The goal of the research activities presented in this thesis is the design of intelligent behaviours for a complex robotic system, which is composed of a swarm of autonomous units. Inspired by the organisational skills of social insects, we are particularly interested in the study of collective behaviours based on self-organisation.<p><p>The problem of designing self-organising behaviours for a swarm of robots is tackled resorting to artificial evolution, which proceeds in a bottom-up direction by first defining the controllers at the individual level and then testing their effect at the collective level. In this way, it is possible to bypass the difficulties encountered in the decomposition of the global behaviour into individual ones, and the further encoding of the individual behaviours into the controllers' rules. In the experiments presented in this thesis, we show that this approach is viable, as it produces efficient individual controllers and robust self-organising behaviours. To the best of our knowledge, our experiments are the only example of evolved self-organising behaviours that are successfully tested on a physical robotic platform.<p><p>Besides the engineering value, the evolution of self-organising behaviours for a swarm of robots also provides a mean for the understanding of those biological processes that were a fundamental source of inspiration in the first place. In this perspective, the experiments presented in this thesis can be considered an interesting instance of a synthetic approach to the study of collective intelligence and, more in general, of Cognitive Science.<p> / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

Swarm robotics is the study of developing and controlling large groups of robots. Collectives of robots possess advantages over single robots such as being robust to mission failures due to single-robot errors. Experimental research in swarm robotics is currently limited by swarm robotic technology. Current swarm robotic systems are either small groups of sophisticated robots or large groups of simple robots due to manufacturing overhead, functionality-cost dependencies, and their need to avoid collisions, amongst others. It is therefore useful to develop a swarm robotic system that is easy to manufacture, that utilises its sensors beyond standard usage, and that allows for physical interactions. In this work, I introduce a new type of low-friction locomotion and show its first implementation in the HoverBot system. The HoverBot system consists of an air-levitation and magnet table, and a HoverBot agent. HoverBots are levitating circuit boards which are equipped with an array of planar coils and a Hall-effect sensor. HoverBot uses its coils to pull itself towards magnetic anchors that are embedded into a levitation table. These robots consist of a Printed Circuit Board (PCB), surface mount components, and a battery. HoverBots are easily manufacturable, robots can be ordered populated; the assembly consists of plugging in a battery to a robot. I demonstrate how HoverBot's low-cost hardware can be used beyond its standard functionality. HoverBot's magnetic field readouts from its Hall-effect sensor can be associated with successful movement, robot rotation and collision measurands. I build a time series classifier based on these magnetic field readouts, I modify and apply signal processing techniques to enable the online classification of the time-variant magnetic field measurements on HoverBot's low-cost microcontroller. This method allows HoverBot to detect rotations, successful movements, and collisions by utilising readouts from its single Hall-effect sensor. I discuss how this classification method could be applied to other sensors and demonstrate how HoverBots can utilise their classifier to create an occupancy grid map. HoverBots use their multi-functional sensing capabilities to determine whether they moved successfully or collided with a static object to map their environment. HoverBots execute an "explore-and-return-to-nest" strategy to deal with their sensor and locomotion noise. Each robot is assigned to a nest (landmark); robots leave their nests, move n steps, return and share their observations. Over time, a group of four HoverBots collectively builds a probabilistic belief over its environment. In summary, I build manufacturable swarm robots that detect collisions through a time series classifier and map their environment by colliding with their surroundings. My work on swarm robotic technology pushes swarm robotics research towards studies on collision-dependent behaviours, a research niche that has been barely studied. Collision events occur more often in dense areas and/or large groups, circumstances that swarm robots experience. Large groups of robots with collision-dependent behaviours could become a research tool to help invent and test novel distributed algorithms, to understand the dependencies between local to global (emergent) behaviours and more generally the science of complex systems. Such studies could become tremendously useful for the execution of large-scale swarm applications such as the search and rescue of survivors after a natural disaster.

CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / Em diversas situações, o volume de tarefas a serem cumpridas não pode ser realizado por um único robô. Assim, um campo que tem despertado crescente interesse é a investigação do comportamento de enxame de robôs de busca. Estratégias de cooperação e controle desse enxame devem ser consideradas para um desempenho eficiente do time de robôs. Existem várias técnicas clássicas em inteligência artificial que são capazes de resolver este problema. Neste trabalho um conjunto de técnicas bio-inspiradas, que engloba um modelo baseado em autômatos celulares com memória e feromônio invertido, foi considerado inicialmente para coordenar um time de robôs na tarefa de forrageamento para ambientes previamente conhecidos. Os robôs do time compartilham o mesmo ambiente, comunicando-se através do feromônio invertido, que é depositado por todos os agentes a cada passo de tempo, resultando em forças de repulsão e maior cobertura do ambiente. Por outro lado, o processo de retorno para o ninho é baseado no comportamento social observado no processo de evacuação de pedestres, resultando em forças de atração. Todos os movimentos deste processo são de primeira escolha e a resolução de conflitos proporciona uma característica não-determinista ao modelo. Posteriormente, o modelo base foi adaptado para a aplicação nas tarefas de coleta seletiva e busca e resgate. Os resultados das simulações foram apresentados em diferentes condições de ambiente. Além disso, parâmetros como quantidade e disposição da comida, posição dos ninhos e largura, constantes relacionadas ao feromônio, e tamanho da memória foram analisados nos experimentos. Em seguida, o modelo base proposto neste trabalho para tarefa de forrageamento, foi implementado usando os robôs e-Puck no ambiente de simulação Webots, com as devidas adaptações. Por fim, uma análise teórica do modelo investigado foi analisado através da teoria dos grafos e das filas. O método proposto neste trabalho mostrou-se eficiente e passível de ser implementado num alto nível de paralelismo e distribuição. Assim, o modelo torna-se interessante para a aplicação em outras tarefas robóticas, especialmente em problemas que envolvam busca multi-objetiva paralela. / In several situations, the volume of tasks to be accomplished can not be performed by a single robot. Thus, a field that has attracted growing interest is the behavior investigation of the search swarm robots. Cooperation and control strategies of this swarm should be considered for an efficient performance of the robot team. There are several classic techniques in artificial intelligence that are able to solve this problem. In this work a set of bio-inspired techniques, which includes a model based on cellular automata with memory and inverted pheromone, was initially considered to coordinate a team of robots in the task of foraging to previously known environments. The team's robots share the same environment, communicating through the inverted pheromone, which is deposited by all agents at each step of time, resulting in repulsive forces and increasing environmental coverage. On the other hand, the return process to the nest is based on the social behavior observed in the process of pedestrian evacuation, resulting in forces of attraction. All movements in this process are first choice and conflict resolution provides a non-deterministic characteristic to the model. Subsequently, the base model was adapted for the application in the tasks of selective collection and search and rescue. The results of the simulations were presented under different environment conditions. In addition, parameters such as amount and arrangement of food, nest position and width, pheromone-related constants, and memory size were analyzed in the experiments. Then, the base model proposed in this work for foraging task, was implemented using the e-Puck robots in the simulation environment Webots, with the appropriate adaptations. Finally, a theoretical analysis of the investigated model was analyzed through the graphs and queuing theory. The method proposed in this work proved to be efficient and capable of being implemented at a high level of parallelism and distribution. Thus, the model becomes interesting for the application in other robotic tasks, especially in problems that involve parallel multi-objective search. / Tese (Doutorado)