TurtleBot3-robotit

Insinöörityössä tutustuttiin ROS-väliohjelmistoon (Robot Operating System) ja otettiin käyttöön ROS:ia hyödyntävät TurtleBot3-robotit. Robotit tulivat opetuskäyttöön Metropolia Ammattikorkeakoululle. Työ tehtiin kokoamalla robotit, asentamalla niihin käyttöjärjestelmät sekä muu ohjelmisto. Ohjelmisto asennettiin myös tietokoneelle, jolta robotteja ohjataan. Tietokone asetettiin robottien Wi-Fi-yhteyspisteeksi ja aikapalvelimeksi. Työssä tutkittiin myös karttojen tekemistä roboteilla SLAM-menetelmää käyttäen ja navigointia luoduilla kartoilla. Robotit saatiin otettua käyttöön ja niillä saatiin tehtyä kaksi- ja kolmiulotteisia karttoja tietynlaisista ympäristöistä. Navigointi luoduilla kaksiulotteisilla kartoilla onnistui, vaikka olikin hidasta. Työn tekemistä hankaloitti se, että robottien ohjelmisto jumiutuu helposti. Jumittaminen estää robottien käytön vaativissa sovelluksissa.The aim of this study was to get acquainted with ROS (Robot Operating System) and to deploy the ROS-based TurtleBot3 robots for teaching purposes. The work was commissioned by Metropolia University of Applied Sciences. The project was done by assembling the robots and installing operating systems and other software on them. Software was also installed on the remote PC which was used to control the robots. The computer was set as a Wi-Fi access point and a time server for the robots. The study also investigated 2D and 3D mapping with the robots using the SLAM method. Navigation tasks were done on the maps. The robots were deployed and are ready for teaching purposes. The robots could create two and three-dimensional maps of certain types of environments. Navigation using the 2D maps works slow, but reliably. The study was complicated by continuous freezing of the robots. The robots work well for studying purposes, but freezing avoids using the robots in demanding applications

Development of low cost autonomous wheelchair using gps for outdoor purposes

Electric wheelchair has been widely used to facilitate and minimize the user’s effort to move independently. Users prefer to control the movement of the wheelchair on their own without any assistance. Although electric wheelchairs are considered a good solution to minimise the effort in independently moving the wheelchair, but unfortunately, electric wheelchairs are expensive in Malaysia. Moreover, most of the available electric wheelchairs in the market use only the joystick as control device. However, the joystick is not suitable for most cases. For example, blind users, users with mental disorders or with both hands paralyzed, are unable to hold and control the joystick. Such users still need to be assisted by others. However, other people will not always be available to help due to any constraints. Using other means of control devices may partially solve the issues but may not be entirely resolved. Therefore, wheelchair needs some improvement utilising smarter and low cost control system that can resolve some critical cases for example the users that are unable to use both their hands and legs. This research main focuses on developing a control system to allow wheelchairs to move autonomously from one point to another using Global Positioning System (GPS) while saving the cost to make it affordable for the users. The main problem in building an autonomous system is the accuracy and consistency of GPS reading. To solve that problem a simple algorithm is developed to improve the accuracy in positioning and path planning for the wheelchair. The averaging technique was applied in positioning to improve the accuracy and consistency of the GPS reading. The GPS positioning becomes more accurate as the averaging technique reduced the GPS reading to two consistent readings instead of five different readings. In terms of accuracy, the distance between the actual point and the GPS measured point had decreased from 4 meters to only 3 meters. The stop angle was adjusted by changing the setting for the stop angle’s constant because the wheelchair does not immediately stop at the desired turning point due to the Law of Inertia. The value of that constant has to be experimentally set according to error in turning angle. The suggested solution is by integrating rotary encoder with the compass. The constant kp= 60 pulses was applied in straight movement correction, and can be seen that the wheels always trying to balance each other. Experiments have been conducted to test the ability of the system and fulfil the task of reaching a pre-stated destination accurately. This wheelchair can be used for outdoor movement as the GPS is more accurate outside of the building. For instance, the users want to go to the nearest clinic or park within 1 kilometre from their home. This will save time as they don’t need to wait to seek for assistance

Differential evolution Markov chain filter for global localization

A key challenge for an autonomous mobile robot is to estimate its location according to the available information. A particular aspect of this task is the global localization problem. In our previous work, we developed an algorithm based on the Differential Evolution method that solves this problem in 2D and 3D environments. The robot’s pose is represented by a set of possible location estimates weighted by a fitness function. The Markov Chain Monte Carlo algorithms have been successfully applied to multiple fields such as econometrics or computing science. It has been demonstrated that they can be combined with the Differential Evolution method to solve efficiently many optimization problems. In this work, we have combined both approaches to develop a global localization filter. The algorithm performance has been tested in simulated and real maps. The population requirements have been reduced when compared to the previous version.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU.Publicad

Using the Jensen-Shannon, density power, and Itakura-Saito divergences to implement an evolutionary-based global localization filter for mobile robots

One of the most demanding skills for a mobile robot is to be intelligent enough to know its own location. The global localization problem consists of obtaining the robot's pose (position and orientation) in a known map if the initial location is unknown. This task is addressed applying evolutionary computation concepts (Differential Evolution). In the current approach, the distances obtained from the laser sensors are combined with the predicted scan (in the known map) from possible locations to implement a cost function that is optimized by an evolutionary filter. The laser beams (sensor information) are modeled using a combination of probability distributions to implement a non-symmetric fitness function. The main contribution of this paper is to apply the probabilistic approach to design three different cost functions based on known divergences (Jensen-Shannon, Itakura-Saito, and density power). The three metrics have been tested in different experiments and the localization module performance is exceptional in regions with occlusions caused by different obstacles. This fact validates that the non-symmetric probabilistic approach is a suitable technique to be applied to multiple metrics.This work was supported by the Competitive Improvement of Drilling and Blasting Cycle in Mining and Underground-Works through New Techniques of Engineering, Explosives, Prototypes, and Advanced Tools, which is a Research and Development project undertaken by the following companies: Obras Subterr a neas, MaxamCorp Holding, Putzmeister Iberica, Subterra Ingenieria, Expace On Boards Systems, Dacartec Servicios Informaticos, and Cepasa Ensayos Geotecnicos

Localización y mapeado simultáneos en robótica mediante visión omnidireccional

Algoritmos de localización y SLAM para cámaras omnidireccionales que utilizan como balizas las luces del entorno, capaces de operar en tiempo real y bajo oclusiones frecuentes y severas

Probabilistische Methoden für die Roboter-Navigation am Beispiel eines autonomen Shopping-Assistenten

Abstract Autonomous navigation, in addition to interaction, is a basic ability for the operation of a mobile service robot. Here, important subskills are selfocalization, path planning, and motion control with collision avoidance. A further pre-condition for many navigation tasks ist the generation of an environment model from sensor observationa, often in combination with autonomous exploration. In this thesis, these challenges are considered in the context of the development of an interactive mobile shopping guide, which is able to provide information about the shop's products to customers of a home improvement store and guide them to the respective location. The focus of this work lies on the initial environment mapping. A method for Simultaneous Localization and Mapping (SLAM) has been developed, which in contrast to other comparable approaches does not assume the use of high-precision laser range scanners. Instead, sonar range sensors are used mainly, which feature an inferior spatial resolution and increased measurement noise. The resulting Map-Match-SLAM algorithm is based on the well known Rao-Blackwellized Particle Filter (RBPF), in combination with local maps for representation of most recent observations and and a map matching function for comparison of local and global maps. By adding a memory-effcient global map representation and dynamic adaption of the number of particles, online mapping is possible even under high state uncertainty resulting from the sensor characteristics. The use of local maps for representation of the observations and the sensor-independent weighting function make Map-Match-SLAM applicable for a wide range of different sensors. This has been demonstrated by mapping with a stereo camera and with a single camera, in combination with a depth-from-motion algorithm for pre-processing. Furthermore, a SLAM assistant has been developed, which is generating direction hints for the human operator during the mapping phase, in order to ensure a route that enables optimal operation of the SLAM algorithm. The assistant represents an intermediate step between purely manual mapping and completely autonomous exploration. A second main part of the work presented here are methods for the autonomous operation of the robot. For selflocalization, a map matching approach with local maps is used, similar to the proposed SLAM algorithm. Improvements of robustness and precision are achieved in combination with an existing visual localization approach which is using omnidirectional camera images. Path planning is done by the utilization of standard graph search algorithms. To that purpose, the grid cells of the global map are regarded as graph nodes. Comparitive analysis is presented for search algorithms with and without heuristics (A*/Dijkstra algorithm), for the specifcs of typical operation areas. Two different algorithms have been developed for motion control and collision avoidance: A reactive method, which is an enhancement of the existing Vector Field Histogram (VFH) approach, is experimentally compared with a new anticipative method based on sampling and stochastic search in the trajectory space. All the developed methods are employed on a team of shopping robots, which have been in permanent public test operation in a home improvement store for six months currently. The description of navigation methods is complemented by an overview of further software componentsof the robots, e.g. for Human-Robot-Interaction, and a detailed description of the control architecture for coordination of the subsystems. Analysis of long term test operation proves that all the applied methods are suitable for real world applications and that the robot is accepted and regarded as a valuable service by the customers.Die autonome Navigation stellt neben der Interaktionsfähigkeit eine Grundlage für die Funktion eines mobilen Serviceroboters dar. Wichtige Teilleistungen sind dabei die Selbstlokalisation, die Pfadplanung und die Bewegungssteuerung unter Vermeidung von Kollisionen. Eine Voraussetzung für viele Navigationsaufgaben ist zudem die Erstellung eines Umgebungsmodells aus sensorischen Beobachtungen, unter Umständen in Verbindung mit einer selbständigen Exploration. Diese Teilprobleme wurden in der vorgelegten Arbeit vor dem Hintergrund der Entwicklung eines interaktiven mobilen Shopping-Lotsen bearbeitet, welcher Kunden eines Baumarktes Informationen zu Produkten zur Verfügung stellen und sie auf Wunsch zum Standort der gesuchten Waren führen kann. Den methodischen Kern der Arbeit bildet die initiale Umgebungskartierung. Dafür wurde ein Verfahren zum Simultaneous Localization and Mapping (SLAM) entwickelt, welches im Gegensatz zu vergleichbaren Ansätzen nicht auf den Einsatz hochgenauer Laser-Range-Scanner ausgerichtet ist. Stattdessen wurden hauptsächlich Sonar-Sensoren benutzt, die sich durch eine wesentlich geringere räumliche Auflösung und höhere Messunsicherheit auszeichnen. Der entwickelte Map-Match-SLAM-Algorithmus beruht auf dem bekannten Rao-Blackwellized Particle Filter (RBPF), welcher mit einer lokalen Karte zur Repräsentation der aktuellen Umgebungsbeobachtungen sowie einer Map-Matching-Methode zum Vergleich der lokalen und globalen Karte kombiniert wurde. Durch eine speichereffiziente Darstellung der globalen Karte und dynamische Adaption der Partikel-Anzahl ist trotz der aus den sensorischen Beschränkungen resultierenden großen Zustandsunsicherheit die Online-Kartierung möglich. Durch die Transformation der Beobachtungen in eine lokale Karte und die sensorunabhängige Bewertungsfunktion ist das Map-Match-SLAMVerfahren für ein breites Spektrum unterschiedlicher Sensoren geeignet. Dies wurde exemplarisch durch die Kartierung unter Nutzung einer Stereo-Kamera-Anordnung und einer einfachen Kamera in Verbindung mit einem Depth-from-Motion-Verfahren gezeigt. Aufbauend auf dem Kartierungsalgorithmus wurde zudem ein SLAM-Assistent entwickelt, welcher während der Kartierungsphase Aktionsvorschläge für den menschlichen Bediener präsentiert, die eine optimale Funktion des SLAM-Algorithmus gewährleisten. Der Assistent stellt damit eine Zwischenstufe zwischen rein manueller Steuerung und komplett autonomer Exploration dar. Einen weiteren Schwerpunkt der Arbeit stellen die Verfahren für die autonome Funktion des Roboters dar. Für die Selbstlokalisation wird ebenso wie beim SLAM ein Map Matching mit lokalen Karten eingesetzt. Eine Verbesserung der Robustheit und Genauigkeit wird durch die Kombination dieses Ansatzes mit einem vorhandenen visuellen Selbstlokalisations-Verfahren auf Basis einer omnidirektionalen Kamera erzielt. Für die Bestimmung des optimalen Pfades zu einem Zielpunkt kommen Standard-Algorithmen zur Pfadsuche in Graphen zum Einsatz, die Zellen der Karte werden dazu als Graphknoten interpretiert. Die Arbeit präsentiert vergleichende Untersuchungen zur Effizienz von Algorithmen mit und ohne Suchheuristik (A*/Dijkstra-Algorithmus) in der konkreten Einsatzumgebung. Für die Bewegungssteuerung und Kollisionsvermeidung wurden zwei verschiedene Algorithmen entwickelt: Einem reaktiven Verfahren, welches eine Weiterentwicklung des bekannten Vector Field Histogram (VFH) darstellt, wird ein neues antizipatives Verfahren auf Basis von Sampling und stochastischer Suche im Raum der möglichen Bewegungstrajektorien gegenüber gestellt und experimentell verglichen. Die entwickelten Methoden kommen auf mehreren Shopping-Robotern zum Einsatz, die sich seit ca. sechs Monaten im dauerhaften öffentlichen Testbetrieb in einem Baumarkt befinden. Neben den Navigationsmethoden gibt die Arbeit einen Überblick über die weiteren Module des Roboters, z.B. für die Nutzer-Interaktion, und beschreibt detailliert die Steuerarchitektur zur Koordinierung der Teilleistungen. Die Eignung aller eingesetzten Methoden für den Einsatz in einer realen Anwendung und die hohe Akzeptanz der Nutzer für das entwickelte Gesamtsystem werden durch die Auswertung von Langzeittests nachgewiesen

Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions

Welcome to ROBOTICA 2009. This is the 9th edition of the conference on Autonomous Robot Systems and Competitions, the third time with IEEE‐Robotics and Automation Society Technical Co‐Sponsorship. Previous editions were held since 2001 in Guimarães, Aveiro, Porto, Lisboa, Coimbra and Algarve. ROBOTICA 2009 is held on the 7th May, 2009, in Castelo Branco , Portugal. ROBOTICA has received 32 paper submissions, from 10 countries, in South America, Asia and Europe. To evaluate each submission, three reviews by paper were performed by the international program committee. 23 papers were published in the proceedings and presented at the conference. Of these, 14 papers were selected for oral presentation and 9 papers were selected for poster presentation. The global acceptance ratio was 72%. After the conference, eighth papers will be published in the Portuguese journal Robótica, and the best student paper will be published in IEEE Multidisciplinary Engineering Education Magazine. Three prizes will be awarded in the conference for: the best conference paper, the best student paper and the best presentation. The last two, sponsored by the IEEE Education Society ‐ Student Activities Committee. We would like to express our thanks to all participants. First of all to the authors, whose quality work is the essence of this conference. Next, to all the members of the international program committee and reviewers, who helped us with their expertise and valuable time. We would also like to deeply thank the invited speaker, Jean Paul Laumond, LAAS‐CNRS France, for their excellent contribution in the field of humanoid robots. Finally, a word of appreciation for the hard work of the secretariat and volunteers. Our deep gratitude goes to the Scientific Organisations that kindly agreed to sponsor the Conference, and made it come true. We look forward to seeing more results of R&D work on Robotics at ROBOTICA 2010, somewhere in Portugal

Navegação de um robô móvel por processamento de imagem

Trabalho final de Mestrado para obtenção do grau de Mestre em Engenharia Eletrotécnica – Ramo de Automação e Eletrónica IndustrialA robótica móvel é um campo científico notável, que pretende automatizar a movimentação de um mecanismo com uma designada função, para que este tenha a habilidade de realizar as suas tarefas sem permanecer numa posição fixa. Para este efeito, utiliza uma variedade de recursos para identificar o seu ambiente durante o deslocamento. Um dos métodos mais significativos, para o auxílio do reconhecimento do espaço envolvente, é o processamento de imagem. Esta dissertação visa o desenvolvimento de um robô móvel, composto por uma câmera, numa posição alta, com o intuito de realizar a aquisição da imagem, e sensores para evitar obstáculos. O utilizador interage com o robô móvel através do número de dedos da mão. Assim, o algoritmo de processamento de imagem desenvolvido permite controlar a movimentação do robô. O robô móvel foi estruturado com uma perspetiva simples e de baixo custo, mas que também conseguisse desempenhar o seu papel eficientemente. Para realizar o controlo do mesmo é utilizado um Raspberry Pi model 3B+. O foco principal deste projeto está presente no algoritmo de processamento de imagem, desenvolvido em python, com auxílio da biblioteca OpenCV. Empregando métodos de extração de características e de obtenção de contornos, foi possível identificar a mão do utilizador e o número de dedos apresentados. Este programa permite assim, controlar o robô móvel como um tipo de comando, em que consoante o número de dedos exibidos irá executar um diferente tipo de movimento.Mobile robotics is a remarkable scientific field, that aims to automate the movement of a mechanism with a designated function, so that it has the ability to perform its tasks without remaining in a fixed position. For this purpose, it utilizes a variety of features to identify its environment while moving. One of the most significant methods for assisting in the recognition of the surrounding space, is image processing. This dissertation aims to develop a mobile robot, composed of a camera in an elevated position, in order to perform image acquisition, and sensors to avoid obstacles. The user interacts with the mobile robot through the number of fingers on the hand. Thus, the developed image processing algorithm allows control of the robot's movement. The mobile robot was structured with a low-cost perspective and to be relatively simple, but also able to perform its role efficiently. A Raspberry Pi model 3B+ is used to control it. The main focus of this project is present in the image processing algorithm, developed in python, with the aid of the OpenCV library. By employing methods of feature and contour extraction, it was possible to identify the user's hand and the number of fingers displayed. From this program, it is possible to control the mobile robot as a type of remote, where depending on the number of fingers displayed it will perform a different movement function.N/