reducing the mean positional error and variance. In the future, our approach could help identify vascular structures more effectively and help pave the way towards semi-autonomous assistance during partial hepatectomy and the potential to reduce procedure length and complication rates

COVID-19 and vertical transmission: assessing the expression of ACE2 / TMPRSS2 in the human fetus and placenta to assess the risk of SARS-CoV-2 infection

Background: While pregnant women have been identified as a potentially at-risk group concerning COVID-19 infection, little is known regarding the susceptibility of the fetus to infection. Co-expression of ACE2 and TMPRSS2 has been identified as a pre-requisite for infection, and expression across different tissues is known to vary between children and adults. However, the expression of these proteins in the fetus is unknown. Methods: We performed a retrospective analysis of single cell data repositories. This data was then validated at both gene and protein level by performing qRT-PCR and two-colour immunohistochemistry on a library of second-trimester human fetal tissues. Findings: TMPRSS2 is present at both gene and protein level in the predominantly epithelial fetal tissues analysed. ACE2 is present at significant levels, only in the fetal intestine and kidney and is not expressed in the fetal lung. The placenta is also negative for the two proteins both during development and at term. Interpretation: This dataset indicates that the lungs are unlikely to be a viable route of SARS-CoV2 fetal infection. The fetal kidney, despite presenting both the proteins required for the infection, is anatomically protected from the exposure to the virus. However, the GI tract is likely to be susceptible to infection due to its high co-expression of both proteins, as well as its exposure to potentially infected amniotic fluid

Magnetic flexible endoscope for colonoscopy: an initial learning curve analysis

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