A Convolutional Neural Network-based Patent Image Retrieval Method for Design Ideation

The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we propose a convolutional neural network (CNN)-based patent image retrieval method. The core of this approach is a novel neural network architecture named Dual-VGG that is aimed to accomplish two tasks: visual material type prediction and international patent classification (IPC) class label prediction. In turn, the trained neural network provides the deep features in the image embedding vectors that can be utilized for patent image retrieval and visual mapping. The accuracy of both training tasks and patent image embedding space are evaluated to show the performance of our model. This approach is also illustrated in a case study of robot arm design retrieval. Compared to traditional keyword-based searching and Google image searching, the proposed method discovers more useful visual information for engineering design.Comment: 11 pages, 11 figure

Wireless Controlled Robotic Automation System

The project is to develope a controller and to control a 6 DOF robotic arm for Pick & Place Application over wireless. The objective is to learn various types of control methods for the pick and place robotic arm for educational purpose uses. The 6-DOF robot arm is controlled by a serial servo controller circuit board. The controller board utilizes a Atmega328 microcontroller ( Boot loaded with Arduino Diecimila Bootloader ) from Atmel Corporation as the control system to control all the activities. The input sensors like potentiometers will send a the input signals to the microcontroller, then microcontroller will analyze the data accordingly and will send control signals to the output devices. This output signal basically turns ON or OFF the output devices such as servo motors. The servo controller board is connected to the serial port on a PC running the Microsoft Windows operating system. The ATMega328 will be programmed to run robot arm sequences independently by help of a FT232RL breakout board. Arduino integrated development environment (IDE) is used to develop the arduino sketch. Various types of control methods have been implemented. Manual control of the Robotic arm by mirroring the designed prototype movements. Prototype was developed by help of sensors like potentiometers. Also automated control of the robotic arm has been realized. The controller is also interfaced with a XBee XBP24-AWI-001 wireless module for remote control of the robotic arm from a PC using a graphical user interface (GUI), which was designed using processing development environment (PDE).The EEPROM present in Atmega328 MC unit has been utilized to make the robotic arm learnable. i.e. it can learn the movements stored in the memory and can replay it whenever prompted remotely. Programming is done remotely and the output data is sent over wireless to control the robotic arm

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities

We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals. Through this interface, humans communicate their intended objects of interest and actions to the robots using electroencephalography (EEG). Our novel system demonstrates success in an expansive array of 20 challenging, everyday household activities, including cooking, cleaning, personal care, and entertainment. The effectiveness of the system is improved by its synergistic integration of robot learning algorithms, allowing for NOIR to adapt to individual users and predict their intentions. Our work enhances the way humans interact with robots, replacing traditional channels of interaction with direct, neural communication. Project website: https://noir-corl.github.io/

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

Learning-based robotic manipulation for dynamic object handling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronic Engineering at the School of Food and Advanced Technology, Massey University, Turitea Campus, Palmerston North, New Zealand

Figures are re-used in this thesis with permission of their respective publishers or under a Creative Commons licence.Recent trends have shown that the lifecycles and production volumes of modern products are shortening. Consequently, many manufacturers subject to frequent change prefer flexible and reconfigurable production systems. Such schemes are often achieved by means of manual assembly, as conventional automated systems are perceived as lacking flexibility. Production lines that incorporate human workers are particularly common within consumer electronics and small appliances. Artificial intelligence (AI) is a possible avenue to achieve smart robotic automation in this context. In this research it is argued that a robust, autonomous object handling process plays a crucial role in future manufacturing systems that incorporate robotics—key to further closing the gap between manual and fully automated production. Novel object grasping is a difficult task, confounded by many factors including object geometry, weight distribution, friction coefficients and deformation characteristics. Sensing and actuation accuracy can also significantly impact manipulation quality. Another challenge is understanding the relationship between these factors, a specific grasping strategy, the robotic arm and the employed end-effector. Manipulation has been a central research topic within robotics for many years. Some works focus on design, i.e. specifying a gripper-object interface such that the effects of imprecise gripper placement and other confounding control-related factors are mitigated. Many universal robotic gripper designs have been considered, including 3-fingered gripper designs, anthropomorphic grippers, granular jamming end-effectors and underactuated mechanisms. While such approaches have maintained some interest, contemporary works predominantly utilise machine learning in conjunction with imaging technologies and generic force-closure end-effectors. Neural networks that utilise supervised and unsupervised learning schemes with an RGB or RGB-D input make up the bulk of publications within this field. Though many solutions have been studied, automatically generating a robust grasp configuration for objects not known a priori, remains an open-ended problem. An element of this issue relates to a lack of objective performance metrics to quantify the effectiveness of a solution—which has traditionally driven the direction of community focus by highlighting gaps in the state-of-the-art. This research employs monocular vision and deep learning to generate—and select from—a set of hypothesis grasps. A significant portion of this research relates to the process by which a final grasp is selected. Grasp synthesis is achieved by sampling the workspace using convolutional neural networks trained to recognise prospective grasp areas. Each potential pose is evaluated by the proposed method in conjunction with other input modalities—such as load-cells and an alternate perspective. To overcome human bias and build upon traditional metrics, scores are established to objectively quantify the quality of an executed grasp trial. Learning frameworks that aim to maximise for these scores are employed in the selection process to improve performance. The proposed methodology and associated metrics are empirically evaluated. A physical prototype system was constructed, employing a Dobot Magician robotic manipulator, vision enclosure, imaging system, conveyor, sensing unit and control system. Over 4,000 trials were conducted utilising 100 objects. Experimentation showed that robotic manipulation quality could be improved by 10.3% when selecting to optimise for the proposed metrics—quantified by a metric related to translational error. Trials further demonstrated a grasp success rate of 99.3% for known objects and 98.9% for objects for which a priori information is unavailable. For unknown objects, this equated to an improvement of approximately 10% relative to other similar methodologies in literature. A 5.3% reduction in grasp rate was observed when removing the metrics as selection criteria for the prototype system. The system operated at approximately 1 Hz when contemporary hardware was employed. Experimentation demonstrated that selecting a grasp pose based on the proposed metrics improved grasp rates by up to 4.6% for known objects and 2.5% for unknown objects—compared to selecting for grasp rate alone. This project was sponsored by the Richard and Mary Earle Technology Trust, the Ken and Elizabeth Powell Bursary and the Massey University Foundation. Without the financial support provided by these entities, it would not have been possible to construct the physical robotic system used for testing and experimentation. This research adds to the field of robotic manipulation, contributing to topics on grasp-induced error analysis, post-grasp error minimisation, grasp synthesis framework design and general grasp synthesis. Three journal publications and one IEEE Xplore paper have been published as a result of this research

Learning to grasp in unstructured environments with deep convolutional neural networks using a Baxter Research Robot

Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. The capability of a robotic system to manipulate objects in unstructured environments is becoming an increasingly necessary skill. Due to the dynamic nature of these environments, traditional methods, that require expert human knowledge, fail to adapt automatically. After reviewing the relevant literature a method was proposed to utilise deep transfer learning techniques to detect object grasps from coloured depth images. A grasp describes how a robotic end-effector can be arranged to securely grasp an object and successfully lift it without slippage. In this study, a ResNet-50 convolutional neural network (CNN) model is trained on the Cornell grasp dataset. The training was completed within 30 hours using a workstation PC with accelerated GPU support via an NVIDIA Titan X. The trained grasp detection model was further evaluated with a Baxter research robot and a Microsoft Kinect-v2 and a successful grasp detection accuracy of 93.91% was achieved on a diverse set of novel objects. Physical grasping trials were conducted on a set of 8 different objects. The overall system achieves an average grasp success rate of 65.0% while performing the grasp detection in under 25 milliseconds. The results analysis concluded that the objects with reasonably straight edges and moderately pronounced heights above the table are easily detected and grasped by the system

Study of robotics systems applications to the space station program

Applications of robotics systems to potential uses of the Space Station as an assembly facility, and secondarily as a servicing facility, are considered. A typical robotics system mission is described along with the pertinent application guidelines and Space Station environmental assumptions utilized in developing the robotic task scenarios. A functional description of a supervised dual-robot space structure construction system is given, and four key areas of robotic technology are defined, described, and assessed. Alternate technologies for implementing the more routine space technology support subsystems that will be required to support the Space Station robotic systems in assembly and servicing tasks are briefly discussed. The environmental conditions impacting on the robotic configuration design and operation are reviewed