Robot soccer provides a fertile environment for the development of artificial intelligence techniques. Robot controls require high speed lower level reactive layers as well as higher level deliberative functions. This thesis focuses on a number of aspects in the robot soccer arena. Topics covered include boundary avoidance strategies, vision detection and the application of evolutionary learning to find fuzzy controllers for the control of mobile robot. A three input, two output controller using two angles and a distance as the input and producing two wheel velocity outputs, was developed using evolutionary learning. Current wheel velocities were excluded from the input. The controller produced was a coarse control permitting only either forward or reverse facing impact with the ball. A five input controller was developed which expanded upon the three input model by including the current wheel velocities as inputs. The controller allowed both forward and reverse facing impacts with the ball. A five input hierarchical three layer model was developed to reduce the number of rules to be learnt by an evolutionary algorithm. Its performance was the same as the five input model. Fuzzy clustering of evolved paths was limited by the information available from the paths. The information was sparse in many areas and did not produce a controller that could be used to control the robots. Research was also conducted on the derivation of simple obstacle avoidance strategies for robot soccer. A new decision region method for colour detection in the UV colour map to enable better detection of the robots using an overhead vision system. Experimental observations are given.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / This paper describes the experiences and final design of one team in a senior design competition to build a soccer-playing robot. Each robot was required to operate autonomously under the remote control of a dedicated host computer via a wireless link. Each team designed and constructed a robot and wrote its control software. Certain components were made available to all teams. These components included wireless transmitters and receivers, microcontrollers, overhead cameras, image processing boards, and desktop computers. This paper describes the team’s hardware and software designs, problems they encountered, and lessons learned.

International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, California / This paper describes the construction of an autonomous soccer playing robot as part of a senior design project at Brigham Young University. Each participating team designed and built a robot to compete in an annual tournament. To accomplish this, each team had access to images received from a camera placed above a soccer field. The creation of image processing and artificial intelligence software were required to allow the robot to perform against other robots in a one-on-one competition. Each participating team was given resources to accomplish this project. This paper contains a summary of the experiences gained by team members and also a description of the key components created for the robot named Prometheus to compete and win the annual tournament.

Thesis (MSc)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: This study has started as part of a research project at Stellenbosch University (SU) that aims at building a team of soccer-playing robots for the RoboCup Small Size League (SSL). In the RoboCup SSL the Decision- Making Module (DMM) plays an important role for it makes all decisions for the robots in the team. This research focuses on the development of some parts of the DMM for the team at SU. A literature study showed that the DMM is typically developed in a hierarchical structure where basic soccer skills form the fundamental building blocks and high-level team behaviours are implemented using these basic soccer skills. The literature study also revealed that strategies in the DMM are usually developed using a hand-coded approach in the RoboCup SSL domain, i.e., a specific and fixed strategy is coded, while in other leagues a Machine Learning (ML) approach, Reinforcement Learning (RL) in particular, is widely used. This led to the following research objective of this thesis, namely to develop basic soccer skills using RL for the RoboCup Small Size League. A second objective of this research is to develop a simulation environment to facilitate the development of the DMM. A high-level simulator was developed and validated as a result. The temporal-difference value iteration algorithm with state-value functions was used for RL, along with a Multi-Layer Perceptron (MLP) as a function approximator. Two types of important soccer skills, namely shooting skills and passing skills were developed using the RL and MLP combination. Nine experiments were conducted to develop and evaluate these skills in various playing situations. The results showed that the learning was very effective, as the learning agent executed the shooting and passing tasks satisfactorily, and further refinement is thus possible. In conclusion, RL combined with MLP was successfully applied in this research to develop two important basic soccer skills for robots in the RoboCup SSL. These form a solid foundation for the development of a complete DMM along with the simulation environment established in this research. / AFRIKAANSE OPSOMMING: Hierdie studie het ontstaan as deel van 'n navorsingsprojek by Stellenbosch Universiteit wat daarop gemik was om 'n span sokkerrobotte vir die RoboCup Small Size League (SSL) te ontwikkel. Die besluitnemingsmodule (BM) speel 'n belangrike rol in die RoboCup SSL, aangesien dit besluite vir die robotte in die span maak. Hierdie navorsing fokus op ontwikkeling van enkele komponente van die BM vir die span by SU. 'n Literatuurstudie het getoon dat die BM tipies ontwikkel word volgens 'n hiërargiese struktuur waarin basiese sokkervaardighede die fundamentele boublokke vorm en hoëvlak spangedrag word dan gerealiseer deur hierdie basiese vaardighede te gebruik. Die literatuur het ook getoon dat strategieë in die BM van die RoboCup SSL domein gewoonlik ontwikkel word deur 'n hand-gekodeerde benadering, dit wil s^e, 'n baie spesifieke en vaste strategie word gekodeer, terwyl masjienleer (ML) en versterkingsleer (VL) wyd in ander ligas gebruik word. Dit het gelei tot die navorsingsdoelwit in hierdie tesis, naamlik om basiese sokkervaardighede vir robotte in die RoboCup SSL te ontwikkel. 'n Tweede doelwit was om 'n simulasie-omgewing te ontwikkel wat weer die ontwikkeling van die BM sou fasiliteer. Hierdie simulator is suksesvol ontwikkel en gevalideer. Die tydwaarde-verskil iterariewe algoritme met toestandwaarde-funksies is gebruik vir VL saam met 'n multi-laag perseptron (MLP) vir funksiebenaderings. Twee belangrike sokkervaardighede, naamlik doelskop- en aangeevaardighede is met hierdie kombinasie van VL en MLP ontwikkel. Nege eksperimente is uitgevoer om hierdie vaardighede in verskillende speelsituasies te ontwikkel en te evalueer. Volgens die resultate was die leerproses baie effektief, aangesien die leer-agent die doelskiet- en aangeetake bevredigend uitgevoer het, en verdere verfyning is dus moontlik. Die gevolgtrekking is dat VL gekombineer met MLP suksesvol toegepas is in hierdie navorsingswerk om twee belangrike, basiese sokkervaardighede vir robotte in die RoboCup SSL te ontwikkel. Dit vorm 'n sterk fondament vir die ontwikkeling van 'n volledige BM tesame met die simulasie-omgewing wat in hierdie werk daargestel is.

Made available in DSpace on 2014-12-17T14:56:05Z (GMT). No. of bitstreams: 1 MarceloMY.pdf: 692352 bytes, checksum: bba1f91bbb77a56af37aa6e9e2a536a6 (MD5) Previous issue date: 2005-02-04 / Este trabalho apresenta o desenvolvimento de um m?todo de coordena??o e coopera??o para uma frota de mini-rob?s m?veis. O escopo do desenvolvimento ? o futebol de rob?s. Trata-se de uma plataforma bem estruturada, din?mica e desenvolvida no mundo inteiro. O futebol de rob?s envolve diversos campos do conhecimento incluindo: vis?o computacional, teoria de controle, desenvolvimento de circuitos microcontrolados, planejamento cooperativo, entre outros. A t?tulo de organiza??o os sistema foi dividido em cinco m?dulos: rob?, vis?o, localiza??o, planejamento e controle. O foco do trabalho se limita ao m?dulo de planejamento. Para auxiliar seu desenvolvimento um simulador do sistema foi implementado. O simulador funciona em tempo real e substitui os rob?s reais. Dessa forma os outros m?dulos permanecem praticamente inalterados durante uma simula??o ou execu??o com rob?s reais. Para organizar o comportamento dos rob?s e produzir a coopera??o entre eles foi adotada uma arquitetura hierarquizada: no mais alto n?vel est? a escolha do estilo de jogo do time; logo abaixo decide-se o papel que cada jogador deve assumir; associado ao papel temos uma a??o espec?fica e finalmente calcula-se a refer?ncia de movimento do rob?. O papel de um rob? dita o comportamento do rob? na dada ocasi?o. Os pap?is s?o alocados dinamicamente durante o jogo de forma que um mesmo rob? pode assumir diferentes pap?is no decorrer da partida

Submitted by Jessica Andrade (jessicastefanysa@gmail.com) on 2018-06-28T18:00:49Z No. of bitstreams: 1 TELMO.pdf: 5034534 bytes, checksum: d5ff82ed46adf98d29eb0e7b1578bb38 (MD5) / Approved for entry into archive by Margareth Ferreira Pinto (margarethfpinto@hotmail.com) on 2018-07-16T21:35:04Z (GMT) No. of bitstreams: 1 TELMO.pdf: 5034534 bytes, checksum: d5ff82ed46adf98d29eb0e7b1578bb38 (MD5) / Made available in DSpace on 2018-07-16T21:35:04Z (GMT). No. of bitstreams: 1 TELMO.pdf: 5034534 bytes, checksum: d5ff82ed46adf98d29eb0e7b1578bb38 (MD5) Previous issue date: 2015 / O desenvolvimento de sistemas com complexidade necessária a uma abordagem mul- tiagente carrega consigo também aspectos de complexidade relacionados à avaliação dos diferentes níveis e componentes desse sistema. Um sistema multiagente pode ser com- posto por uma série de agentes heterogêneos, que apresentam variabilidade quanto à sua arquitetura interna, modelos utilizados para o seu desenvolvimento, linguagem de programação, de especificação e validação. Agregam-se a isso, contextos específicos de cada solução para com o ambiente para o qual foi projetado. Deste modo, impõem-se mecanismos que permitam estabelecer métricas de avaliação para cada nível do desenvol- vimento de um sistema multiagente, considerando dimensões como organização, comuni- cação entre agentes e os agentes em si. Esta dissertação apresenta como problemática, o estabelecimento de um benchmark para sistemas multiagente dentro do simulador de futebol de robôs Soccer Server 2D. Mais especificamente, este benchmark deve prover métricas e mecanismos de avaliação de esquemas organizacionais multiagente segundo os diferentes cenários que podem se estabelecer dentro da dinâmica de uma partida de futebol. Não obstante, deve-se permitir o estabelecimento de referências de avaliação da coletividade dos times implementados para o Soccer Server 2D, indiferente aos demais níveis de concepção do sistema. / The development of systems with the required complexity for a multi-agent approach, also carries complexity aspects related to the evaluation of different levels and compo- nents of such a system. A multi-agent system can be composed of a series of hetero- geneous agents, which have variability regarding its internal architecture, models used for its development, programming language, specification and validation. Added to this, are situated the particular contexts of each solution towards the environment for which it is designed. Therefore, it is needed mechanisms to establish evaluation metrics for each multi-agent system development level, taking into account dimensions such as or- ganization, communication between agents and the agents themselves. This dissertation presents as a problem, establish a benchmark for multi-agent systems within the robot soccer simulator Soccerserver 2D. Specifically, this benchmark should provide metrics and evaluation mechanisms of multi-agent organization schemes according to different scenarios that can be established within the dynamics of a football match . Nevertheless, it should be allowed the establishment of assessment referrals for the Robocup teams collectivity, regardless to other levels of system design.

Abstract - Mobile devices in the near future will need to collaborate to fulfill their function. Collaboration will be done by communication. We use a real world example of robotic soccer to come up with the necessary structures required for robotic communication. A review of related work is done and it is found no examples come close to providing a RANET. The robotic ad hoc network (RANET) we suggest uses existing structures pulled from the areas of wireless networks, peer to peer and software life-cycle management. Gaps are found in the existing structures so we describe how to extend some structures to satisfy the design. The RANET design supports robot cooperation by exchanging messages, discovering needed skills that other robots on the network may possess and the transfer of these skills. The network is built on top of a Bluetooth wireless network and uses JXTA to communicate and transfer skills. OSGi bundles form the skills that can be transferred. To test the nal design a reference implementation is done. Deficiencies in some third party software is found, specifically JXTA and JamVM and GNU Classpath. Lastly we look at how to fix the deciencies by porting the JXTA C implementation to the target robotic platform and potentially eliminating the TCP/IP layer, using UDP instead of TCP or using an adaptive TCP/IP stack. We also propose a future areas of investigation; how to seed the configuration for the Personal area network (PAN) Bluetooth protocol extension so a Bluetooth TCP/IP link is more quickly formed and using the STP to allow multi-hop messaging and transfer of skills.

Made available in DSpace on 2014-12-17T14:56:22Z (GMT). No. of bitstreams: 1 KelsonRTA.pdf: 822338 bytes, checksum: 2e3a06ae915ace956aac24995e24973f (MD5) Previous issue date: 2001-03-28 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Navigation based on visual feedback for robots, working in a closed environment, can be obtained settling a camera in each robot (local vision system). However, this solution requests a camera and capacity of local processing for each robot. When possible, a global vision system is a cheapest solution for this problem. In this case, one or a little amount of cameras, covering all the workspace, can be shared by the entire team of robots, saving the cost of a great amount of cameras and the associated processing hardware needed in a local vision system. This work presents the implementation and experimental results of a global vision system for mobile mini-robots, using robot soccer as test platform. The proposed vision system consists of a camera, a frame grabber and a computer (PC) for image processing. The PC is responsible for the team motion control, based on the visual feedback, sending commands to the robots through a radio link. In order for the system to be able to unequivocally recognize each robot, each one has a label on its top, consisting of two colored circles. Image processing algorithms were developed for the eficient computation, in real time, of all objects position (robot and ball) and orientation (robot). A great problem found was to label the color, in real time, of each colored point of the image, in time-varying illumination conditions. To overcome this problem, an automatic camera calibration, based on clustering K-means algorithm, was implemented. This method guarantees that similar pixels will be clustered around a unique color class. The obtained experimental results shown that the position and orientation of each robot can be obtained with a precision of few millimeters. The updating of the position and orientation was attained in real time, analyzing 30 frames per second / A navega??o baseada em realimenta??o visual para rob?s, trabalhando em um ambiente fechado, pode ser obtida instalando-se uma c?mera em cada rob? (sistema de vis?o local). Esta solu??o, entretanto, requer uma c?mera e capacidade de processamento embarcado para cada rob?. Quando poss?vel, um sistema de vis?o global ? uma solu??o barata para este problema. Neste caso, uma ou uma pequena quantidade de c?meras, cobrindo todo o espa?o de trabalho, pode ser compartilhada pelos rob?s, diminuindo o custo de uma grande quantidade de c?meras e o hardware de processamento necess?rio a um sistema de vis?o local. Este trabalho apresenta a implementa??o e os resultados experimentais de um sistema de vis?o global para uma frota de mini-rob?s m?veis, utilizando como plataforma de testes uma partida de futebol entre rob?s. O sistema de vis?o proposto consiste de uma c?mera, uma placa digitalizadora de imagens e um computador (PC) para o processamento das imagens. O PC ? respons?vel pelo controle dos rob?s, baseado em realimenta??o visual, enviando comandos aos rob?s atrav?s de um transmissor de r?dio. Com o objetivo de possibilitar ao sistema reconhecer unicamente cada rob?, eles possuem r?tulos em seu topo, consistindo de dois c?rculos coloridos. Algoritmos de processamento de imagem foram desenvolvidos para o c?mputo eficiente, em tempo real, da posi??o (rob? e bola) e orienta??o (rob?) dos objetos em campo. Um grande problema encontrado foi rotular a cor, em tempo real, cada ponto colorido da imagem, em condi??es de varia??o de luminosidade. Para resolver este problema, um software de calibra??o autom?tica da c?mera, baseado no algoritmo de aglomera??o K-means, foi implementado. Este m?todo garante que pixels similares sejam agrupados ao redor de uma ?nica classe de cor. Os resultados experimentais obtidos mostram que a posi??o e a orienta??o de cada rob? pode ser obtida com uma precis?o de poucos mil?metros. A atualiza??o das informa??es de posi??o e orienta??o foi realizada em tempo real, analisando 30 quadros por segundo

Multi-Agent coordination and control has been studied for a long time, but has recently gained more interest because of technology improvements allowing smaller, more versatile robots and other types of agents. To facilitate multi-agent experiments between heterogeneous agents, including robots and UAVs, we have created a test-bed with both simulation and hardware capabilities. This thesis discusses the creation of this unique, versatile test-bed for multi-agent experiments, also a unique graph creation algorithm, and some experimental results obtained using the test-bed.

La tesis propone un marco de trabajo para el soporte de la toma de decisiones adecuado para soportar la ejecución distribuida de acciones cooperativas en entornos multi-agente dinámicos y complejos. Soporte para la toma de decisiones es un proceso que intenta mejorar la ejecución de la toma de decisiones en escenarios cooperativos. Este proceso ocurre continuamente en la vida diaria. Los humanos, por ejemplo, deben tomar decisiones acerca de que ropa usar, que comida comer, etc. En este sentido, un agente es definido como cualquier cosa que está situada en un entorno y que actúa, basado en su observación, su interpretación y su conocimiento acerca de su situación en tal entorno para lograr una acción en particular.Por lo tanto, para tomar decisiones, los agentes deben considerar el conocimiento que les permita ser consientes en que acciones pueden o no ejecutar. Aquí, tal proceso toma en cuenta tres parámetros de información con la intención de personificar a un agente en un entorno típicamente físico. Así, el mencionado conjunto de información es conocido como ejes de decisión, los cuales deben ser tomados por los agentes para decidir si pueden ejecutar correctamente una tarea propuesta por otro agente o humano. Los agentes, por lo tanto, pueden hacer mejores decisiones considerando y representando apropiadamente tal información. Los ejes de decisión, principalmente basados en: las condiciones ambientales, el conocimiento físico y el valor de confianza del agente, provee a los sistemas multi-agente un confiable razonamiento para alcanzar un factible y exitoso rendimiento cooperativo.Actualmente, muchos investigadores tienden a generar nuevos avances en la tecnología agente para incrementar la inteligencia, autonomía, comunicación y auto-adaptación en escenarios agentes típicamente abierto y distribuidos. En este sentido, esta investigación intenta contribuir en el desarrollo de un nuevo método que impacte tanto en las decisiones individuales como colectivas de los sistemas multi-agente. Por lo tanto, el marco de trabajo propuesto ha sido utilizado para implementar las acciones concretas involucradas en el campo de pruebas del fútbol robótico. Este campo emula los juegos de fútbol real, donde los agentes deben coordinarse, interactuar y cooperar entre ellos para solucionar tareas complejas dentro de un escenario dinámicamente cambiante y competitivo, tanto para manejar el diseño de los requerimientos involucrados en las tareas como para demostrar su efectividad en trabajos colectivos. Es así que los resultados obtenidos tanto en el simulador como en el campo real de experimentación, muestran que el marco de trabajo para el soporte de decisiones propuesto para agentes situados es capaz de mejorar la interacción y la comunicación, reflejando en un adecuad y confiable trabajo en equipo dentro de entornos impredecibles, dinámicos y competitivos. Además, los experimentos y resultados también muestran que la información seleccionada para generar los ejes de decisión para situar a los agentes, es útil cuando tales agentes deben ejecutar una acción o hacer un compromiso en cada momento con la intención de cumplir exitosamente un objetivo colectivo. Finalmente, algunas conclusiones enfatizando las ventajas y utilidades del trabajo propuesto en la mejora del rendimiento colectivo de los sistemas multi-agente en situaciones tales como tareas coordinadas y asignación de tareas son presentadas. / This thesis proposes a framework to decision support suitable for supporting the distributed performing of cooperative actions in dynamic and complex multi-agent environments. Decision support is a process aiming to improve the decision-making performance in cooperative scenarios. Simply stated, decision-making is the process of selecting a specific action out of multiple alternatives. This process occurs continuously in daily life. Humans, for instance, have to take decisions about what cloths to wear, what food to eat, etc. In this sense, an agent is defined as anything that is situated in an environment and acts, based on its observation, its interpretation and its knowledge about its situation on such environment to fulfil a particular action. Therefore, to take decisions, agents must get knowledge that allow them to be aware on what actions can or cannot perform. Here, such process takes three information parameters trying to embody an agent in a typically physical world. This set of information is known as decision axes, which it any agent must take into account to decide if it can perform correctly the task proposed by other agent or human. Agents can make better decision by considering and representing properly such information. Decision axes, mainly based on the agents' environmental condition, the agents' physical knowledge and the agents' trust value, provide multi-agent systems a reliable reasoning for achieving feasible and successful cooperative performance. Currently, many researches tend to generate news advances in agent technology to increase the intelligence, autonomy, communication and self-adaptation in open and distributed agent scenarios. In this sense, this research aims to contribute to the development of a new path to impact on both individual and cooperative decisions in multi-agent environments In this light, the thesis was used to implement the concrete actions involved in the robot soccer both in simulated as in real scenarios. It emulates a soccer game where agents must communicate; interact and cooperate among them to perform complex actions within a dynamic and competitive scenario, both to drive the design of the involved actions' requirements as to demonstrate its effectiveness in cooperative jobs. Therefore, the thesis has obtained results, both on simulation and on real experimentations, showing that the framework to decision support for situated agents presented is capable to improve the interaction and the communication, reflect in a suitable and reliable agent's team-work within an unpredictable, dynamic and competitive environment. The experimentation also showed that the selected information to generate the decision axes to situate agents are useful when these agents must perform the proper action or made sure commitments at each moment in order to reach successfully a goal. Conclusions emphasizing the advantages and usefulness of the introduced approach, in the improvement of multi-agent performance in coordinated task and task allocation problems are presented.