Interfaz de usuario de un sistema multi-robot de reconocimiento de superficies con Google Earth

Els sistemes multi-robot de reconeixement de superfícies es poden utilitzar tant per a l'exploració de llocs remots, de difícil accés o perillosos. Normalment, els robots no són autònoms, depenen d'operadors humans per dirigir-los. La informació que capten ha de ser processada i mostrada a l'usuari o usuària del sistema de forma intel·ligible. Un exemple d'aplicació seria el d'un sistema multirobot format per diversos helicòpters no tripulats que proporciona informació d'una àrea que ha patit algun desastre. El sistema informàtic recolliria la informació i la transmetria al coordinador de l'operatiu d'assistència de l'emergència. La idea del projecte és la de combinar la informació proporcionada pel sistema multi-robot amb la de la zona disponible a Google Earth i fer d'aquesta eina l'interfície d'usuari de l'aplicació.

Anàlisi, disseny i construcció d'un robot educatiu basat en microcontrolador AVR-ATMEGA

Aquest projecte presenta el disseny, construcció i programació d’un robot autònom, com a base per una proposta educativa. Per aconseguir aquest objectiu s’ha dotat el robot d’una unitat de procés, un sistema de locomoció i un seguit de sensors que proporcionaran a la unitat informació respecte l’entorn. Per gestionar totes aquestes funcionalitats, s’ha fet servir un sistema operatiu en temps real capaç de gestionar amb efectivitat les tasques que puguin ser executades pel robot. Finalment, s’ha exposat una detallada descripció dels costos per una producció de volum mig i de caire merament educatiu.

Estudi: microcontrolador LPC2119 - ARM. Cas pràctic: anàlisi, disseny i construcció d'un robot amb pèndul invertit

Estudi de l'arquitectura i prestacions del microcontrolador LPC2119 tot implementant la proposta d’un cas pràctic. En la besant teòrica, es fa una anàlisi acurada del dispositiu LPC2119, enumerant les principals característiques i exposant les seves parts, aprofundint sobretot en l’arquitectura i core ARM que incorpora. En l'àmbit pràctic, s'introdueix el problema del pèndul invertit com a proposta per a ser integrada sobre un robot que exploti les funcionalitats del dispositiu integrat presentades a l'estudi teòric.

Utilització del robot mòbil com a eina d'aprenentatge en ensenyaments tècnics

En aquest Projecte de Millora de la Qualitat Docent es descriu el disseny, la construcció i la utilització d’un robot mòbil com a eina docent en titulacions d’Enginyeria. El robot mòbil té com a element de control un PC portàtil convencional per tal de facilitar el procés d’aprenentatge de l’alumnat estigui centrat en l’objectiu de les pràctiques i no en el funcionament i control del robot. A més a més, el robot disposa d’un elevat nombre de sensors i actuadors per tal d’oferir un elevat grau d’interdisciplinaritat.

Cortical mapping of the neuronal circuits modulating the muscle tone. Introduction to the electrophysiological treatment of the spastic hand.

L’objectiu d’aquest estudi es investigar l’organització cortical junt amb la connectivitat còrtico-subcortical en subjectes sans, com a estudi preliminar. Els mapes corticals s’han fet per TMS navegada, i els punts motors obtinguts s’han exportant per estudi tractogràfic i anàlisi de las seves connexions. El coneixement precís de la localització de l’àrea cortical motora primària i les seves connexions es la base per ser utilitzada en estudis posteriors de la reorganització cortical i sub-cortical en pacients amb infart cerebral. Aquesta reorganització es deguda a la neuroplasticitat i pot ser influenciada per els efectes neuromoduladors de la estimulació cerebral no invasiva.

Eye tracking for on-screen reading

A medida que avanza la tecnolog a, cada vez son m as comunes los libros digitales. Por eso, existen varias formas de mejorar la experiencia de lectura del usuario, como mostrar la de nici on de una palabra que resulte dif cil, o resaltar lo importante del texto cuando se pasa la vista por encima. En este proyecto, se ha investigado la base de esto con la ayuda de un Eye Tracker. Se ha implementado una clasi caci on en palabras f aciles y dif ciles dependiendo de c omo una persona lee, y una forma de saber si se est a leyendo el texto o pasando la vista por encima.

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

A system to evaluate the accuracy of a visual mosaicking methodology

When underwater vehicles navigate close to the ocean floor, computer vision techniques can be applied to obtain motion estimates. A complete system to create visual mosaics of the seabed is described in this paper. Unfortunately, the accuracy of the constructed mosaic is difficult to evaluate. The use of a laboratory setup to obtain an accurate error measurement is proposed. The system consists on a robot arm carrying a downward looking camera. A pattern formed by a white background and a matrix of black dots uniformly distributed along the surveyed scene is used to find the exact image registration parameters. When the robot executes a trajectory (simulating the motion of a submersible), an image sequence is acquired by the camera. The estimated motion computed from the encoders of the robot is refined by detecting, to subpixel accuracy, the black dots of the image sequence, and computing the 2D projective transform which relates two consecutive images. The pattern is then substituted by a poster of the sea floor and the trajectory is executed again, acquiring the image sequence used to test the accuracy of the mosaicking system

Estimating the motion of an underwater robot from a monocular image sequence

When underwater vehicles perform navigation close to the ocean floor, computer vision techniques can be applied to obtain quite accurate motion estimates. The most crucial step in the vision-based estimation of the vehicle motion consists on detecting matchings between image pairs. Here we propose the extensive use of texture analysis as a tool to ameliorate the correspondence problem in underwater images. Once a robust set of correspondences has been found, the three-dimensional motion of the vehicle can be computed with respect to the bed of the sea. Finally, motion estimates allow the construction of a map that could aid to the navigation of the robot

Detection of matchings in a sequence of underwater images through texture analysis

This paper presents an approach to ameliorate the reliability of the correspondence points relating two consecutive images of a sequence. The images are especially difficult to handle, since they have been acquired by a camera looking at the sea floor while carried by an underwater robot. Underwater images are usually difficult to process due to light absorption, changing image radiance and lack of well-defined features. A new approach based on gray-level region matching and selective texture analysis significantly improves the matching reliability

VLSI architecture for an Underwater Robot Vision System

It is well known that image processing requires a huge amount of computation, mainly at low level processing where the algorithms are dealing with a great number of data-pixel. One of the solutions to estimate motions involves detection of the correspondences between two images. For normalised correlation criteria, previous experiments shown that the result is not altered in presence of nonuniform illumination. Usually, hardware for motion estimation has been limited to simple correlation criteria. The main goal of this paper is to propose a VLSI architecture for motion estimation using a matching criteria more complex than Sum of Absolute Differences (SAD) criteria. Today hardware devices provide many facilities for the integration of more and more complex designs as well as the possibility to easily communicate with general purpose processors

Omnidirectional Depth Computation from a Single Image

Omnidirectional cameras offer a much wider field of view than the perspective ones and alleviate the problems due to occlusions. However, both types of cameras suffer from the lack of depth perception. A practical method for obtaining depth in computer vision is to project a known structured light pattern on the scene avoiding the problems and costs involved by stereo vision. This paper is focused on the idea of combining omnidirectional vision and structured light with the aim to provide 3D information about the scene. The resulting sensor is formed by a single catadioptric camera and an omnidirectional light projector. It is also discussed how this sensor can be used in robot navigation applications