Integració i estudi d’un conjunt de propulsors per un robot submarí

L’objectiu d’aquest projecte/treball fi de carrera es estudiar els propulsors i el seu protocol de comunicació proporcionant informació útil a l’hora de dissenyar i construir el robot subaquàtic que implementi els propulsors

Odometria i planificació de trajectòries amb el robot e-puck

En el laboratori docent de robòtica s'utilitzen robots mòbils autònoms per treballar aspectes relacionats amb el posicionament, el control de trajectòries, la construcció de mapes... Es disposa de cinc robots comercials anomenats “e-puck”, que es caracteritzen per les seves dimensions reduïdes, dos motors i un conjunt complet de sensors. Aquests robots es programen en C++ utilitzant el simulador Webots, que disposa d'un conjunt de llibreries per programar el robot. També es disposa d'un entorn de proves on els robots es poden moure i evitar obstacles. Donat el poc temps que disposen els estudiants que realitzen pràctiques en aquest laboratori, és d'interès desenvolupar un software que contingui ja el posicionament del robot mitjançant odometria i també varis algoritmes de control de trajectòries. Per últim, en el laboratori es disposa de càmeres i targes d'adquisició de dades. Així doncs els objectius que s'han proposat per el projecte són: 1. Estudi de la documentació i software proporcinats pels fabricants del robot i de l'entorn Webots; 2. Programació del software de l'odometria i realització de proves per comprovar-ne la precisió; 3. Disseny, programació i verificació del software dels algoritmes de planificació de trajectòries. Realització d'experiments per a comprovar-ne el funcionament i 4. Disseny, programació i verificació d'un sistema de visió artificial que permeti conèixer la posició absoluta del robot en l'entorn

Programació del robot e-puck amb Microsoft Robotics Studio

Microsoft Robotics Studio (MRS) és un entorn per a crear aplicacions per a robots utilitzant una gran varietat de plataformes hardware. Conté un entorn de simulació en el que es pot modelar i simular el moviment del robot. Permet també programar el robot, i executar-lo en l’entorn simulat o bé en el real. MRS resol la comunicació entre els diferents processos asíncrons que solen estar presents en el software de control d’un robot: processos per atendre sensors, actuadors, sistemes de control, comunicacions amb l’exterior,... MRS es pot utilitzar per modelar nous robots utilitzant components que ja estiguin disponibles en les seves llibreries, o també permet crear component nous. Per tal de conèixer en detall aquesta eina, seria interessant utilitzar-la per programa els robots e-pucks, uns robots mòbils autònoms de petites dimensions que disposen de dos motors i un complet conjunt de sensors. El que es vol és simular-los, realitzar un programa de control, realitzar la interfície amb el robot i comprovar el funcionament amb el robot real

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

Vision-based localization of an underwater robot in a structured environment

This paper presents a vision-based localization approach for an underwater robot in a structured environment. The system is based on a coded pattern placed on the bottom of a water tank and an onboard down looking camera. Main features are, absolute and map-based localization, landmark detection and tracking, and real-time computation (12.5 Hz). The proposed system provides three-dimensional position and orientation of the vehicle along with its velocity. Accuracy of the drift-free estimates is very high, allowing them to be used as feedback measures of a velocity-based low-level controller. The paper details the localization algorithm, by showing some graphical results, and the accuracy of the system

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

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

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

Augmented state Kalman filtering for AUV navigation

Addresses the problem of estimating the motion of an autonomous underwater vehicle (AUV), while it constructs a visual map ("mosaic" image) of the ocean floor. The vehicle is equipped with a down-looking camera which is used to compute its motion with respect to the seafloor. As the mosaic increases in size, a systematic bias is introduced in the alignment of the images which form the mosaic. Therefore, this accumulative error produces a drift in the estimation of the position of the vehicle. When the arbitrary trajectory of the AUV crosses over itself, it is possible to reduce this propagation of image alignment errors within the mosaic. A Kalman filter with augmented state is proposed to optimally estimate both the visual map and the vehicle position

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Visual SLAM for 3D large-scale seabed acquisition employing underwater vehicles

This paper presents a novel technique to align partial 3D reconstructions of the seabed acquired by a stereo camera mounted on an autonomous underwater vehicle. Vehicle localization and seabed mapping is performed simultaneously by means of an Extended Kalman Filter. Passive landmarks are detected on the images and characterized considering 2D and 3D features. Landmarks are re-observed while the robot is navigating and data association becomes easier but robust. Once the survey is completed, vehicle trajectory is smoothed by a Rauch-Tung-Striebel filter obtaining an even better alignment of the 3D views and yet a large-scale acquisition of the seabed

Estudi del model dinàmic d’un robot submarí d’intervenció (IAUV) composat per un vehicle i un braç manipulador

Fins a la data d’avui, el grup VICOROB de la Universitat de Girona ha desenvolupat diversos vehicles autònoms (GARBÍ, URIS i ICTINEU). El projecte que comença aquest any té com objectiu desenvolupar un nou vehicle submarí autònom amb capacitat d’intervenció (I-AUV) gràcies a un braç manipulador. Aquest projecte final de carrera té com objectiu desenvolupar en entorn MATLAB un simulador d’un I-AUV, format per un AUV i un braç manipulador de n graus de llibertat per tal d’avaluar les reaccions dels moviments del braç, amb càrrega i sense, sobre el robot, i viceversa

Disseny de controladors òptims per al robot Pioneer

Aquest projecte titulat: “Disseny de controladors òptims per al robot Pioneer”, té com a funció incloure en la recerca, que ja està iniciada, del control del Robot Pioneer 2DX, una nova versió d’agents go to per al funcionament del robot. La problemàtica que ens trobem és sobretot per al primer controlador. Fins ara el sistema multi-agent fet, feia servir un agent go to que generava la trajectòria a seguir i la controlava mitjançant un PID. Introduint un mètode geomètric com és el cas del pure pursuit la cosa es complica ja que és més complex l’ajustament del funcionament d’aquest. Centrant-nos en canvi el cas del segon controlador el problema es simplifica ja que l’ajustatge d’aquest mateix es pot realitzar de manera empírica i la problemàtica per a la situació en concret es millora amb major facilitat. És per aquest motiu, sobretot pel primer controlador, que s’han hagut de realitzar algunes modificacions en el plantejament del projecte al llarg d’aquest. En un principi estava pensat crear aquest controlador a través de Matlab® mitjançant l’eina Simulink® però per problemes de software en un moment donat hem hagut de redirigir el projecte cap al llenguatge base de l’estructura multi-agent com és el C++. Per aquest motiu també s’ha hagut de prescindir de la implementació d’aquests també en l’estructura LabView®.