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El Robotino es un robot mòbil desenvolupat per Festo Didactics per a l’aprenentatge i la investigació en el camp dels robots autònoms. El problema del posicionament d’aquest tipus de robots mòbils és àmpliament estudiat i encara no té una solució ideal per a totes les àrees d’aplicació. El Robotino utilitza la tècnica de l’odometria amb les seves rodes per estimar la posició i orientació. Per millorar la precisió del posicionament, anteriorment s’han desenvolupat altres projectes, com la integració d’un sensor giroscopi per estimar la seva orientació amb més precisió. L’objectiu d’aquest projecte es millorar les capacitats de posicionament del Robotino. Per a fer-ho, s’ha desenvolupat una solució de posicionament alternativa basada en sistemes de navegació inercial (giroscopis i acceleròmetres). El dispositiu escollit és una Unitat de Mesura Inercial (IMU), que s’ha integrat físicament amb el Robotino a traves del port sèrie RS-232. S’ha desenvolupat el programari per fer servir aquest nou dispositiu juntament amb el programari existent del Robotino, el qual calcula la velocitat i posició del robot en tot moment. S’han realitzat proves per verificar l’eficàcia del nou sistema de posicionament, les quals han consistit a fer que el Robotino realitzi diferents desplaçaments i extreure’n les dades cinemàtiques per comparar-les amb el sistema de posicionament per odometria. Les proves s’han fet amb un altre programari d’aplicació demostrativa, desenvolupada en aquest projecte. Els resultats obtinguts mostren que el nou sistema de posicionament no es millor que el sistema anterior, i per tant no s’ha assolit l’objectiu del treball. Els motius d’això han estat la falta de precisió en la calibració del dispositiu utilitzat, així com les limitacions pròpies del dispositiu per a aquest tipus d’aplicacions

Per a moltes empreses, l'automatització dels magatzems s'està convertint en un procés clau per millorar l'eficiència, reduir els costos i augmentar la seguretat. En aquest procés, els robots AGV son una eina fonamental. L'objectiu d'aquest projecte era desenvolupar un sistema de gestió i control remot per a un robot AGV (vehicle guiat autònomament) per a la seva utilització futura en aplicacions d'automatització de magatzem. Durant la realització del projecte, s'ha complert aquest objectiu amb èxit gràcies a la utilització de dades en temps real de lidar, càmera i altres sensors de l'AGV. Això ha permès al robot navegar pel magatzem, transportar mercaderia i inventariar el magatzem automàticament. For many companies, warehouse automation is becoming a key process to improve efficiency, reduce costs and increase security. As small, autonomous robotic platforms, AGV robots are key enablers for warehouse automation processes. The objective of this project was to develop a remote control and management system for an AGV robot (autonomous guided vehicle) for its future use in warehouse automation applications. During the realization of the project, this objective has been successfully met thanks to the use of real-time data from Lidar, camera and other AGV sensors that, with the developed system, allow the AGV robot to navigate through the warehouse, transport merchandise and automatically log or update warehouse inventory Para muchas empresas, la automatización de los almacenes se está convirtiendo en un proceso clave para mejorar la eficiencia, reducir los costes y aumentar la seguridad. En estos procesos de automatización, los robots AGV son una herramienta fundamental. El objetivo de este proyecto es desarrollar un sistema de gestión y control remoto para un robot AGV (vehículo guiado autónomamente) para su futura utilización en aplicaciones de automatización de almacén. Durante la realización del proyecto se ha cumplido este objetivo con éxito gracias a la utilización de datos en tiempo real de Lidar, cámara y otros sensores del AGV que con el sistema desarrollado permiten al robot AGV navegar por el almacén, transportar mercancía e inventariar el almacén automáticamente

Being able to teach complex capabilities, such as folding garments, to a bi-manual robot is a very challenging task, which is often tackled using learning from demonstration datasets. The few garment folding datasets available nowadays to the robotics research community are either gathered from human demonstrations or generated through simulation. The former have the huge problem of perceiving human action and transferring it to the dynamic control of the robot, while the latter requires coding human motion into the simulator in open loop, resulting in far-from-realistic movements. In this article, we present a reduced but very accurate dataset of human cloth folding demonstrations. The dataset is collected through a novel virtual reality (VR) framework we propose, based on Unity¿s 3D platform and the use of a HTC Vive Pro system. The framework is capable of simulating very realistic garments while allowing users to interact with them, in real time, through handheld controllers. By doing so, and thanks to the immersive experience, our framework gets rid of the gap between the human and robot perception-action loop, while simplifying data capture and resulting in more realistic samples., This work was developed in the context of the project CLOTHILDE (”CLOTH manIpulation Learning from DEmonstrations”) which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 741930) and is also supported by the BURG project PCI2019-103447 funded by MCIN/ AEI /10.13039/501100011033 and by the ”European Union”.

A dia d’avui trobem intel·ligència artificial aplicada a gran diversitat de camps, la podem trobar als telèfons mòbils, presents a la butxaca de qualsevol persona fins als hospitals o als bancs. L’aplicació de la intel·ligència artificial amb combinació de la robòtica ha representat una millora de la qualitat de vida. Des-de feines tedioses i monòtones fins a tasques perilloses o complicades que abans es duien a terme per persones ara es fan amb robots. Els robots autònoms són capaços de reconèixer i aprendre sobre el seu entorn i prendre decisions de forma independent sense la necessitat de control humà. Varien amb dimensions, funcionalitat, mobilitat, etc. en funció de les seves aplicacions. Es poden trobar als habitatges com els robot Roomba, processos industrials o destinats a l’exploració. La percepció es molt important en el robots autònoms, ja que abans de prendre decisions han d’avaluar informació sobre l’entorn. L’objectiu d’aquest treball de final grau serà dotar un robot quadrúpede, el Bittle, amb percepció visual a traves de l’entrenament d’una xarxa neuronal que utilitza el model Mask RCNN. Controlat per una Raspberry Pi, es buscara que el Bittle sigui capaç de detectar pilotes i actuar amb conseqüència

Este proyecto ha tenido como objetivo principal la construcción de un robot capaz de jugar autónomamente al conocido juego 2048. Se puede desglosar el trabajo en dos grandes partes: una de software y una de hardware. En lo que respecta a la primera, se ha realizado un análisis inicial de diversos aspectos del juego y se ha comparado distintos métodos de resolución, principalmente minimax, expectimax y redes neuronales. De todos estos métodos se han propuesto multitud de variantes en las que se han añadido diversas características como, por ejemplo, las distintas heurísticas para evaluar los nodos del árbol de búsqueda en los métodos minimax y expectimax o distintas arquitecturas en el caso de las redes neuronales. En cuanto al hardware, se ha construido un robot ideado para ser posicionado delante de la pantalla de un ordenador con el juego abierto e interaccionar con el ordenador, dando las órdenes de movimiento a través del puerto USB. El robot incorpora una placa Raspberry Pi 4, una placa Pro Micro ATmega32u4, una cámara y componentes de soporte, todo ello, dentro de una carcasa hecha a partir de modelado e impresión 3D. The main objective of this project was the construction of a robot capable of autonomously playing the well-known game 2048. The work has two main parts: software and hardware. Regarding the first, an initial analysis of various aspects of the game has been carried out and different resolution methods have been compared, mainly minimax, expectimax and neural networks. Many variants of all these methods have been proposed in which various features have been added, such as, for example, the different heuristics to evaluate the nodes of the search tree in the minimax and expectimax methods or different architectures in the case of neural networks. For the hardware, a robot has been built and designed to be positioned in front of a computer screen with the game open and interact with the computer, giving movement orders through the USB port. The robot incorporates a Raspberry Pi 4 board, a Pro Micro ATmega32u4 board, a camera and supporting components, all inside a 3D modelled and printed case.

Trabajo fin de máster presentado en la Universidad Politécnica de Cataluña, Máster en Automática y Robótica.--2021-10-08, A fundamental necessity in mobile robotics is the ability of knowing the robot own location. In many applications, a map of the environment is not available or it does not have the accuracy or the information necessary for robot localization. In this cases, it is necessary to build a map simultaneously (SLAM). In applications where the GPS signal is not reliable, like in indoor environments, the localization has to be based in estimating robot position with the information of the robot movement. This estimation is obtained in local coordinates and it implies that a drift in the estimation accumulates error over time. In order to reduce the drift effects, it is necessary the capacity to recognize places that had already been visited and readjust the localization. This search of loop closures provides global reference points to the localization system. This field of study is already a very discussed topic, but finding an equilibrium between efficiency and robustness in order to be able to work in large environments is still a challenge. The aim of this work is to equip the WOLF SLAM library with an state-of-art loop closure for 2D LIDAR sensors. WOLF SLAM library is integrated with ROS and proposes an interesting environment to manage SLAM and localization problems based in graph-SLAM. In this work, we first performed a review of the state of the art for loop closure that lead us to FALKO. It is an open source project focused in loop closure that implements feature detection and scene matching using descriptors. Furthermore, the FALKO loop closure algorithm has been integrated along with an Iterative Closest Point method for robustness and accuracy purposes. ICP is a point to line metric optimized for range-finder scan matching. The integration and developed algorithms has been tested with experimental data from a real robot

Nowadays, autonomous robots for all different purposes are the state-of-the-art technology, all companies across the industry are implementing all kind of autonomous machines to control and computerize the industrial systems and processes, making errors virtually impossible and, despite all the first investment, the transition pays off in the mid and long-term because of all the time and resources saved. This new industrial phase, which is a current tendency commonly known as Industry 4.01 , focuses on interconnectivity, automation, machine learning, and real-time data. The same trend applies to vehicles or mobile machines (for industrial or non-industrial purposes), the use of fossil fuels is decreasing whereas the implementation of automation and systems of artificial intelligence is strongly increasing day by day. Heavy-duty hydraulically driven mobile working machines (for mining, construction, forestry, and material handling purposes) are also in constant development for increasing efficiency and performance because of the higher requirements set by the constant growth of this sector. As a result, working machines are increasingly expected to become more precise, and robotics like towards autonomous operation. This thesis studies the design requirements and sizing for each hydraulic actuator system of the case of study, a long-reach manipulator 4x4 Haulotte HA16 RTJ PRO which is available at the University of Tampere (Hervanta Campus). This hydraulic articulated boom, originally manually driven, is being redesigned into a servo-hydraulic system maintaining the original diesel motor source and taking into account its limited power. The general work consists of requirements of development, hydraulic engineering designs, component selection, modeling, and simulations towards a new prototyped system meeting the set requirements. During the making of this thesis, the component selection was already done and the scope of the study relies on the kinematic and hydraulic analysis for the vehicle. Also, this thesis has pursued another objective, which is the compilation of technical data (such as the boom dimensions, original hydraulics, or technical information about the main components) among the different reports about the Haulotte, trying to achieve that the upcoming students or researchers can get clearer ideas and the maximum possible information about the case of study reading this document. The goals are got from a theoretical viewpoint through equations and hypotheses considered throughout the study, therefore, the results obtained must be taken as approximations. The reason is the coronavirus pandemic, which forced the university and the laboratories to close since the beginning of the semester and some parameters or variables taken may not always coincide with the reality because were taken from a 3D model from the original Haulotte website. Keywords: Wheeled Mobile Robot, Mobile platform, Hydraulics, Automation, Kinematics, Haulotte, HA16 RTJ PRO, Boom, Articulating boom lift, 4WSD

One of the problems that service robotics deals with is to bring mobile manipulators to work in semi-structured human scenarios, which requires an efficient and flexible way to execute every-day tasks, like serve a cup in a cluttered environment. Usually, for those tasks, the combination of symbolic and geometric levels of planning is necessary, as well as the integration of perception models with knowledge to guide both planning levels, resulting in a sequence of actions or skills which, according to the current knowledge of the world, may be executed. This paper proposes a planning and execution framework, called SkillMaN, for robotic manipulation tasks, which is equipped with a module with experiential knowledge (learned from its experience or given by the user) on how to execute a set of skills, like pick-up, put-down or open a drawer, using workflows as well as robot trajectories. The framework also contains an execution assistant with geometric tools and reasoning capabilities to manage how to actually execute the sequence of motions to perform a manipulation task (which are forwarded to the executor module), as well as the capacity to store the relevant information to the experiential knowledge for further usage, and the capacity to interpret the actual perceived situation (in case the preconditions of an action do not hold) and to feed back the updated state to the planner to resume from there, allowing the robot to adapt to non-expected situations. To evaluate the viability of the proposed framework, an experiment has been proposed involving different skills performed with various types of objects in different scene contexts

Autonomous indoor service robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions. Particularly, for complex manipulation tasks which are subject to geometric constraints, spatial information and a rich semantic knowledge about objects, types, and functionality are required, together with the way in which these objects can be manipulated. In this line, this paper presents an ontological-based reasoning framework called Perception and Manipulation Knowledge (PMK) that includes: (1) the modeling of the environment in a standardized way to provide common vocabularies for information exchange in human-robot or robot-robot collaboration, (2) a sensory module to perceive the objects in the environment and assert the ontological knowledge, (3) an evaluation-based analysis of the situation of the objects in the environment, in order to enhance the planning of manipulation tasks. The paper describes the concepts and the implementation of PMK, and presents an example demonstrating the range of information the framework can provide for autonomous robots.

Research article compendium : peace and conflict research The University of Barcelona, in collaboration with different Spanish and foreign institutions and universities, hosted an international workshop on the ‘Sense and Scope of Autonomy in Emerging Military and Security Technologies’, which took place in February 2017. This academic event paved the way to the elaboration of the present report, which contains a compendium of innovative articles written by experts on scientific, legal, diplomatic and military matters. All papers reflect a broad spectrum of views on how ‘autonomy’ can be understood in emerging military and security technologies and what their legal, technical, political and societal impacts are by building on discussions unfolded along a variety of reflective approaches.

It is at least two decades since the conventional robotic manipulators have become a common manufacturing tool for different industries, from automotive to pharmaceutical. The advances in manipulators and sensors have given robots the opportunity to become useful for more and more applications. Engineers have taken advantage of the extra mobility of the advanced robots to make them work in constrained environments, ranging from limited joint motions for redundant manipulators to obstacles in the way of mobile (ground, marine, and aerial) robots [1]. However, the incorporation some of these abilities and capacities that are already being used in land, have not made their way to the sea domain. This Abstract describes the project consisting in the design, development and manufacture of a prototype manipulator arm for ROVs introducing innovative fabrication technologies. The work has been done collaboratively among ACSM Maritime Agency SL, CIMA Group and the University of Vigo.

Treball desenvolupat dins el marc del programa 'European Project Semester'. The robotics facilities at Oslo and Akershus University College of Applied Sciences (HiOA) have at their disposal an autonomous humanoid robot by the name of Nao. It is a commercial product developed by SoftBank Robotics for research and educational purposes, and an international project team was assembled for the European Project Semester (EPS) program as a means of assessing its competencies and bolstering them wherever possible. The team set forth with a mission to establish meaningful interactions between the robot and his physical surroundings while placing an emphasis on humanlike behavior to the fullest extent. A goal was soon initiated for Nao to play a rule-based game against a human opponent such that his vast array of available faculties could be tested and put to use in one cohesive demonstration. Achieving this goal meant making advances in artificial intelligence,computer vision and motion planning, first as separate entities that would later converge into a singular yet multifaceted display of abilities. At the end of the project, computer vision has been implemented to the point where pattern recognition can be performed with accuracy amidst awide variety of error-prone detection scenarios. Several artificial intelligence schemes have been explored and compared quantitatively to derive performance metrics for each. Proof-ofconcept movement strategies have also been derived, and best practices for each focus area are provided as a resource for any future researchers or project groups who stand to benefit from the discoveries made in this endeavor

This work introduces new results on the modeling of early-vocal development using artificial intelligent cognitive architectures and a simulated vocal tract. The problem is addressed using intrinsically-motivated learning algorithms for autonomous sensorimotor exploration, a kind of algorithm belonging to the active learning architectures family. The artificial agent is able to autonomously select goals to explore its own sensorimotor system in regions where its competence to execute intended goals is improved. We propose to include a somatosensory system to provide a proprioceptive feedback signal to reinforce learning through the autonomous discovery of motor constraints. Constraints are represented by a somatosensory model which is unknown beforehand to the learner. Both the sensorimotor and somatosensory system are modeled using Gaussian mixture models. We argue that using an architecture which includes a somatosensory model would reduce redundancy in the sensorimotor model and drive the learning process more efficiently than algorithms taking into account only auditory feedback. The role of this proposed system is to predict whether an undesired collision within the vocal tract under a certain motor configuration is likely to occur. Thus, compromised motor configurations are rejected, guaranteeing that the agent is less prone to violate its own constraints.

This paper addresses the problem of obtaining the required motions for a humanoid robot to perform grasp actions trying to mimic the coordinated hand–arm movements humans do. The first step is the data acquisition and analysis, which consists in capturing human movements while grasping several everyday objects (covering four possible grasp types), mapping them to the robot and computing the hand motion synergies for the pre-grasp and grasp phases (per grasp type). Then, the grasp and motion synthesis step is done, which consists in generating potential grasps for a given object using the four family types, and planning the motions using a bi-directional multi-goal sampling-based planner, which efficiently guides the motion planning following the synergies in a reduced search space, resulting in paths with human-like appearance. The approach has been tested in simulation, thoroughly compared with other state-of-the-art planning algorithms obtaining better results, and also implemented in a real robot.

ResumenEl SLAM (simultaneous localization and mapping) es una técnica en la cual un robot o vehículo autónomo opera en un entorno a priori desconocido, utilizando únicamente sus sensores de abordo, mientras construye un mapa de su entorno, el cual utiliza al mismo tiempo para localizarse. Los sensores tienen un gran impacto en los algoritmos usados en SLAM. Enfoques recientes se están centrando en el uso de cámaras como sensor principal, ya que generan mucha información y están bien adaptadas para su aplicación en sistemas embebidos: son ligeras, baratas y ahorran energía. Sin embargo, a diferencia de los sensores de rango, los cuales proveen información angular y de rango, una cámara es un sensor proyectivo que mide el ángulo (bearing) respecto a los elementos de la imagen, por lo que la profundidad o rango no puede ser obtenida mediante una sola medición. Lo anterior ha motivado la aparición de una nueva familia de métodos en SLAM: los métodos de SLAM basados en sensores angulares, los cuales están principalmente basados en técnicas especiales para la inicialización de características en el sistema, permitiendo el uso de sensores angulares (como cámaras) en SLAM. Este artículo presenta un método práctico para la inicialización de nuevas características en sistemas de SLAM basados en sensores angulares. El método propuesto implementa mediante un retardo una técnica de triangulación estocástica para definir una hipótesis para la profundidad inicial de las características. Para mostrar el desempeño del método propuesto se presentan resultados experimentales con simulaciones y también se presentan dos casos de aplicación para escenarios con datos reales procedentes de distintos sensores angulares.AbstractThe SLAM or Simultaneous Localization and Mapping, is a technique in which a robot or autonomous vehicle operate in an a priori unknown environment, using only its onboard sensors to simultaneously build a map of its surroundings and use it to track its position. The sensors have a large impact on the algorithm used for SLAM. Recent approaches are focusing on the use of cameras as the main sensor, because they yield a lot of information and are well adapted for embedded systems: they are light, cheap and power saving. However, unlike range sensors which provide range and angular information, a camera is a projective sensor which measures the bearing of images features. Therefore depth information (range) cannot be obtained in a single step. This fact has propitiated the emergence of a new family of SLAM algorithms: the Bearing-Only SLAM methods, which mainly rely in especial techniques for features system-initialization in order to enable the use of bearing sensors (as cameras) in SLAM systems. In this work a practical method is presented, for initializing new features in bearing-only SLAM systems. The proposed method, defines a single hypothesis for the initial depth of features, by the use of an stochastic technique of triangulation. Several simulations as well two scenarios of applications with real data are presented, in order to show the performance of the proposed method.

Treball desenvolupat dins el marc del programa 'European Project Semester'. The robotics facilities at Oslo and Akershus University College of Applied Sciences (HiOA) have at their disposal an autonomous humanoid robot by the name of Nao. It is a commercial product developed by SoftBank Robotics for research and educational purposes, and an international project team was assembled for the European Project Semester (EPS) program as a means of assessing its competencies and bolstering them wherever possible. The team set forth with a mission to establish meaningful interactions between the robot and his physical surroundings while placing an emphasis on humanlike behavior to the fullest extent. A goal was soon initiated for Nao to play a rule-based game against a human opponent such that his vast array of available faculties could be tested and put to use in one cohesive demonstration. Achieving this goal meant making advances in artificial intelligence,computer vision and motion planning, first as separate entities that would later converge into a singular yet multifaceted display of abilities. At the end of the project, computer vision has been implemented to the point where pattern recognition can be performed with accuracy amidst awide variety of error-prone detection scenarios. Several artificial intelligence schemes have been explored and compared quantitatively to derive performance metrics for each. Proof-ofconcept movement strategies have also been derived, and best practices for each focus area are provided as a resource for any future researchers or project groups who stand to benefit from the discoveries made in this endeavor

Severe disabled children have little chance of environmental and social exploration and discovery, and due to this lack of interaction and independency, it may lead to an idea that they are unable to do anything by themselves. Trying to help these children on this situation, educational robotics can offer and aid, once it can give them a certain degree of independency in exploration of environment. The system developed in this work allows the child to transmit the commands to a robot. Sensors placed on the child’s body can obtain information from head movement or muscle signals to command the robot to carry out tasks. With the use of this system, the disabled children get a better cognitive development and social interaction, balancing in a certain way, the negative effects of their disabilities.

This paper shows the development of a high technical equipment to be used as tooling of submersible ROV (Remote Operated Vehicles) for offshore operations, particularly the design and fabrication by Additive Manufacturing (AM) of a Titanium Manipulator for ROVs. From the initial concept and design until a new formed company “TITANROB”, this document shortly describes the fabrication of hydraulic titanium manipulators for mid size ROV vehicles, the TitanRob series M501, G500 and the M700.

The work presented on this paper is aimed to explain the role that unmanned underwater vehicles (AUVs/ROVs) plays in the ECSEL-H2020 SWARMS project. The main goal of the project is to reduce the operational cost and increase the safety of tasks assigned to divers in these operations. This will be achieved enabling the AUVs/ROVs to work in a cooperative mesh. The challenge is to design and develop an integrated platform (a set of Software/Hardware components), incorporated into the current generation of underwater vehicles in order to improve autonomy, cooperation, robustness, cost-effectiveness, and reliability of the offshore operations. The first demonstration of the project will be performed at PLOCAN (Oceanic Platform of the Canary Islands) where these technologies will be validated on its first stage.

Primera posició al Ranquing 2018 dels treballs acadèmics més consultats de l’ETSETB The line following robots are exciting, because they are a gateway to robotics, and also allow the person who performs them have the freedom of being able to design using a variety of technology. In this project is intended to improve the detection system of a line follower, moving from an infrared sensor to a CCD Sensor. To do this, first and foremost, the different types of line follower robots are analyzed, typical technologies used, and improvements that can be applied. Later we will talk about the decisions that have been taken in this project, which technologies are to be used, as will be used and what problems have been taken to implement them. We will continue with detailing the specific development of the robot: at what point was in each stage, what was done to try to solve the problems, and how the electronics stayed. This point takes us to the final assembly of the robot, which we have named Linebot. Finally the operation of the robot will be discussed, particularly how it faces the challenges and makes decisions based on the input stimuli it receives, and the design improvements introduced in order to improve their performance. Los robots seguidores son apasionantes, ya que son una puerta de entrada a la robótica, y además permiten a la persona que los realiza tener la libertad de poderlo diseñar utilizando una variada gama de tecnología. En este proyecto se intentará mejorar el sistema de detección de un seguidor de líneas, pasando de los sensores de infrarrojos a un sensor CCD. Para ello, y en primer lugar, se analizarán los distintos tipos de robots seguidores de línea, que tecnologías típicas utilizan, y las mejoras que se pueden aplicar. Posteriormente se detallarán las decisiones que se han tomado en este proyecto, qué tecnologías se quieren utilizar, cómo se van a utilizar y qué problemas se han tenido al implementarlas. Seguiremos con el desarrollo específico del robot detallando: en qué punto se encontraba en cada fase, qué se hizo para intentar solucionar los problemas, y cómo quedo la electrónica asociada. Este punto nos llevará al montaje final del robot, al que hemos bautizado con el nombre de Linebot. Por último se analizará el funcionamiento del robot, en particular cómo afronta los retos y toma las decisiones a partir de los estímulos de entrada que recibe, y las mejoras al diseño introducidas de cara a mejorar su rendimiento. Els robots seguidors són apassionants, ja que són la porta d'entrada a la robòtica, i a més permeten a la persona que els realitza tenir la llibertat de poder-ho dissenyar utilitzant una variada gama de tecnologia. En aquest projecte s'intentarà millorar el sistema de detecció d'un seguidor de línies, passant dels sensors d'infraroigs a un sensor CCD. Per això, i en primer lloc, s'analitzaran els diferents tipus de robots seguidors de línia, quines tecnologies típiques s'utilitzen, i les millores que es poden aplicar. Posteriorment es detallaran les decisions que s'han pres en aquest projecte, què tecnologies es volen utilitzar, com es van a utilitzar, i quins problemes s'han tingut al implementar-les. Seguirem amb el desenvolupament específic del robot detallant: en quin punt es trobava cada fase, què es va fer per intentar solucionar els problemes, i com va quedar l'electrònica associada. Aquest punt ens portarà al muntatge final del robot, al que hem batejat amb el nom de Linebot. Per últim s'analitzarà el funcionament del robot, en particular com afronta els reptes i pren les decisions a partir dels estímuls d'entrada que rep, i les millores al disseny introduïdes de cara a millorar el seu rendiment.

Romeo as humanoid robot, which has for main goal help people who has some kind of disability, needs to be capable to grasp objects. Considering that it has led to do this first approach to grasp with Romeo. Moreover, it is wanted to grasp object which has been recognised by the RTM software (Recognition, Tracking and Modelling Objects) with a Romeo camera or an external one. This approach is included in the ROS framework as an independent package named romeo_grasper whose code is free to be shared or improved. The general idea of the approach starts with the RTM software getting the position, on camera reference, of the object, which has to be previously modelled or at least in the database. Then, this pose is transformed on robot reference and sent to MoveIt, that combined with the Rviz simulator and some ROS packages for Romeo, moves the arm of the robot to an optimal position for the grasp. Currently, it is used the IK solver from the KDL library, but here it is also explained how has been tried to implement the IKFast solver on Romeo, but without success. Finally, it is achieved to make work together all the systems, but the grasp has a some imprecision, producing that sometime it cannot be accomplish. However, in the experiments, it has been discerned where this inaccuracy comes from and it has been proposed some ways to reduce it. Furthermore, some guidelines has been set to lead Romeo to achieve a grasp using machine learning and allowing it to accomplish its goal, help the ones who need it

El trabajo presentado se enmarca en una iniciativa global que tiene como objeto la recuperacion de la plataforma robotica AIBO de Sony. Para demostrar las prestaciones de la arquitectura propuesta y la viabilidad del robot AIBO como plataforma robotica util, se han escogido algoritmos de aprendizaje por refuerzo muy novedosos como implementacion en la tarea de mantener el equilibrio ante movimientos indeseados en la base de apoyo del robot. El robot AIBO puede ser controlado de forma permanente con un tiempo de respuesta adecuado para la tarea.

Nowadays have already passed more than two years since PATRICIA research project about using pet robots to reduce pain and anxiety in hospitalized children was started and the advances made are more than significant. Patients, parents, nurses, psychologists, engineers... all of them have adopted with illusion Pleo robot, a kind of robotic pet, working hard looking for new procedures and new solutions to the current pediatrics diseases. From this work, a technological contribution is provided going one step beyond to what seems a long path. Concretely, it is wanted to develop a system to wirelessly communicate with Pleo in order to help the coordinator who leads the therapy with the kid, to understand and control Pleo’s behaviour at any moment. This article explains how this technological part is being developed and obtained technical results.

El trabajo tiene por objetivo implementar un conjunto de modulos de software que permitan integrar la plataforma robotica AIBO en el marco de trabajo ROS (Robot Operating System). El problema principal radica en el metodo utilizado para comunicarlos. Inicialmente se estudiaron los diferentes software alternativos con los que poder operar. Luego se compararon varios metodos de programacion, de forma que un analisis cuantitativo permitio decidir el metodo mas adecuado. Finalmente se implementaron los modulos que facilitan la integracion con ROS. Por un lado el modulo de control, al que se le han aplicado unos criterios de valoracion y se ha puesto a prueba su funcionamiento, y por otro el modulo que facilita la integracion de un modelo de visualizacion 3D. Con todo este trabajo desarrollado, se realizo la aplicacion de sincronizacion de movimientos de un par de robots AIBO y se realizaron experimentaciones que muestran la bondad del trabajo desarrollado.

This article describes an exploratory study of social human-robot interaction with the experimental robotic platform MASHI. The experiences were carried out in La Bobila Cultural Center in Barcelona, Spain to study the visit

En este documento se realiza una introduccion al trabajo realizado con el robot AIBO, de Sony, para la obtencion de imagenes, asi como su tratamiento con el objetivo que el robot navegue de forma autonoma mediante algoritmos de vision artificial. A nivel practico, el principal punto de interes del articulo se centra en el entorno de programacion, que se ha exportado al marco de ROS (Robot Operating System), de forma que se pueda hacer uso de la extensa biblioteca de algoritmos ya desarrollados en este entorno.

Simultaneous localization and mapping (SLAM) is an important problem to solve in robotics theory in order to build truly autonomous mobile robots. This work presents a novel method for implementing a SLAM system based on a single camera sensor. The SLAM with a single camera, or monocular SLAM, is probably one of the most complex SLAM variants. In this case, a single camera, which is freely moving through its environment, represents the sole sensor input to the system. The sensors have a large impact on the algorithm used for SLAM. Cameras are used more frequently, because they provide a lot of information and are well adapted for embedded systems: they are light, cheap and power-saving. Nevertheless, and unlike range sensors, which provide range and angular information, a camera is a projective sensor providing only angular measurements of image features. Therefore, depth information (range) cannot be obtained in a single step. In this case, special techniques for feature system-initialization are needed in order to enable the use of angular sensors (as cameras) in SLAM systems. The main contribution of this work is to present a novel and robust scheme for incorporating and measuring visual features in filtering-based monocular SLAM systems. The proposed method is based in a two-step technique, which is intended to exploit all the information available in angular measurements. Unlike previous schemes, the values of parameters used by the initialization technique are derived directly from the sensor characteristics, thus simplifying the tuning of the system. The experimental results show that the proposed method surpasses the performance of previous schemes.

Aquest projecte consisteix en el disseny, construcció i programació de dos robots seguidors de línia capaços de resoldre qualsevol laberint que compleixi una sèrie de restriccions. Els dos robots seran totalment independents, essent el primer robot l’encarregat de trobar el final del laberint, i guiar al segon sense cap error en les seves decisions. Per a guiar-lo, els robots es comunicaran mitjançant un mòdul sense fils. Per a la tria del camí correcte, el segon robot preguntarà en cada intersecció quin es el camí correcte a prendre al robot que coneix la solució. Aquest projecte està també enfocat cap a una ampliació d’aquest per a aconseguir una cooperació entre tres robots. És per això que els robots estan preparats mecànicament per a aquesta ampliació, i es pot trobar un esbós del programa.

A major focus for children’s quality of life programs in hospitals is improving their experiences during procedures. In anticipation of treatment, children may become anxious and during procedures pain appears. The aim of this article is to introduce a proposal to design pioneering techniques based on the use of social robots to improve the patient experience by eliminating or minimizing pain and anxiety. According to this proposed challenge, this research aims to design and develop specific human-social robot interaction with pet robots. Robot interactive behavior will be designed based on modular skills using soft-computing paradigms.

This work deals with the problem of planning the movements of a two-hand system in order to grasp an object with one hand and using the other to remove potential obstacles. The approach is based on a Probabilistic Road Map that does not rule out samples with collisions with removable objects but instead classifies them according to the collided obstacle(s), and allows the search of free paths with the indication of which objects must be removed from the work-space to make the path be valid. The approach has been implemented and some examples are presented in this work.

An exportable and robust system using only camera images is proposed for path execution in robot navigation. Motion information is extracted in the form of optical flow from SURF robust descriptors of consecutive frames, so the method is called SURF flow. This information is used to correct robot displacement when a straight forward path command is sent to the robot, but it is not really executed due to several robot and environmental concerns. The proposed system has been successfully tested on the legged robot Aibo.

En el proyecto singular estratégico Avanza2 ACROSS (AutoConfigurable Robots for Social Services) se definen tres escenarios para validar los desarrollos experimentales: (i) marketing, (ii) niños con problemas psicoafectivos y (iii) vida independiente. A su vez, estos escenarios permiten definir diferentes casos de uso, siguiendo un criterio de funcionalidades del robot. Atendiendo a la aproximación general de robótica cognitiva y de conicimiento del contexto del proyecto, este documento tiene por objetivo la definición de unas reglas de comportamiento robóticas para cada diversos de esos casos de uso. Las reglas se definen en función de las características técnicas del robot y las necesidades de usuario en cada escenario y caso de uso. Para ello se ha realizado un estudio sobre la recogida continuada de información de los usuarios antes y después de su interacción experimental con las plataformas robóticas.

This work presents an intergated strategy for planning and learning suitable to execute tasks with robotic platforms without any previous task specification. The approach rapidly learns planning operators from few action experiences using a competitive strategy where many alternatives of cause-effect explanations are evaluated in parallel, and the most successful ones are used to generate the operators. The system operates without task interruption by integrating in the planning-learning loop a human teacher that supports the planner in making decisions. All the mechanisms are integrated and synchronized in the robot using a general decision-making framework.

Projecte final de carrera realitzat en col.laboració amb ETSIT UPM El presente Proyecto Fin de Carrera se haya dividido en dos partes diferenciadas y correspondientes a: Análisis y diseño de un Robot móvil autónomo para un entorno inteligente: en esta parte se parte del prototipo de un robot comercial, el robot X80 de Dr. Robot Inc. y, aprovechando su estructura y algunos de los sensores de la red, se construye desde cero un robot móvil autónomo con una red de cerca de 40 sensores encuestados por I2C. El robot estará basado en un agente reactivo con arquitectura de subsunción y se comunicará con el ambiente inteligente del Laboratorio de Sistemas Integrados mediante un módulo Wi USB. La comunicación le permitirá intercambiar con el entorno información acerca de su estado y recibir órdenes del mismo. Análisis y diseño de la localización e integración de un robot móvil en un entorno inteligente: en esta segunda parte se partirá del prototipo del robot creado en la primera. Al robot se le añadirá, por encima de la capa reactiva, una capa deliberativa basada en la arquitectura AuRA que le permita llevar a cabo funciones de alto nivel. De toda la arquitectura se diseñaría la parte de la localización y mapeado simultáneo del robot en su entorno, así como la plani cación de rutas sencillas (búsqueda de caminos entre dos puntos de un mapa). También se diseñaría un sistema simple para llevar a cabo el reconocimiento de los usuarios que se mueven por el entorno inteligente, mediante la càmara de a bordo preinstalada en el robot X80. En este proyecto se prestaría especial atención a la utilización de los recursos del sistema del robot, que utilizará como placa de inteligencia bàsica un System on Module (SoM) con un kernel linux. Uno de los requisitos principales sería el de mantener siempre la reactividad de robot y su buena adaptación del entorno, evitando que los programas puedan sobrecargar el sistema.

Se trabaja sobre una instalación artística que trata de aumentar la atención de las personas sobre su columna vertebral y el efecto que la gravedad tiene sobre la estructura corporal en general; para ello se tienen un conjunto de 10 robots autónomos con la función de interactuar entre ellos y con una persona en el exterior. Se quiere que cuando un usuario entre al campo de los robots uno de ellos (robot maestro) lo detecte, lo localice y vaya hacia él, cuando lo logré encontrar llamará a los otros robots (robots esclavos) que se colocaran en fila. El primero seguirá a la persona a través de un sensor de temperatura y los demás se seguirán entre ellos por infrarrojo. Cuando el usuario se mueva la columna de robots deberá moverse también hacia la persona. Todos los robots de la instalación disponen de sensores de infrarrojos (emisores y receptores) y de temperatura, así como de módulos de comunicación radio. Los robots son totalmente operativos a nivel de hardware, pero las pruebas de software que se han llevado a cabo indican que es necesario mejorar considerablemente los algoritmos implementados de navegación y seguimiento. Se tratará de implementar algoritmos eficientes de navegación y seguimiento que permitan la interacción de una instalación multi-robot con los usuarios de la misma. A partir de las medidas físicas obtenidas de los emisores y receptores de infrarrojos de los que disponen los robots se tratará de seleccionar los algoritmos de predicción y seguimiento de trayectoria más eficientes, para a continuación llevar a cabo su implementación sobre los mismos. En la actualidad no se cuenta con ninguna interfaz completamente confiable para la interacción con los robots de la instalación, y resulta muy importante el desarrollo de una aplicación que permita la comunicación para el monitoreo y control de los mismos para la implementación y prueba de los algoritmos de seguimiento. Dicha aplicación permitirá además la prueba de los algoritmos desarrollados antes incluso de ser implementados en los robots.

El presente proyecto pretende realizar la construcción de un microbot de exploración que sea autónomo. Se ha incorporado una micro cámara y sensores para recoger los datos y la información del hábitat en el que se desplaza para posteriormente enviarlo a un equipo remoto. La información que recoge se muestra en una aplicación realizada en el lenguaje de programación Visual Basic. El sistema de tracción del microbot esta formada por dos motores bipolares paso a paso, que ha sido configurado en modo diferencial. El apoyo lo realiza mediante una tercera rueda loca descentrada. En el sistema de control se ha optado por la utilización de microcontroladores en configuración maestro - esclavo, basado en la topología de comunicación del bus Serial Peripheral Interface (Bus SPI). La estructura física y el diseño de la creación del microbot se ha realizado a medida, puesto que las estructuras de tipo meccano o lego limitan la flexibilidad.

This is a post-peer-review, pre-copyedit version of an article published in ROBOT - Iberian Robotics Conference. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-35990-4_31”. Autonomous indoor robots are supposed to accomplish tasks, like serve a cup, which involve manipulation actions, where task and motion planning levels are coupled. In both planning levels and execution phase, several source of failures can occur. In this paper, an interpretation ontology covering several sources of failures in automated planning and also during the execution phases is introduced with the purpose of working the planning more informed and the execution prepared for recovery. The proposed failure interpretation ontological module covers: (1) geometric failures, that may appear when e.g. the robot can not reach to grasp/place an object, there is no free-collision path or there is no feasible Inverse Kinematic (IK) solution. (2) hardware related failures that may appear when e.g. the robot in a real environment requires to be re-calibrated (gripper or arm), or it is sent to a non-reachable configuration. (3) software agent related failures, that may appear when e.g. the robot has software components that fail like when an algorithm is not able to extract the proper features. The paper describes the concepts and the implementation of failure interpretation ontology in several foundations like DUL and SUMO, and presents an example showing different situations in planning demonstrating the range of information the framework can provide for autonomous robots

Treball desenvolupat dins el marc del programa 'European Project Semester'. The robotics facilities at Oslo and Akershus University College of Applied Sciences (HiOA) have at their disposal an autonomous humanoid robot by the name of Nao. It is a commercial product developed by SoftBank Robotics for research and educational purposes, and an international project team was assembled for the European Project Semester (EPS) program as a means of assessing its competencies and bolstering them wherever possible. The team set forth with a mission to establish meaningful interactions between the robot and his physical surroundings while placing an emphasis on humanlike behavior to the fullest extent. A goal was soon initiated for Nao to play a rule-based game against a human opponent such that his vast array of available faculties could be tested and put to use in one cohesive demonstration. Achieving this goal meant making advances in artificial intelligence,computer vision and motion planning, first as separate entities that would later converge into a singular yet multifaceted display of abilities. At the end of the project, computer vision has been implemented to the point where pattern recognition can be performed with accuracy amidst awide variety of error-prone detection scenarios. Several artificial intelligence schemes have been explored and compared quantitatively to derive performance metrics for each. Proof-ofconcept movement strategies have also been derived, and best practices for each focus area are provided as a resource for any future researchers or project groups who stand to benefit from the discoveries made in this endeavor

This work presents a complete navigation architecture for an autonomous aerial robot. The proposed scheme consist of: i) a low-level controller for establishing the attitude and position of the vehicle, ii) a Simultaneous Localization and Mapping (SLAM) system, based in bearing (angular) measurements, which gives the robot the ability for navigating in unknown environments, and iii) a high-level motion control system which generates online trajectories. The high-level motion control system (MCS), which represents the main contribution of this work, is inspired by the behavior-based control strategies. The MCS takes as input a very high level mission target (e.g. “explore as much as you can”) and generates online trajectories according to the mission, but at the same time minimizing uncertainty in the estimations in order to maintain the integrity of the robot. The proposed architecture is explained for simplified 3DOF dynamics, but it could be extended in a straightforward manner in order to be applied to full dynamics. Several simulations are included in order to show the performance of the proposed scheme.

In recent years, many researchers have addressed the issue of making Unmanned Aerial Vehicles (UAVs) more and more autonomous. In this context, the state estimation of the vehicle position is a fundamental necessity for any application involving autonomy. However, the problem of position estimation could not be solved in some scenarios, even when a GPS signal is available, for instance, an application requiring performing precision manoeuvres in a complex environment. Therefore, some additional sensory information should be integrated into the system in order to improve accuracy and robustness. In this work, a novel vision-based simultaneous localization and mapping (SLAM) method with application to unmanned aerial vehicles is proposed. One of the contributions of this work is to design and develop a novel technique for estimating features depth which is based on a stochastic technique of triangulation. In the proposed method the camera is mounted over a servo-controlled gimbal that counteracts the changes in attitude of the quadcopter. Due to the above assumption, the overall problem is simplified and it is focused on the position estimation of the aerial vehicle. Also, the tracking process of visual features is made easier due to the stabilized video. Another contribution of this work is to demonstrate that the integration of very noisy GPS measurements into the system for an initial short period of time is enough to initialize the metric scale. The performance of this proposed method is validated by means of experiments with real data carried out in unstructured outdoor environments. A comparative study shows that, when compared with related methods, the proposed approach performs better in terms of accuracy and computational time.

Aquest projecte consisteix en el disseny, construcció i programació de dos robots seguidors de línia capaços de resoldre qualsevol laberint que compleixi una sèrie de restriccions. Els dos robots seran totalment independents, essent el primer robot l’encarregat de trobar el final del laberint, i guiar al segon sense cap error en les seves decisions. Per a guiar-lo, els robots es comunicaran mitjançant un mòdul sense fils. Per a la tria del camí correcte, el segon robot preguntarà en cada intersecció quin es el camí correcte a prendre al robot que coneix la solució. Aquest projecte està també enfocat cap a una ampliació d’aquest per a aconseguir una cooperació entre tres robots. És per això que els robots estan preparats mecànicament per a aquesta ampliació, i es pot trobar un esbós del programa.

Robots and automation systems have become a valuable partner in several facets of human life: from learning and teaching, to daily working, including health monitoring and assistance. So far, these appealing robot-based applications are restricted to conduct repetitive, yet useful, tasks due to the reduced individual robots’ capabilities in terms of processing and computation. This concern prevents current robots from facing more complex applications related to understanding hu- man beings and perceiving their subtle feelings. Such hardware limitations have been already found in the computer science field. In this domain, they are currently being addressed using a new resource exploitation model coined as cloud computing, which is targeted at enabling massive storage and computation using smartly connected and inexpensive commodity hardware. The purpose of this paper is to propose a cloud-based robotics architecture to effectively develop complex tasks related to hospitalized children assistance. More specifically, this paper presents a multi-agent learning system that combines machine learning and cloud computing using low-cost robots to (1) collect and perceive children status, (2) build a human-readable set of rules related to the child-robot relationship, and (3) improve the children experience during their stay in the hos- pital. Conducted preliminary experiments proof the feasibility of this proposal and encourage practitioners to work towards this direction.