575 research outputs found

    Characterisation and State Estimation of Magnetic Soft Continuum Robots

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    Minimally invasive surgery has become more popular as it leads to less bleeding, scarring, pain, and shorter recovery time. However, this has come with counter-intuitive devices and steep surgeon learning curves. Magnetically actuated Soft Continuum Robots (SCR) have the potential to replace these devices, providing high dexterity together with the ability to conform to complex environments and safe human interactions without the cognitive burden for the clinician. Despite considerable progress in the past decade in their development, several challenges still plague SCR hindering their full realisation. This thesis aims at improving magnetically actuated SCR by addressing some of these challenges, such as material characterisation and modelling, and sensing feedback and localisation. Material characterisation for SCR is essential for understanding their behaviour and designing effective modelling and simulation strategies. In this work, the material properties of commonly employed materials in magnetically actuated SCR, such as elastic modulus, hyper-elastic model parameters, and magnetic moment were determined. Additionally, the effect these parameters have on modelling and simulating these devices was investigated. Due to the nature of magnetic actuation, localisation is of utmost importance to ensure accurate control and delivery of functionality. As such, two localisation strategies for magnetically actuated SCR were developed, one capable of estimating the full 6 degrees of freedom (DOFs) pose without any prior pose information, and another capable of accurately tracking the full 6-DOFs in real-time with positional errors lower than 4~mm. These will contribute to the development of autonomous navigation and closed-loop control of magnetically actuated SCR

    Development of a Novel Ball-and-Socket Flexible Manipulator for Minimally Invasive Flexible Surgery

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    This work proposes a novel flexible manipulator consisting of a series of 2-DOF vertebrae based on a ball-andsocket joint that is connected by a ball-shaped surface and a cupshaped socket and constrained by pins for circumferential rotation. This manipulator can demonstrate outstanding torsional stiffness since the circumferential rotation between the vertebrae is constrained by four ball pins. The point contact between ball pins and guideways effectively reduces the friction between the vertebrae, thus allowing the designed manipulator to yield a smooth bending shape with constant curvature. This manipulator features high axial and torsional stiffness, excellent bending performance, sufficient loading capacity, and convenient integration with surgical instruments. Moreover, the excellent torsional stiffness enables this manipulator to efficiently transfer torque and be applied in in-situ torsional motion, effectively addressing the typical issue of limited dexterity for torsional motion. The kinematic modeling of the proposed manipulator under in-situ torsional motion has been derived, and its workspace has been analyzed. A robotic system has been assembled, and experiments have verified the proposed design and modeling validity. The results show that the maximum position errors in bending motion are 2.39% (horizontal direction) and 1.98% (vertical direction), and its torsional stiffness is 21.13N∙mm/deg, which is 46 times higher than that of a typical spherical flexible manipulator (SFM). Such merits support this manipulator excellently performing the in-situ torsional motion with a maximum average position error of 3.58%. Furthermore, a phantom test of the larynx has been performed to verify the potential of clinical feasibility

    A review on model-based and model-free approaches to control soft actuators and their potentials in colonoscopy

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    Colorectal cancer (CRC) is the third most common cancer worldwide and responsible for approximately 1 million deaths annually. Early screening is essential to increase the chances of survival, and it can also reduce the cost of treatments for healthcare centres. Colonoscopy is the gold standard for CRC screening and treatment, but it has several drawbacks, including difficulty in manoeuvring the device, patient discomfort, and high cost. Soft endorobots, small and compliant devices thatcan reduce the force exerted on the colonic wall, offer a potential solution to these issues. However, controlling these soft robots is challenging due to their deformable materials and the limitations of mathematical models. In this Review, we discuss model-free and model-based approaches for controlling soft robots that can potentially be applied to endorobots for colonoscopy. We highlight the importance of selecting appropriate control methods based on various parameters, such as sensor and actuator solutions. This review aims to contribute to the development of smart control strategies for soft endorobots that can enhance the effectiveness and safety of robotics in colonoscopy. These strategies can be defined based on the available information about the robot and surrounding environment, control demands, mechanical design impact and characterization data based on calibration.<br/

    A New Index for Detecting and Avoiding Type II Singularities for the Control of Non-Redundant Parallel Robots

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    [ES] Los robots paralelos (PR por sus siglas en inglés) son mecanismos donde el efector final está unido a la base, mediante al menos dos cadenas cinemáticas abiertas. Los PRs ofrecen una gran capacidad de carga y alta precisión, lo que los hace adecuados para diversas aplicaciones, entre ellas la interacción persona-robot. Sin embargo, en las proximidades de una singularidad Tipo II (singularidad dentro del espacio de trabajo), un PR pierde el control sobre los movimientos del efector final. La pérdida de control representa un riesgo importante para los usuarios, especialmente en rehabilitación robótica. En las últimas décadas, los PR se han popularizado en la rehabilitación de miembros inferiores debido al aumento del número de personas que viven con limitaciones físicas. Así, esta tesis trata sobre la detección y evitación de singularidades de Tipo II para asegurar total control de un PR no redundante para la rehabilitación y diagnóstico de rodilla, denominado 3UPS+RPU. En la literatura, existen varios índices para detectar y medir la cercanía a una singularidad basados en métodos analíticos y geométricos. Sin embargo, algunos de estos índices carecen de significado físico y son incapaces de identificar los actuadores responsables de la pérdida de control. Esta tesis aporta dos novedosos índices para detectar y medir la proximidad a una singularidad de Tipo II, capaces de identificar el par de actuadores responsables de la singularidad. Los dos índices son los ángulos entre los componentes lineal (T_i,j) y angular (O_i,j) de dos Twist Screw de Salida (OTS por sus siglas en inglés) normalizados i,j. Una singularidad Tipo II es detectada cuando T_i,j = O_i,j = 0 y su proximidad se mide mediante los mínimos ángulos T_i,j (minT) y O_i,j (minO) para los casos plano y espacial, respectivamente. La eficacia de los índices T_i,j y O_i,j se evalúa de forma teórica y experimental en un robot 3UPS+RPU y un mecanismo de cinco barras. Además, se propone un procedimiento experimental para el adecuado establecimiento del límite de cercanía a una singularidad de Tipo II mediante la aproximación progresiva del PR a una singularidad y la medición de la última posición controlable. Posteriormente, se desarrollan dos nuevos algoritmos deterministas para liberar y evitar una singularidad de Tipo II basados en minT y minO para PR no redundantes. minT y minO se utilizan para identificar los dos actuadores a mover para liberar o evitar el PR de una singularidad. Ambos algoritmos requieren una medición precisa de la pose alcanzada por el efector final. El algoritmo para liberar un PR de una configuración singular se aplica con éxito en un controlador híbrido basado en visión artificial para el PR 3UPS+RPU. El controlador utiliza un sistema de fotogrametría para medir la pose del robot debido a la degeneración del modelo cinemático en las proximidades de una singularidad. El algoritmo de evasión de singularidades Tipo II se aplica a la planificación offline y online de trayectorias no singulares para un mecanismo de cinco barras y el PR 3UPS+RPU. Estas aplicaciones verifican el bajo coste computacional y la mínima desviación introducida en la trayectoria original por los nuevos algoritmos. La implementación directa de un controlador de fuerza/posición en el PR 3UPS+RPU es insegura porque el paciente podría llevar involuntariamente al PR a una singularidad. Por lo tanto, esta tesis concluye presentando un novedoso controlador de fuerza/posición complementado con el algoritmo de evasión de singularidades de Tipo II. El nuevo controlador se evalúa durante rehabilitación activa de una pierna de maniquí y una pierna humana no lesionada. Los resultados muestran que el nuevo controlador combinado mantiene el PR 3UPS+RPU lejos de configuraciones singulares con una desviación mínima de la trayectoria original. Por lo tanto, esta tesis habilita el 3UPS+RPU PR para la rehabilitación segura de miembros inferiores lesionados.[CAT] Els robots paral·lels (PR per les seues sigles en anglés) són mecanismes on l'efector final està unit a la base, mitjançant almenys dues cadenes cinemàtiques obertes. Els PRs ofereixen una gran capacitat de càrrega i alta precisió, la qual cosa els fa adequats per a diverses aplicacions, entre elles la interacció persona-robot. No obstant això, en les proximitats d'una singularitat Tipus II (singularitat dins de l'espai de treball), un PR perd el control sobre els moviments de l'efector final. La pèrdua de control representa un risc important per als usuaris, especialment en rehabilitació robòtica. En les últimes dècades, els PR s'han popularitzat en la rehabilitació de membres inferiors a causa de l'augment del nombre de persones que viuen amb limitacions físiques. Així, aquesta tesi tracta sobre la detecció i evació de singularitats de Tipus II per a assegurar total control d'un PR no redundant per a la rehabilitació i diagnòstic de genoll, denominat 3UPS+RPU. En la literatura, existeixen diversos índexs per a detectar i mesurar la proximitat a una singularitat basats en mètodes analítics i geomètrics. No obstant això, alguns d'aquests índexs manquen de significat físic i són incapaços d'identificar els actuadors responsables de la pèrdua de control. Aquesta tesi aporta dos nous índexs per a detectar i mesurar la proximitat a una singularitat de Tipus II, capaços d'identificar el parell d'actuadors responsables de la singularitat. Els dos índexs són els angles entre els components lineal (T_i,j) i angular (O_i,j) de dues Twist Screw d'Eixida (OTS per les seues sigles en engonals) normalitzats i,j. Una singularitat Tipus II és detectada quan T_i,j = O_i,j = 0 i la seua proximitat es mesura mitjançant els minimos angles T_i,j (minT) i O_i,j (minO) per als casos pla i espacial, respectivament. L'eficàcia dels índexs T_i,j i O_i,j es evalua de manera teòrica i experimental en un robot 3UPS+RPU i un mecanisme de cinc barres. A més, es proposa un procediment experimental per a l'adequat establiment del límit de proximitat a una singularitat de Tipus II mitjançant l'aproximació progressiva del PR a una singularitat i el mesurament de l'última posició controlable. Posteriorment, es desenvolupen dos nous algorismes deterministes per a alliberar i evadir una singularitat de Tipus II basats en minT i minO per a PR no redundants. minT i minO s'utilitzen per a identificar els dos actuadors a moure per a alliberar o evadir el PR d'una singularitat. Aquests algorismes requereixen un mesurament precís de la posa aconseguida per l'efector final. L'algorisme per a alliberar un PR d'una configuració singular s'aplica amb èxit en un controlador híbrid basat en visió artificial per al PR 3UPS+RPU. El controlador utilitza un sistema de fotogrametria per a mesurar la posa del robot a causa de la degeneració del model cinemàtic en les proximitats d'una singularitat. L'algorisme d'evació de singularitats Tipus II s'aplica a la planificació offline i en línia de trajectòries no singulars per a un mecanisme de cinc barres i el PR 3UPS+RPU. Aquestes aplicacions verifiquen el baix cost computacional i la mínima desviació introduïda en la trajectòria original pels nous algorismes. La implementació directa d'un controlador de força/posició en el PR 3UPS+RPU és insegura perquè el pacient podria portar involuntàriament al PR a una singularitat. Per tant, aquesta tesi conclou presentant un nou controlador de força/posició complementat amb l'algorisme d'evació de singularitats de Tipus II. El nou controlador s'avalua durant la rehabilitació activa d'una cama de maniquí i una cama humana no lesionada. Els resultats mostren que el nou controlador combinat manté el PR 3UPS+RPU lluny de configuracions singulars amb una desviació mínima de la trajectòria original. Per tant, aquesta tesi habilita el 3UPS+RPU PR per a la rehabilitació segura dels membres inferiors lesionats.[EN] Parallel Robots (PR)s are mechanisms where the end-effector is linked to the base by at least two open kinematics chains. The PRs offer a high payload and high accuracy, making them suitable for various applications, including human robot interaction. However, in proximity to a Type II singularity (singularity within the workspace), a PR loses control over the movements of the end-effector. The loss of control represents a major risk for users, especially in robotic rehabilitation. In the last decades, PRs have become popular in lower limb rehabilitation because of the increment in the number of people living with physical limitations. Thus, this thesis is about the detection and avoidance of Type II singularities to ensure complete control of a non-redundant PR for knee rehabilitation and diagnosis named 3UPS+RPU. In the literature, several indices exist to detect and measure the closeness to a singular configuration based on analytical and geometrical methods. However, some of these indices have no physical meaning, and they are unable to identify the actuators responsible for the loss of control. This thesis contributes two novel indices to detect and measure the proximity to a Type II singularity capable of identifying the pair of actuators responsible for the singularity. The two indices are the angles between the linear (T_i,j) and the angular (O_i,j) components of two i,j normalised Output Twist Screws (OTSs). A Type II singularity is detected when the angles T_i,j = O_i,j = 0 and its closeness is measured by the minimum T_i,j (minT) and minimum O_i,j (minO) for planar and spatial cases, respectively. The effectiveness of the indices T_i,j and O_i,j is evaluated from a theoretical and experimental perspective in a 3UPS+RPU and a five bars mechanism. Moreover, an experimental procedure is proposed for setting a proper limit of closeness to a Type II singularity by the progressive approach of the PR to singular configuration and measuring the last controllable pose. Subsequently, two novel deterministic algorithms for releasing and avoiding Type II singularities based on minT and minO are developed for non-redundant PRs. The minT and minO are used to identify the two actuators to move for release or prevent the PR from the singularity. Both algorithms require an accurate measuring of the pose reached by the end-effector. The algorithm to release a PR from a singular configuration is successfully applied in a vision-based hybrid controller for the 3UPS+RPU PR. The controller uses a photogrammetry system to measure the pose of the robot due to the degeneration of the kinematic model in the vicinity of a singularity. The Type II singularity avoidance algorithm is applied to offline and online free-singularity trajectory planning for a five-bar mechanism and the 3UPS+RPU PR. These applications verify the low computation cost and the minimum deviation introduced in the original trajectory for both novel algorithms. The direct implementation of a force/position controller in the 3UPS+RPU PR is unsafe because the patient could unintentionally drive the PR to a Type II singularity. Therefore, this thesis concludes by presenting a novel force/position controller complemented with the Type II singularity avoidance algorithm. The complemented controller is evaluated during patient-active exercises in a mannequin leg and an uninjured human limb. The results show that the novel combined controller keeps the 3UPS+RPU PR far from singular configurations with a minimum deviation on the original trajectory. Hence, this thesis enables the 3UPS+RPU PR for the safe rehabilitation of injured lower limbs.Pulloquinga Zapata, JL. (2023). A New Index for Detecting and Avoiding Type II Singularities for the Control of Non-Redundant Parallel Robots [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19427

    Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

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    Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

    Structured machine learning models for robustness against different factors of variability in robot control

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    An important feature of human sensorimotor skill is our ability to learn to reuse them across different environmental contexts, in part due to our understanding of attributes of variability in these environments. This thesis explores how the structure of models used within learning for robot control could similarly help autonomous robots cope with variability, hence achieving skill generalisation. The overarching approach is to develop modular architectures that judiciously combine different forms of inductive bias for learning. In particular, we consider how models and policies should be structured in order to achieve robust behaviour in the face of different factors of variation - in the environment, in objects and in other internal parameters of a policy - with the end goal of more robust, accurate and data-efficient skill acquisition and adaptation. At a high level, variability in skill is determined by variations in constraints presented by the external environment, and in task-specific perturbations that affect the specification of optimal action. A typical example of environmental perturbation would be variation in lighting and illumination, affecting the noise characteristics of perception. An example of task perturbations would be variation in object geometry, mass or friction, and in the specification of costs associated with speed or smoothness of execution. We counteract these factors of variation by exploring three forms of structuring: utilising separate data sets curated according to the relevant factor of variation, building neural network models that incorporate this factorisation into the very structure of the networks, and learning structured loss functions. The thesis is comprised of four projects exploring this theme within robotics planning and prediction tasks. Firstly, in the setting of trajectory prediction in crowded scenes, we explore a modular architecture for learning static and dynamic environmental structure. We show that factorising the prediction problem from the individual representations allows for robust and label efficient forward modelling, and relaxes the need for full model re-training in new environments. This modularity explicitly allows for a more flexible and interpretable adaptation of trajectory prediction models to using pre-trained state of the art models. We show that this results in more efficient motion prediction and allows for performance comparable to the state-of-the-art supervised 2D trajectory prediction. Next, in the domain of contact-rich robotic manipulation, we consider a modular architecture that combines model-free learning from demonstration, in particular dynamic movement primitives (DMP), with modern model-free reinforcement learning (RL), using both on-policy and off-policy approaches. We show that factorising the skill learning problem to skill acquisition and error correction through policy adaptation strategies such as residual learning can help improve the overall performance of policies in the context of contact-rich manipulation. Our empirical evaluation demonstrates how to best do this with DMPs and propose “residual Learning from Demonstration“ (rLfD), a framework that combines DMPs with RL to learn a residual correction policy. Our evaluations, performed both in simulation and on a physical system, suggest that applying residual learning directly in task space and operating on the full pose of the robot can significantly improve the overall performance of DMPs. We show that rLfD offers a gentle to the joints solution that improves the task success and generalisation of DMPs. Last but not least, our study shows that the extracted correction policies can be transferred to different geometries and frictions through few-shot task adaptation. Third, we employ meta learning to learn time-invariant reward functions, wherein both the objectives of a task (i.e., the reward functions) and the policy for performing that task optimally are learnt simultaneously. We propose a novel inverse reinforcement learning (IRL) formulation that allows us to 1) vary the length of execution by learning time-invariant costs, and 2) relax the temporal alignment requirements for learning from demonstration. We apply our method to two different types of cost formulations and evaluate their performance in the context of learning reward functions for simulated placement and peg in hole tasks executed on a 7DoF Kuka IIWA arm. Our results show that our approach enables learning temporally invariant rewards from misaligned demonstration that can also generalise spatially to out of distribution tasks. Finally, we employ our observations to evaluate adversarial robustness in the context of transfer learning from a source trained on CIFAR 100 to a target network trained on CIFAR 10. Specifically, we study the effects of using robust optimisation in the source and target networks. This allows us to identify transfer learning strategies under which adversarial defences are successfully retained, in addition to revealing potential vulnerabilities. We study the extent to which adversarially robust features can preserve their defence properties against black and white-box attacks under three different transfer learning strategies. Our empirical evaluations give insights on how well adversarial robustness under transfer learning can generalise.

    Separable Tendon-Driven Robotic Manipulator with a Long, Flexible, Passive Proximal Section

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    This work tackles practical issues which arise when using a tendon-driven robotic manipulator (TDRM) with a long, flexible, passive proximal section in medical applications. Tendon-driven devices are preferred in medicine for their improved outcomes via minimally invasive procedures, but TDRMs come with unique challenges such as sterilization and reuse, simultaneous control of tendons, hysteresis in the tendon-sheath mechanism, and unmodeled effects of the proximal section shape. A separable TDRM which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. An open-loop redundant controller which resolves the redundancy in the kinematics is developed. Simple linear hysteresis compensation and re-tension compensation based on the physical properties of the device are proposed. The controller and compensation methods are evaluated on a testbed for a straight proximal section, a curved proximal section at various static angles, and a proximal section which dynamically changes angles; and overall, distal tip error was reduced

    Simultaneous Position-and-Stiffness Control of Underactuated Antagonistic Tendon-Driven Continuum Robots

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    Continuum robots have gained widespread popularity due to their inherent compliance and flexibility, particularly their adjustable levels of stiffness for various application scenarios. Despite efforts to dynamic modeling and control synthesis over the past decade, few studies have focused on incorporating stiffness regulation in their feedback control design; however, this is one of the initial motivations to develop continuum robots. This paper aims to address the crucial challenge of controlling both the position and stiffness of a class of highly underactuated continuum robots that are actuated by antagonistic tendons. To this end, the first step involves presenting a high-dimensional rigid-link dynamical model that can analyze the open-loop stiffening of tendon-driven continuum robots. Based on this model, we propose a novel passivity-based position-and-stiffness controller adheres to the non-negative tension constraint. To demonstrate the effectiveness of our approach, we tested the theoretical results on our continuum robot, and the experimental results show the efficacy and precise performance of the proposed methodology

    Model Based Control of Soft Robots: A Survey of the State of the Art and Open Challenges

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    Continuum soft robots are mechanical systems entirely made of continuously deformable elements. This design solution aims to bring robots closer to invertebrate animals and soft appendices of vertebrate animals (e.g., an elephant's trunk, a monkey's tail). This work aims to introduce the control theorist perspective to this novel development in robotics. We aim to remove the barriers to entry into this field by presenting existing results and future challenges using a unified language and within a coherent framework. Indeed, the main difficulty in entering this field is the wide variability of terminology and scientific backgrounds, making it quite hard to acquire a comprehensive view on the topic. Another limiting factor is that it is not obvious where to draw a clear line between the limitations imposed by the technology not being mature yet and the challenges intrinsic to this class of robots. In this work, we argue that the intrinsic effects are the continuum or multi-body dynamics, the presence of a non-negligible elastic potential field, and the variability in sensing and actuation strategies.Comment: 69 pages, 13 figure

    A Novel Continuum Overtube with Improved Triangulation for Flexible Robotic Endoscopy

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    This paper presents a novel continuum overtube that consists of a notched tendon-driven 2-DOF continuum joint and a distal deployable structure driven by a flexible screw rod. A smooth bending shape with constant curvature is achieved by adopting an overlapped area between notches to generate uniform stress distribution. The distal deployable structure provides an extensive triangulation for bimanual operations. The presented design achieves high flexibility, sufficient loading and anti-twisting capacity, and an improved layout of functional channels for flexible robotic endoscopy. Design optimization is performed to optimize structural parameters for performance investigation and improvement. The proposed continuum joint achieves an average distal positioning error of 1.48% and 1.20% within -115∘, 115∘ in the two bending planes with minor hysteresis errors of less than 1.5%, indicating the outstanding constant bending curvature characteristics for kinematic modeling and control. The loading capacity achieves 4.27N and provides significant advantages in terms of sufficient rigidity over the commercial endoscope. The designed deployable structure has significantly improved operational triangulation, which can effectively support bimanual operations with two instruments for complex operations. Meanwhile, the torsional stiffness of the designed continuum joint reaches a considerable value of 8.73mNm/∘ and provides stable support for instruments during operations. Ex-vivo experiments of gastric tissue biopsy have been performed to verify the feasibility of the presented design in a practical scenario
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