Locating an odor source in a turbulent environment, an instinctive behavior of insects such as moths, is a nontrivial task in robotics. Robots equipped with odor sensors find it difficult to locate the odor source due to the sporadic nature of odor patches in a turbulent environment. In this thesis, we develop a swarm algorithm which acquires information from odor patches and utilizes it to locate the odor source. The algorithm utilizes an intelligent integration of the chemotaxis, anemotaxis and spiralling approaches, where the chemotactic behavior is implemented by the recently proposed Glowworm Swarm Optimization (GSO) algorithm. Agents switch between chemotactic, anemotactic, and spiralling modes in accordance with the information available from the environment for optimal performance. The proposed algorithm takes full advantage of communication and collaboration between the robots. It is shown to be robust, efficient and well suited for implementation in olfactory robots. An important feature of the algorithm is the use of maximum concentration encountered in the recent past for navigation, which is seen to improve algorithmic performance significantly. The algorithm initially assumes agents to be point masses, later this is modified for robots and includes a gyroscopic avoidance strategy. A variant of the algorithm which does not demand wind information, is shown to be capable of locating odor sources even in no wind environment. A deterministic GSO algorithm has been proposed which is shown capable of faster convergence. Another proposed variant, the push pull GSO algorithm is shown to be more efficient in the presence of obstacle avoidance. The proposed algorithm is also seen capable of locating odor source under varying wind conditions. We have also shown the simultaneous capture of multiple odor sources by the proposed algorithm. A mobile odor source is shown to be captured and tracked by the proposed approach. The proposed approaches are later tested on data obtained from a realistic dye mixing experiment. A gas source localization experiment is also carried out in the lab to demonstrate the validity of the proposed approaches under real world conditions.

A Inteligência de Enxame foi proposta a partir da observação do comportamento social de espécies de insetos, pássaros e peixes. A ideia central deste comportamento coletivo é executar uma tarefa complexa decompondo-a em tarefas simples, que são facilmente executadas pelos indivíduos do enxame. A realização coordenada destas tarefas simples, respeitando uma proporção pré-definida de execução, permite a realização da tarefa complexa. O problema de alocação de tarefas surge da necessidade de alocar as tarefas aos indivíduos de modo coordenado, permitindo o gerenciamento do enxame. A alocação de tarefas é um processo dinâmico pois precisa ser continuamente ajustado em resposta a alterações no ambiente, na configuração do enxame e/ou no desempenho do mesmo. A robótica de enxame surge deste contexto de cooperação coletiva, ampliada à robôs reais. Nesta abordagem, problemas complexos são resolvidos pela realização de tarefas complexas por enxames de robôs simples, com capacidade de processamento e comunicação limitada. Objetivando obter flexibilidade e confiabilidade, a alocação deve emergir como resultado de um processo distribuído. Com a descentralização do problema e o aumento do número de robôs no enxame, o processo de alocação adquire uma elevada complexidade. Desta forma, o problema de alocação de tarefas pode ser caracterizado como um processo de otimização que aloca as tarefas aos robôs, de modo que a proporção desejada seja atendida no momento em que o processo de otimização encontre a solução desejada. Nesta dissertação, são propostos dois algoritmos que seguem abordagens distintas ao problema de alocação dinâmica de tarefas, sendo uma local e a outra global. O algoritmo para alocação dinâmica de tarefas com abordagem local (ADTL) atualiza a alocação de tarefa de cada robô a partir de uma avaliação determinística do conhecimento atual que este possui sobre as tarefas alocadas aos demais robôs do enxame. O algoritmo para alocação dinâmica de tarefas com abordagem global (ADTG) atualiza a alocação de tarefas do enxame com base no algoritmo de otimização PSO (Particle swarm optimization). No ADTG, cada robô possui uma possível solução para a alocação do enxame que é continuamente atualizada através da troca de informação entre os robôs. As alocações são avaliadas quanto a sua aptidão em atender à proporção-objetivo. Quando é identificada a alocação de maior aptidão no enxame, todos os robôs do enxame são alocados para as tarefas definidas por esta alocação. Os algoritmos propostos foram implementados em enxames com diferentes arranjos de robôs reais demonstrando sua eficiência e eficácia, atestados pelos resultados obtidos. / Swarm Intelligence has been proposed based on the observation of social behavior of insect species, birds and fishes. The main idea of this collective behavior is to perform a complex task decomposing it into many simple tasks, that can be easily performed by individuals of the swarm. Coordinated realization of these simple tasks while adhering to a pre-defined distribution of execution, allows for the achievement of the original complex task. The problem of task allocation arises from the need of assigning tasks to individuals in a coordinated fashion, allowing a good management of the swarm. Task allocation is a dynamic process because it requires a continuous adjustment in response to changes in the environment, the swarm configuration and/or the performance of the swarm. Swarm robotics emerges from this context of collective cooperation applied to swarms of real robots. In this approach, complex problems are solved by performing complex tasks using swarms of simple robots, with a limited processing and communication capabilities. Aiming at achieving flexibility and reliability, the allocation should emerge as a result of a distributed process. With the decentralization of the problem and the increasing number of robots in the swarm, the allocation process acquires a high complexity. Thus, the problem of task allocation can be characterized as an optimization process that assigns tasks to robots, so that the desired proportion is met at the end of the optimization process, find the desired solution. In this dissertation, we propose two algorithms that follow different to the problem of dynamic task allocation approaches: one is local and the other global. The algorithm for dynamic allocation of tasks with a local approach (ADTL) updates the task assignment of each robot based on a deterministic assessment of the current knowledge it has so far about the tasks allocated to the other robots of the swarm. The algorithm for dynamic task allocation with a global approach (ADTG) updates the allocation of tasks based on a swarm optimization process, inspired by PSO (Particle swarm optimization). In ADTG, each robot has a possible solution to the swarm allocation, which is continuously updated through the exchange of information between the robots. The allocations are evaluated for their fitness in meeting the goal proportion. When the allocation of highest fitness in the swarm is identified, all robots of the swarm are allocated to the tasks defined by this allocation. The proposed algorithms were implemented on swarms of different arrangements of real robots demonstrating their efficacy, robustness and efficiency, certified by obtained the results.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Os Sistemas Multi-Robôs proporcionam vantagens sobre um robô individual, quando da realização de uma tarefa com maiores velocidade, precisão e tolerância a falhas. Os estudos dos comportamentos sociais na natureza têm permitido desenvolver algoritmos bio-inspirados úteis na área da robótica de enxame. Seguindo instruções simples e repetitivas, grupos de robôs, fisicamente limitados, conseguem solucionar problemas complexos. Quando existem duas ou mais tarefas a serem realizadas e o conjunto de robôs é heterogêneo, é possível agrupá-los de acordo com as funcionalidades neles disponíveis. No caso em que o conjunto de robôs é homogêneo, o agrupamento pode ser realizado considerando a posição relativa do robô em relação a uma tarefa ou acrescentando alguma característica distintiva. Nesta dissertação, é proposta uma técnica de clusterização espacial baseada simplesmente na comunicação local de robôs. Por meio de troca de mensagens entre os robôs vizinhos, esta técnica permite formar grupos de robôs espacialmente próximos sem precisar movimentar os robôs. Baseando-se nos métodos de clusterização de fichas, a técnica proposta emprega a noção de fichas virtuais, que são chamadas de cargas, sendo que uma carga pode ser estática ou dinâmica. Se uma carga é estática permite determinar a classe à qual um robô pertence. Dependendo da quantidade e do peso das cargas disponíveis no sistema, os robôs intercambiam informações até alcançar uma disposição homogênea de cargas. Quando as cargas se tornam estacionárias, é calculada uma densidade que permite guiar aquelas que estão ainda em movimento. Durante as experiências, foi observado visualmente que as cargas com maior peso acabam se agrupando primeiro enquanto aquelas com menor peso continuam se deslocando no enxame, até que estas cargas formem faixas de densidades diferenciadas para cada classe, alcançando assim o objetivo final que é a clusterização dos robôs. / Multi-Robots Systems provide advantages over a single robot when performing a task, achieving a greater speed, higher accuracy and better fault tolerance. The studies of social behavior in nature have allowed to develop bio-inspired algorithms useful in swarm robotics. Following simple and repetitive rules, groups of robots can provide solutions to complex problems. When two or more tasks to be executed by a set of heterogeneous robots, it is possible to cluster the robots according to their intrinsic features. When homogeneous robots are used, the clustering may be achieved by considering the robot relative position regarding the location where the task has to be performed or adding some other distinct feature. In this dissertation, a technique for spatial clustering simply based on local communication between robots is proposed. Through the message exchange between neighboring robots, this technique allows cluster formation without robot movement. Based on the token clustering methods, the proposed technique employs a virtual token, which is called a load. The load allows identifying the class to which a robot belongs. Depending on the amount and weight of the loads available in the system, the robots interchange information to achieve uniform load distribution. When the loads become stationaries, a density is calculated as to guide the remaining loads that are still in motion. As a consequence, the loads of higher weight cluster first and the those of lower weight continue shifting through the swarm, until they start forming different density ranges for each class, thereby achieving the final aim which is robot clustering.

A Inteligência de Enxame foi proposta a partir da observação do comportamento social de espécies de insetos, pássaros e peixes. A ideia central deste comportamento coletivo é executar uma tarefa complexa decompondo-a em tarefas simples, que são facilmente executadas pelos indivíduos do enxame. A realização coordenada destas tarefas simples, respeitando uma proporção pré-definida de execução, permite a realização da tarefa complexa. O problema de alocação de tarefas surge da necessidade de alocar as tarefas aos indivíduos de modo coordenado, permitindo o gerenciamento do enxame. A alocação de tarefas é um processo dinâmico pois precisa ser continuamente ajustado em resposta a alterações no ambiente, na configuração do enxame e/ou no desempenho do mesmo. A robótica de enxame surge deste contexto de cooperação coletiva, ampliada à robôs reais. Nesta abordagem, problemas complexos são resolvidos pela realização de tarefas complexas por enxames de robôs simples, com capacidade de processamento e comunicação limitada. Objetivando obter flexibilidade e confiabilidade, a alocação deve emergir como resultado de um processo distribuído. Com a descentralização do problema e o aumento do número de robôs no enxame, o processo de alocação adquire uma elevada complexidade. Desta forma, o problema de alocação de tarefas pode ser caracterizado como um processo de otimização que aloca as tarefas aos robôs, de modo que a proporção desejada seja atendida no momento em que o processo de otimização encontre a solução desejada. Nesta dissertação, são propostos dois algoritmos que seguem abordagens distintas ao problema de alocação dinâmica de tarefas, sendo uma local e a outra global. O algoritmo para alocação dinâmica de tarefas com abordagem local (ADTL) atualiza a alocação de tarefa de cada robô a partir de uma avaliação determinística do conhecimento atual que este possui sobre as tarefas alocadas aos demais robôs do enxame. O algoritmo para alocação dinâmica de tarefas com abordagem global (ADTG) atualiza a alocação de tarefas do enxame com base no algoritmo de otimização PSO (Particle swarm optimization). No ADTG, cada robô possui uma possível solução para a alocação do enxame que é continuamente atualizada através da troca de informação entre os robôs. As alocações são avaliadas quanto a sua aptidão em atender à proporção-objetivo. Quando é identificada a alocação de maior aptidão no enxame, todos os robôs do enxame são alocados para as tarefas definidas por esta alocação. Os algoritmos propostos foram implementados em enxames com diferentes arranjos de robôs reais demonstrando sua eficiência e eficácia, atestados pelos resultados obtidos. / Swarm Intelligence has been proposed based on the observation of social behavior of insect species, birds and fishes. The main idea of this collective behavior is to perform a complex task decomposing it into many simple tasks, that can be easily performed by individuals of the swarm. Coordinated realization of these simple tasks while adhering to a pre-defined distribution of execution, allows for the achievement of the original complex task. The problem of task allocation arises from the need of assigning tasks to individuals in a coordinated fashion, allowing a good management of the swarm. Task allocation is a dynamic process because it requires a continuous adjustment in response to changes in the environment, the swarm configuration and/or the performance of the swarm. Swarm robotics emerges from this context of collective cooperation applied to swarms of real robots. In this approach, complex problems are solved by performing complex tasks using swarms of simple robots, with a limited processing and communication capabilities. Aiming at achieving flexibility and reliability, the allocation should emerge as a result of a distributed process. With the decentralization of the problem and the increasing number of robots in the swarm, the allocation process acquires a high complexity. Thus, the problem of task allocation can be characterized as an optimization process that assigns tasks to robots, so that the desired proportion is met at the end of the optimization process, find the desired solution. In this dissertation, we propose two algorithms that follow different to the problem of dynamic task allocation approaches: one is local and the other global. The algorithm for dynamic allocation of tasks with a local approach (ADTL) updates the task assignment of each robot based on a deterministic assessment of the current knowledge it has so far about the tasks allocated to the other robots of the swarm. The algorithm for dynamic task allocation with a global approach (ADTG) updates the allocation of tasks based on a swarm optimization process, inspired by PSO (Particle swarm optimization). In ADTG, each robot has a possible solution to the swarm allocation, which is continuously updated through the exchange of information between the robots. The allocations are evaluated for their fitness in meeting the goal proportion. When the allocation of highest fitness in the swarm is identified, all robots of the swarm are allocated to the tasks defined by this allocation. The proposed algorithms were implemented on swarms of different arrangements of real robots demonstrating their efficacy, robustness and efficiency, certified by obtained the results.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Os Sistemas Multi-Robôs proporcionam vantagens sobre um robô individual, quando da realização de uma tarefa com maiores velocidade, precisão e tolerância a falhas. Os estudos dos comportamentos sociais na natureza têm permitido desenvolver algoritmos bio-inspirados úteis na área da robótica de enxame. Seguindo instruções simples e repetitivas, grupos de robôs, fisicamente limitados, conseguem solucionar problemas complexos. Quando existem duas ou mais tarefas a serem realizadas e o conjunto de robôs é heterogêneo, é possível agrupá-los de acordo com as funcionalidades neles disponíveis. No caso em que o conjunto de robôs é homogêneo, o agrupamento pode ser realizado considerando a posição relativa do robô em relação a uma tarefa ou acrescentando alguma característica distintiva. Nesta dissertação, é proposta uma técnica de clusterização espacial baseada simplesmente na comunicação local de robôs. Por meio de troca de mensagens entre os robôs vizinhos, esta técnica permite formar grupos de robôs espacialmente próximos sem precisar movimentar os robôs. Baseando-se nos métodos de clusterização de fichas, a técnica proposta emprega a noção de fichas virtuais, que são chamadas de cargas, sendo que uma carga pode ser estática ou dinâmica. Se uma carga é estática permite determinar a classe à qual um robô pertence. Dependendo da quantidade e do peso das cargas disponíveis no sistema, os robôs intercambiam informações até alcançar uma disposição homogênea de cargas. Quando as cargas se tornam estacionárias, é calculada uma densidade que permite guiar aquelas que estão ainda em movimento. Durante as experiências, foi observado visualmente que as cargas com maior peso acabam se agrupando primeiro enquanto aquelas com menor peso continuam se deslocando no enxame, até que estas cargas formem faixas de densidades diferenciadas para cada classe, alcançando assim o objetivo final que é a clusterização dos robôs. / Multi-Robots Systems provide advantages over a single robot when performing a task, achieving a greater speed, higher accuracy and better fault tolerance. The studies of social behavior in nature have allowed to develop bio-inspired algorithms useful in swarm robotics. Following simple and repetitive rules, groups of robots can provide solutions to complex problems. When two or more tasks to be executed by a set of heterogeneous robots, it is possible to cluster the robots according to their intrinsic features. When homogeneous robots are used, the clustering may be achieved by considering the robot relative position regarding the location where the task has to be performed or adding some other distinct feature. In this dissertation, a technique for spatial clustering simply based on local communication between robots is proposed. Through the message exchange between neighboring robots, this technique allows cluster formation without robot movement. Based on the token clustering methods, the proposed technique employs a virtual token, which is called a load. The load allows identifying the class to which a robot belongs. Depending on the amount and weight of the loads available in the system, the robots interchange information to achieve uniform load distribution. When the loads become stationaries, a density is calculated as to guide the remaining loads that are still in motion. As a consequence, the loads of higher weight cluster first and the those of lower weight continue shifting through the swarm, until they start forming different density ranges for each class, thereby achieving the final aim which is robot clustering.

Collective decision making can be seen as a means of designing and understanding swarm robotics systems. While decision-making is generally conceived as the cognitive ability of individual agents to select a belief based only on their preferences and available information, collective decision making is a decentralized cognitive process, whereby an ensemble of agents gathers, shares, and processes information as a single organism and makes a choice that is not attributable to any of its individuals. A principled selection of the rules governing this cognitive process allows the designer to define, shape, and foresee the dynamics of the swarm.We begin this monograph by introducing the reader to the topic of collective decision making. We focus on artificial systems for discrete consensus achievement and review the literature of swarm robotics. In this endeavor, we formalize the best-of-n problem—a generalization of the logic underlying several cognitive problems—and define a taxonomy of its possible variants that are of interest for the design of robot swarms. By leveraging on this understanding, we identify the building-blocks that are essential to achieve a collective decision addressing the best-of-n problem: option exploration, opinion dissemination, modulation of positive feedback, and individual decision-making mechanism. We show how a modular perspective of a collective decision-making strategy allows for the systematic modeling of the resulting swarm performance. In doing so, we put forward a modular and model-driven design methodology that allows the designer to study the dynamics of a swarm at different level of abstractions. Successively, we employ the proposed design methodology to derive and to study different collective decision-making strategies for the best-of-n problem. We show how the designed strategies can be readily applied to different real-world scenarios by performing two series of robot experiments. In the first series, we use a swarm of 100 robots to tackle a site-selection scenario; in the second series, we show instead how the same strategies apply to a collective perception scenario. We conclude with a discussion of our research contributions and provide futuredirection of research. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

In this thesis, we propose an approach to achieve autonomous task partitioning in swarms of robots. Task partitioning is the process by which tasks are decomposed into sub-tasks and it is often an advantageous way of organizing work in groups of individuals. Therefore, it is interesting to study its application to swarm robotics, in which groups of robots are deployed to collectively carry out a mission. The capability of partitioning tasks autonomously can enhance the flexibility of swarm robotics systems because the robots can adapt the way they decompose and perform their work depending on specific environmental conditions and goals. So far, few studies have been presented on the topic of task partitioning in the context of swarm robotics. Additionally, in all the existing studies, there is no separation between the task partitioning methods and the behavior of the robots and often task partitioning relies on characteristics of the environments in which the robots operate.<p>This limits the applicability of these methods to the specific contexts for which they have been built. The work presented in this thesis represents the first steps towards a general framework for autonomous task partitioning in swarms of robots. We study task partitioning in foraging, since foraging abstracts practical real-world problems. The approach we propose in this thesis is therefore studied in experiments in which the goal is to achieve autonomous task partitioning in foraging. However, in the proposed approach, the task partitioning process relies upon general, task-independent concepts and we are therefore confident that it is applicable in other contexts. We identify two main capabilities that the robots should have: i) being capable of selecting whether to employ task partitioning and ii) defining the sub-tasks of a given task. We propose and study algorithms that endow a swarm of robots with these capabilities. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

In this dissertation, we study two new approaches to fault detection for autonomous robots. The first approach involves the synthesis of software components that give a robot the capacity to detect faults which occur in itself. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data in three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots both while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of the number of false positives and the time it takes to detect a fault.<p>The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot's sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task. Finally, we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.<p><p>The second approach involves the use of firefly-inspired synchronization to allow the presence of faulty robots to be determined by other non-faulty robots in a swarm robotic system. We take inspiration from the synchronized flashing behavior observed in some species of fireflies. Each robot flashes by lighting up its on-board red LEDs and neighboring robots are driven to flash in synchrony. The robots always interpret the absence of flashing by a particular robot as an indication that the robot has a fault. A faulty robot can stop flashing periodically for one of two reasons. The fault itself can render the robot unable to flash periodically.<p>Alternatively, the faulty robot might be able to detect the fault itself using endogenous fault detection and decide to stop flashing.<p>Thus, catastrophic faults in a robot can be directly detected by its peers, while the presence of less serious faults can be detected by the faulty robot itself, and actively communicated to neighboring robots. We explore the performance of the proposed algorithm both on a real world swarm robotic system and in simulation. We show that failed robots are detected correctly and in a timely manner, and we show that a system composed of robots with simulated self-repair capabilities can survive relatively high failure rates.<p><p>We conclude that i) fault injection and learning can give robots the capacity to detect faults that occur in themselves, and that ii) firefly-inspired synchronization can enable robots in a swarm robotic system to detect and communicate faults.<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

In this thesis, we examine algorithms for the division of labour in a group of robot. The algorithms make no use of direct communication. Instead, they are based only on the interactions among the robots and between the group and the environment.<p><p>Division of labour is the mechanism that decides how many robots shall be used to perform a task. The efficiency of the group of robots depends in fact on the number of robots involved in a task. If too few robots are used to achieve a task, they might not be successful or might perform poorly. If too many robots are used, it might be a waste of resources. The number of robots to use might be decided a priori by the system designer. More interestingly, the group of robots might autonomously select how many and which robots to use. In this thesis, we study algorithms of the latter type.<p><p>The robotic literature offers already some solutions, but most of them use a form of direct communication between agents. Direct, or explicit, communication between the robots is usually considered a necessary condition for co-ordination. Recent studies have questioned this assumption. The claim is based on observations of animal colonies, e.g. ants and termites. They can effectively co-operate without directly communicating, but using indirect forms of communication like stigmergy. Because they do not rely on communication, such colonies show robust behaviours at group level, a condition that one wishes also for groups of robots. Algorithms for robot co-ordination without direct communication have been proposed in the last few years. They are interesting not only because they are a stimulating intellectual challenge, but also because they address a situation that might likely occur when using robots for real-world out-door applications. Unfortunately, they are still poorly studied.<p><p>This thesis helps the understanding and the development of such algorithms. We start from a specific case to learn its characteristics. Then we improve our understandings through comparisons with other solutions, and finally we port everything into another domain.<p><p>We first study an algorithm for division of labour that was inspired by ants' foraging. We test the algorithm in an application similar to ants' foraging: prey retrieval. We prove that the model used for ants' foraging can be effective also in real conditions. Our analysis allows us to understand the underlying mechanisms of the division of labour and to define some way of measuring it.<p><p>Using this knowledge, we continue by comparing the ant-inspired algorithm with similar solutions that can be found in the literature and by assessing their differences. In performing these comparisons, we take care of using a formal methodology that allows us to spare resources. Namely, we use concepts of experiment design to reduce the number of experiments with real robots, without losing significance in the results.<p><p>Finally, we apply and port what we previously learnt into another application: Sensor/Actor Networks (SANETs). We develop an architecture for division of labour that is based on the same mechanisms as the ants' foraging model. Although the individuals in the SANET can communicate, the communication channel might be overloaded. Therefore, the agents of a SANET shall be able to co-ordinate without accessing the communication channel. / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

Research in swarm robotics focuses mostly on how robots interact and cooperate to perform tasks, rather than on the details of task execution. As a consequence, researchers often consider abstract tasks in their experimental work. For example, foraging is often studied without physically handling objects: the retrieval of an object from a source to a destination is abstracted into a trip between the two locations---no object is physically transported. Despite being commonly used, so far task abstraction has only been implemented in an ad hoc fashion.<p><p>In this dissertation, I propose a collection of tools for flexible and reproducible task abstraction. At the core of this collection is a physical device that serves as an abstraction of a single-robot task to be performed by an e-puck robot. I call this device the TAM, an acronym for "task abstraction module". A complex multi-robot task can be abstracted using a group of TAMs by first modeling the task as the set of its constituent single-robot subtasks and then representing each subtask with a TAM. I propose a novel approach to modeling complex tasks and a framework for controlling a group of TAMs such that the behavior of the group implements the model of the complex task.<p><p>The combination of the TAM, the modeling approach, and the control framework forms a collection of tools for conducting research in swarm robotics. These tools enable research on cooperative behaviors and complex tasks with simple, cost-effective robots such as the e-puck - research that would be difficult and costly to conduct using specialized robots or ad hoc solutions to task abstraction. I present proof-of-concept experiments and several studies that use the TAM for task abstraction in order to illustrate the variety of tasks that can be studied with the proposed tools.<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished