812 research outputs found
Robotic system for garment perception and manipulation
Mención Internacional en el tÃtulo 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 podrÃan 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 usarÃa 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 IngenierÃa Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Abderrahmane Kheddar.- Secretario: Ramón Ignacio Barber Castaño.- Vocal: Karinne RamÃrez-Amar
Pneumatic Hyperelastic Robotic End-Effector for Grasping Soft Curved Organic Objects
Pneumatically-driven soft robotic grippers can elastically deform to grasp delicate, curved organic objects with minimal surface damage. However, common actuators have complex geometries and are fabricated with ultra-soft hyperelastic elastomers not originally intended for scientific applications. The complexity of the actuator geometry and extreme nonlinearity of their material’s stress-strain behaviour make it difficult to predict the actuator’s deformation prior to experimentation. In this work, a compact soft pneumatic gripper made with polydimethylsiloxane (PDMS) is developed for grasping delicate organic objects, analyzed through computational modelling and experimentally validated. COMSOL Multiphysics is used to simulate the impact of geometrical parameters on the actuator’s behaviour, allowing for the refinement of the proposed geometry prior to fabrication. Optimal parameters are selected for fabrication, with experimental tests matching simulations within ± 1 mm. Gripper performance is evaluated for three actuator wall thicknesses in terms of contact area with target, contact force, and maximum payload before slippage. The comparative assessment between simulations and experiments demonstrate that the proposed soft actuators can be used in robotic grippers tailored for grasping delicate objects without damaging their surface. Furthermore, analysis of the actuators provides additional insight on how to design simple but effective soft systems
PHYSICS-BASED SHAPE MORPHING AND PACKING FOR LAYOUT DESIGN
The packing problem, also named layout design, has found wide applications in the mechanical engineering field. In most cases, the shapes of the objects do not change during the packing process. However, in some applications such as vehicle layout design, shape morphing may be required for some specific components (such as water and fuel reservoirs). The challenge is to fit a component of sufficient size in the available space in a crowded environment (such as the vehicle under-hood) while optimizing the overall performance objectives of the vehicle and improving design efficiency. This work is focused on incorporating component shape design into the layout design process, i.e. finding the optimal locations and orientations of all the components within a specified volume, as well as the suitable shapes of selected ones. The first major research issue is to identify how to efficiently and accurately morph the shapes of components respecting the functional constraints. Morphing methods depend on the geometrical representation of the components. The traditional parametric representation may lend itself easily to modification, but it relies on assumption that the final approximate shape of the object is known, and therefore, the morphing freedom is very limited. To morph objects whose shape can be changed arbitrarily in layout design, a mesh based morphing method based on a mass-spring physical model is developed. For this method, there is no need to explicitly specify the deformations and the shape morphing freedom is not confined. The second research issue is how to incorporate component shape design into a layout design process. Handling the complete problem at once may be beyond our reach,therefore decomposition and multilevel approaches are used. At the system level, a genetic algorithm (GA) is applied to find the positions and orientations of the objects, while at the sub-system or component level, morphing is accomplished for select components. Although different packing applications may have different objectives and constraints, they all share some common issues. These include CAD model preprocessing for packing purpose, data format translation during the packing process if performance evaluation and morphing use different representation methods, efficiency of collision detection methods, etc. These common issues are all brought together under the framework of a general methodology for layout design with shape morphing. Finally, practical examples of vehicle under-hood/underbody layout design with the mass-spring physical model based shape morphing are demonstrated to illustrate the proposed approach before concluding and proposing continuing work
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Recognition and Manipulation of Deformable Objects Using Predictive Thin Shell Modeling
This thesis focuses on the task of dexterous manipulation of deformable objects, and in particular, clothing and garments. The task of manipulating deformable objects such as clothing can be broken down into a series of sub-tasks: (1) perceive and pick up garment, and then identify garment and recognize its pose; (2) using a manipulation strategy, regrasp the object to put it into a canonical state; (3) scan the surface of the object to find wrinkles, and use an iron to remove the wrinkles; (4) starting from the wrinkle-free state, fold the garment according to pre-planned sequence of manipulations with optimized trajectories; In this thesis, we will address all the phases of this process.
A key contribution of the work is innovative use of simulation. We use offline simulation results to predict states of deformable objects (i.e. cloth, fabric, clothing) that are then recognized by a robotic vision/grasping system to correctly pick up and manipulate these objects. The recognition will use the simulation engine to deform the models in real time to find correct matches. The simulation will also be used to find the optimized trajectories for the manipulation of the garments, such as the garment folding
Automation and Robotics: Latest Achievements, Challenges and Prospects
This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections
Innovative robot hand designs of reduced complexity for dexterous manipulation
This thesis investigates the mechanical design of robot hands to sensibly reduce the system complexity in terms of the number of actuators and sensors, and control needs for performing grasping and in-hand manipulations of unknown objects.
Human hands are known to be the most complex, versatile, dexterous manipulators in nature, from being able to operate sophisticated surgery to carry out a wide variety of daily activity tasks (e.g. preparing food, changing cloths, playing instruments, to name some). However, the understanding of why human hands can perform such fascinating tasks still eludes complete comprehension.
Since at least the end of the sixteenth century, scientists and engineers have tried to match the sensory and motor functions of the human hand. As a result, many contemporary humanoid and anthropomorphic robot hands have been developed to closely replicate the appearance and dexterity of human hands, in many cases using sophisticated designs that integrate multiple sensors and actuators---which make them prone to error and difficult to operate and control, particularly under uncertainty.
In recent years, several simplification approaches and solutions have been proposed to develop more effective and reliable dexterous robot hands. These techniques, which have been based on using underactuated mechanical designs, kinematic synergies, or compliant materials, to name some, have opened up new ways to integrate hardware enhancements to facilitate grasping and dexterous manipulation control and improve reliability and robustness.
Following this line of thought, this thesis studies four robot hand hardware aspects for enhancing grasping and manipulation, with a particular focus on dexterous in-hand manipulation. Namely: i) the use of passive soft fingertips; ii) the use of rigid and soft active surfaces in robot fingers; iii) the use of robot hand topologies to create particular in-hand manipulation trajectories; and iv) the decoupling of grasping and in-hand manipulation by introducing a reconfigurable palm.
In summary, the findings from this thesis provide important notions for understanding the significance of mechanical and hardware elements in the performance and control of human manipulation. These findings show great potential in developing robust, easily programmable, and economically viable robot hands capable of performing dexterous manipulations under uncertainty, while exhibiting a valuable subset of functions of the human hand.Open Acces
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