Directly Printable Flexible Strain Sensors for Bending and Contact Feedback of Soft Actuators

This paper presents a fully printable sensorized bending actuator that can be calibrated to provide reliable bending feedback and simple contact detection. A soft bending actuator following a pleated morphology, as well as a flexible resistive strain sensor, were directly 3D printed using easily accessible FDM printer hardware with a dual-extrusion tool head. The flexible sensor was directly welded to the bending actuator’s body and systematically tested to characterize and evaluate its response under variable input pressure. A signal conditioning circuit was developed to enhance the quality of the sensory feedback, and flexible conductive threads were used for wiring. The sensorized actuator’s response was then calibrated using a vision system to convert the sensory readings to real bending angle values. The empirical relationship was derived using linear regression and validated at untrained input conditions to evaluate its accuracy. Furthermore, the sensorized actuator was tested in a constrained setup that prevents bending, to evaluate the potential of using the same sensor for simple contact detection by comparing the constrained and free-bending responses at the same input pressures. The results of this work demonstrated how a dual-extrusion FDM printing process can be tuned to directly print highly customizable flexible strain sensors that were able to provide reliable bending feedback and basic contact detection. The addition of such sensing capability to bending actuators enhances their functionality and reliability for applications such as controlled soft grasping, flexible wearables, and haptic devices

Robotic system for garment perception and manipulation

Mención Internacional en el título de doctorGarments are a key element of people’s daily lives, as many domestic tasks -such as laundry-, revolve around them. Performing such tasks, generally dull and repetitive, implies devoting many hours of unpaid labor to them, that could be freed through automation. But automation of such tasks has been traditionally hard due to the deformable nature of garments, that creates additional challenges to the already existing when performing object perception and manipulation. This thesis presents a Robotic System for Garment Perception and Manipulation that intends to address these challenges. The laundry pipeline as defined in this work is composed by four independent -but sequential- tasks: hanging, unfolding, ironing and folding. The aim of this work is the automation of this pipeline through a robotic system able to work on domestic environments as a robot household companion. Laundry starts by washing the garments, that then need to be dried, frequently by hanging them. As hanging is a complex task requiring bimanipulation skills and dexterity, a simplified approach is followed in this work as a starting point, by using a deep convolutional neural network and a custom synthetic dataset to study if a robot can predict whether a garment will hang or not when dropped over a hanger, as a first step towards a more complex controller. After the garment is dry, it has to be unfolded to ease recognition of its garment category for the next steps. The presented model-less unfolding method uses only color and depth information from the garment to determine the grasp and release points of an unfolding action, that is repeated iteratively until the garment is fully spread. Before storage, wrinkles have to be removed from the garment. For that purpose, a novel ironing method is proposed, that uses a custom wrinkle descriptor to locate the most prominent wrinkles and generate a suitable ironing plan. The method does not require a precise control of the light conditions of the scene, and is able to iron using unmodified ironing tools through a force-feedback-based controller. Finally, the last step is to fold the garment to store it. One key aspect when folding is to perform the folding operation in a precise manner, as errors will accumulate when several folds are required. A neural folding controller is proposed that uses visual feedback of the current garment shape, extracted through a deep neural network trained with synthetic data, to accurately perform a fold. All the methods presented to solve each of the laundry pipeline tasks have been validated experimentally on different robotic platforms, including a full-body humanoid robot.La ropa es un elemento clave en la vida diaria de las personas, no sólo a la hora de vestir, sino debido también a que muchas de las tareas domésticas que una persona debe realizar diariamente, como hacer la colada, requieren interactuar con ellas. Estas tareas, a menudo tediosas y repetitivas, obligan a invertir una gran cantidad de horas de trabajo no remunerado en su realización, las cuales podrían reducirse a través de su automatización. Sin embargo, automatizar dichas tareas ha sido tradicionalmente un reto, debido a la naturaleza deformable de las prendas, que supone una dificultad añadida a las ya existentes al llevar a cabo percepción y manipulación de objetos a través de robots. Esta tesis presenta un sistema robótico orientado a la percepción y manipulación de prendas, que pretende resolver dichos retos. La colada es una tarea doméstica compuesta de varias subtareas que se llevan a cabo de manera secuencial. En este trabajo, se definen dichas subtareas como: tender, desdoblar, planchar y doblar. El objetivo de este trabajo es automatizar estas tareas a través de un sistema robótico capaz de trabajar en entornos domésticos, convirtiéndose en un asistente robótico doméstico. La colada comienza lavando las prendas, las cuales han de ser posteriormente secadas, generalmente tendiéndolas al aire libre, para poder realizar el resto de subtareas con ellas. Tender la ropa es una tarea compleja, que requiere de bimanipulación y una gran destreza al manipular la prenda. Por ello, en este trabajo se ha optado por abordar una versión simplicada de la tarea de tendido, como punto de partida para llevar a cabo investigaciones más avanzadas en el futuro. A través de una red neuronal convolucional profunda y un conjunto de datos de entrenamiento sintéticos, se ha llevado a cabo un estudio sobre la capacidad de predecir el resultado de dejar caer una prenda sobre un tendedero por parte de un robot. Este estudio, que sirve como primer paso hacia un controlador más avanzado, ha resultado en un modelo capaz de predecir si la prenda se quedará tendida o no a partir de una imagen de profundidad de la misma en la posición en la que se dejará caer. Una vez las prendas están secas, y para facilitar su reconocimiento por parte del robot de cara a realizar las siguientes tareas, la prenda debe ser desdoblada. El método propuesto en este trabajo para realizar el desdoble no requiere de un modelo previo de la prenda, y utiliza únicamente información de profundidad y color, obtenida mediante un sensor RGB-D, para calcular los puntos de agarre y soltado de una acción de desdoble. Este proceso es iterativo, y se repite hasta que la prenda se encuentra totalmente desdoblada. Antes de almacenar la prenda, se deben eliminar las posibles arrugas que hayan surgido en el proceso de lavado y secado. Para ello, se propone un nuevo algoritmo de planchado, que utiliza un descriptor de arrugas desarrollado en este trabajo para localizar las arrugas más prominentes y generar un plan de planchado acorde a las condiciones de la prenda. A diferencia de otros métodos existentes, este método puede aplicarse en un entorno doméstico, ya que no requiere de un contol preciso de las condiciones de iluminación. Además, es capaz de usar las mismas herramientas de planchado que usaría una persona sin necesidad de realizar modificaciones a las mismas, a través de un controlador que usa realimentación de fuerza para aplicar una presión constante durante el planchado. El último paso al hacer la colada es doblar la prenda para almacenarla. Un aspecto importante al doblar prendas es ejecutar cada uno de los dobleces necesarios con precisión, ya que cada error o desfase cometido en un doblez se acumula cuando la secuencia de doblado está formada por varios dobleces consecutivos. Para llevar a cabo estos dobleces con la precisión requerida, se propone un controlador basado en una red neuronal, que utiliza realimentación visual de la forma de la prenda durante cada operación de doblado. Esta realimentación es obtenida a través de una red neuronal profunda entrenada con un conjunto de entrenamiento sintético, que permite estimar la forma en 3D de la parte a doblar a través de una imagen monocular de la misma. Todos los métodos descritos en esta tesis han sido validados experimentalmente con éxito en diversas plataformas robóticas, incluyendo un robot humanoide.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Abderrahmane Kheddar.- Secretario: Ramón Ignacio Barber Castaño.- Vocal: Karinne Ramírez-Amar

Advanced Bionic Attachment Equipment Inspired by the Attachment Performance of Aquatic Organisms: A Review

In nature, aquatic organisms have evolved various attachment systems, and their attachment ability has become a specific and mysterious survival skill for them. Therefore, it is significant to study and use their unique attachment surfaces and outstanding attachment characteristics for reference and develop new attachment equipment with excellent performance. Based on this, in this review, the unique non-smooth surface morphologies of their suction cups are classified and the key roles of these special surface morphologies in the attachment process are introduced in detail. The recent research on the attachment capacity of aquatic suction cups and other related attachment studies are described. Emphatically, the research progress of advanced bionic attachment equipment and technology in recent years, including attachment robots, flexible grasping manipulators, suction cup accessories, micro-suction cup patches, etc., is summarized. Finally, the existing problems and challenges in the field of biomimetic attachment are analyzed, and the focus and direction of biomimetic attachment research in the future are pointed out

Designing a robotic port system for laparo-endoscopic single-site surgery

Current research and development in the field of surgical interventions aim to reduce the invasiveness by using few incisions or natural orifices in the body to access the surgical site. Considering surgeries in the abdominal cavity, the Laparo-Endoscopic Single-site Surgery (LESS) can be performed through a single incision in the navel, reducing blood loss, post-operative trauma, and improving the cosmetic outcome. However, LESS results in less intuitive instrument control, impaired ergonomic, loss of depth and haptic perception, and restriction of instrument positioning by a single incision. Robot-assisted surgery addresses these shortcomings, by introducing highly articulated, flexible robotic instruments, ergonomic control consoles with 3D visualization, and intuitive instrument control algorithms. The flexible robotic instruments are usually introduced into the abdomen via a rigid straight port, such that the positioning of the tools and therefore the accessibility of anatomical structures is still constrained by the incision location. To address this limitation, articulated ports for LESS are proposed by recent research works. However, they focus on only a few aspects, which are relevant to the surgery, such that a design considering all requirements for LESS has not been proposed yet. This partially originates in the lack of anatomical data of specific applications. Further, no general design guidelines exist and only a few evaluation metrics are proposed. To target these challenges, this thesis focuses on the design of an articulated robotic port for LESS partial nephrectomy. A novel approach is introduced, acquiring the available abdominal workspace, integrated into the surgical workflow. Based on several generated patient datasets and developed metrics, design parameter optimization is conducted. Analyzing the surgical procedure, a comprehensive requirement list is established and applied to design a robotic system, proposing a tendon-driven continuum robot as the articulated port structure. Especially, the aspects of stiffening and sterile design are addressed. In various experimental evaluations, the reachability, the stiffness, and the overall design are evaluated. The findings identify layer jamming as the superior stiffening method. Further, the articulated port is proven to enhance the accessibility of anatomical structures and offer a patient and incision location independent design