Recent rapid advances in computing, communication, sensing, and actuation, together with miniaturization technologies, have offered unprecedented opportunities to employ large numbers of autonomous vehicles (air, ground, and water) working cooperatively to accomplish a mission. Cooperative control of such multi-agent dynamical systems has potential impact on numerous civilian, homeland security, and military applications. Compared with single-agent control problems, new theoretical and practical challenges emerge and need to be addressed in cooperative control of multiagent dynamical systems, including but not limited to problem size, task coupling, limited computational resources at individual agent level, communication constraints, and the need for real-time obstacle/collision avoidance. In order to address these challenges, a unified optimal multi-agent cooperative control strategy is proposed to formulate the multi-objective cooperative control problem into one unified optimal control framework. Many cooperative behaviors, such as consensus, cooperative tracking, formation, obstacle/collision avoidance, or flocking with cohesion and repulsion, can be treated in one optimization process. An innovative inverse optimal control approach is utilized to include these cooperative objectives in derived cost functions such that a closedorm cooperative control law can be obtained. In addition, the control law is distributed and only depends on the local neighboring agents’ information. Therefore, this new method does not demand intensive computational load and is easy for real-time onboard implementation. Furthermore, it is very scalable to large multi-agent cooperative dynamical systems. The closed-loop asymptotic stability and optimality are theoretically proved. Simulations based on MATLAB are conducted to validate the cooperative behaviors including consensus, Rendezvous, formation flying, and flocking, as well as the obstacle avoidance performance.

Active sensors, such as radar, lidar and sonar, emit a signal into the environment and gather information from its reflection. In contrast, passive sensors such as cameras and microphones rely on the signals emitted from the environment. In the current application of active sensors in multi-agent autonomous systems, agents only rely on their own active sensing and filter out any information available passively. However, fusing passive and active sensing information may improve the accuracy of the agents. Also, there is evidence that bats who use biosonar eavesdrop on a conspecific's echolocation sound, which shows a successful example of implementing active and passive sonar sensor fusion in nature. We studied the effect of such information fusion in the framework of two problems: the collective behavior in a multi-agent system using the Vicsek model and the canonical robotics problem of Simultaneous Localization And Mapping (SLAM). Collective behavior refers to emergence of a complex behavior in a group of individuals through local interaction. The Vicsek model is a well-established flocking model based on alignment of individuals with their neighbors in the presence of noise. We studied the aligned motion in a group in which the agents employ both active and passive sensing. Our study shows that the group behavior is less sensitive to measurement accuracy compared to modeling precision. Therefore, using measurement values of the noisier passive sonar can be beneficial. In addition, the group alignment is improved when the passive measurements are not dramatically noisier than active measurements. In the SLAM problem, a robot scans an unknown environment building a map and simultaneously localizing itself within that map. We studied a landmark-based SLAM problem in which the robot uses active and passive sensing strategies. The information provided passively can improve the accuracy of the active sensing measurements and compensate for its blind spot. We developed an estimation algorithm using Extended Kalman Filter and employed Monte Carlo simulation to find a parameter region in which fusing passive and active sonar information improves the performance of the robot. Our analysis shows this region is aligned within the common range of active sonar parameters. / Doctor of Philosophy / Group behavior is a fascinating phenomenon in animal groups such as bird flocks, fish schools, bee colonies and fireflies. For instance, many species of fireflies synchronize their flashing when they bio-luminesce. This synchronization pattern is a group behavior created as a result of local interaction formed by sensing individuals in the group. The research question for this dissertation is inspired by comes from group behavior in bats. Bats use echolocation to perceive the environment. They make a sound and listen to the echo of the sound coming back from objects and by analyzing the echo, they can get information about their surroundings. It has been observed that bats may also use the echo of other bats' sound to perceive their environment. In other words they use two different sensors, one is called active sonar since they actively make the sound and listen to its echoes, and the other one is called passive sonar since they just passively listen to the sound generated by other bats. If this information is useful, can we exploit that in design of engineered systems? We investigated these questions using numerical simulation to solve two test bed problems. The first problem is based on a mathematical flocking model in which the individuals in the group align through local interaction. We found out that eavesdropping improves the alignment of the group within a range of parameters in the model which are relevant to the sensing capabilities of the sonar sensors. The other problem is a canonical robotics problem known as the simultaneous localization and mapping (SLAM). In this problem, a robot searches an unknown environment and creates a map of the environment (mapping) and reports the path it takes within the map (localization). We found out that when the robot uses both passive and active sonar, depending on the accuracy of the two sensing approaches, it can improve the accuracy of both the generated map and the robot's path.

Linkages between Complex Adaptive Systems (CAS) thinking and efficiency analysis remain in their infancy. This research associates the basic building blocks of the CAS 'flocking' metaphor with the essential building block concepts of Data Envelopment Analysis (DEA). Within a proposed framework DEA "decision-making units" (DMUs) are represented as agents in the agent-based modeling (ABM) paradigm. Guided by simple rules, agent DMUs representing business units of a larger management system, 'align' with one another to achieve mutual protection/risk reduction and 'cohere' with the most efficient DMUs among them to achieve the greatest possible efficiency in the least possible time. Analysis of the resulting patterns of behavior can provide policy insights that are both evidence-based and intuitive. This research introduces a consistent methodology that will be called here the Complex Adaptive Productive Efficiency Method (CAPEM) and employs it to bridge these domains. This research formalizes CAPEM mathematically and graphically. It then conducts experimentation employing using the resulting CAPEM simulation using data of a sample of electric power plants obtained from Rungsuriyawiboon and Stefanou (2003). Guided by rules, individual agent DMUs (power plants) representing business units of a larger management system,'align' with one another to achieve mutual protection/risk reduction and 'cohere' with the most efficient DMUs among them to achieve the greatest possible efficiency in the least possible time. Using a CAS ABM simulation, it is found that the flocking rules (alignment, cohesion and separation), taken individually and in selected combinations, increased the mean technical efficiency of the power plant population and conversely decreased the time to reach the frontier. It is found however that these effects were limited to a smaller than expected sub-set of these combinations of the flocking factors. Having been successful in finding even a limited sub-set of flocking rules that increased efficiency was sufficient to support the hypotheses and conclude that employing the flocking metaphor offers useful options to decision-makers for increasing the efficiency of management systems. / Ph. D.

O aprendizado de máquina consiste de conceitos e técnicas que permitem aos computadores melhorar seu desempenho com a experiência, ou, em outras palavras, aprender com dados. Um dos principais tópicos do aprendizado de máquina é o agrupamento de dados que, como o nome sugere, procura agrupar os dados de acordo com sua similaridade. Apesar de sua definição relativamente simples, o agrupamento é uma tarefa computacionalmente complexa, tornando proibitivo o emprego de algoritmos exaustivos, na busca pela solução ótima do problema. A importância do agrupamento de dados, aliada aos seus desafios, faz desse campo um ambiente de intensa pesquisa. Também a classe de fenômenos naturais conhecida como comportamento coletivo tem despertado muito interesse. Isso decorre da observação de um estado organizado e global que surge espontaneamente das interações locais presentes em grandes grupos de indivíduos, caracterizando, pois, o que se chama auto-organização ou emergência, para ser mais preciso. Os desafios intrínsecos e a relevância do tema vêm motivando sua pesquisa em diversos ramos da ciência e da engenharia. Ao mesmo tempo, técnicas baseadas em comportamento coletivo vêm sendo empregadas em tarefas de aprendizado de máquina, mostrando-se promissoras e ganhando bastante atenção. No presente trabalho, objetivou-se o desenvolvimento de técnicas de agrupamento baseadas em comportamento coletivo. Faz-se cada item do conjunto de dados corresponder a um indivíduo, definem-se as leis de interação local, e então os indivíduos são colocados a interagir entre si, de modo que os padrões que surgem reflitam os padrões originalmente presentes no conjunto de dados. Abordagens baseadas em dinâmica de troca de energia foram propostas. Os dados permanecem fixos em seu espaço de atributos, mas carregam certa informação a energia , a qual é progressivamente trocada entre eles. Os grupos são estabelecidos entre dados que tomam estados de energia semelhantes. Este trabalho abordou também o aprendizado semissupervisionado, cuja tarefa é rotular dados em bases parcialmente rotuladas. Nesse caso, foi adotada uma abordagem baseada na movimentação dos próprios dados pelo espaço de atributos. Procurou-se, durante todo este trabalho, não apenas propor novas técnicas de aprendizado, mas principalmente, por meio de muitas simulações e ilustrações, mostrar como elas se comportam em diferentes cenários, num esforço em mostrar onde reside a vantagem de se utilizar a dinâmica coletiva na concepção dessas técnicas / Machine learning consists of concepts and techniques that enable computers to improve their performance with experience, i.e., enable computers to learn from data. Data clustering (or just clustering) is one of its main topics, which aims to group data according to their similarities. Regardless of its simple definition, clustering is a complex computational task. Its relevance and challenges make this field an environment of intense research. The class of natural phenomena known as collective behavior has also attracted much interest. This is due to the observation that global patterns may spontaneously arise from local interactions among large groups of individuals, what is know as self-organization (or emergence). The challenges and relevance of the subject are encouraging its research in many branches of science and engineering. At the same time, techniques based on collective behavior are being employed in machine learning tasks, showing to be promising. The objective of the present work was to develop clustering techniques based on collective behavior. Each dataset item corresponds to an individual. Once the local interactions are defined, the individuals begin to interact with each other. It is expected that the patterns arising from these interactions match the patterns originally present in the dataset. Approaches based on dynamics of energy exchange have been proposed. The data are kept fixed in their feature space, but they carry some sort of information (the energy), which is progressively exchanged among them. The groups are established among data that take similar energy states. This work has also addressed the semi-supervised learning task, which aims to label data in partially labeled datasets. In this case, it has been proposed an approach based on the motion of the data themselves around the feature space. More than just providing new machine learning techniques, this research has tried to show how the techniques behave in different scenarios, in an effort to show where lies the advantage of using collective dynamics in the design of such techniques

De nos jours, les applications utilisant des quadrirotors autonomes sont en plein essor. La surveillance et la sécurité de sites industriels ou sensibles, de zones géographiques pour l’agriculture par exemple sont quelques-unes des applications les plus célèbres des véhicules aériens sans pilote (UAV). Actuellement, certains chercheurs et scientifiques se concentrent sur le déploiement multi-drones pour l’inspection et la surveillance de vastes zones. L’objectif de cette thèse est de concevoir des algorithmes afin de réaliser une commande de vol en formation distribuée/décentralisée de multi-UAVs en temps réel dans une perspective de systèmes de systèmes. Tout d’abord, nous avons passé en revue certains travaux récents de la littérature sur la commande de vol en formation et la commande de quadrirotors. Nous avons présenté une brève introduction sur les systèmes de systèmes, leur définition et leurs caractéristiques. Ensuite, nous avons introduit la commande de vol en formation avec ses structures les plus utilisées dans la littérature. Nous avons alors présenté quelques travaux existants traitant du flocking (comportement de regroupement en flotte), les méthodes de modélisation les plus utilisés pour les quadrirotors et quelques approches de commande les plus utilisées pour stabiliser des quadrirotors. Deuxièmement, nous avons utilisé la structure de la commande comportementale pour réaliser un vol en formation de plusieurs UAVs. Nous avons conçu un comportement pour réaliser le vol en formation de multi-UAVs sans fragmentation. Le comportement proposé traite le problème flocking dans une perspective globale, c’est-à-dire, nous avons inclus une tendance dans chaque drone pour former une formation. Les défis des Systèmes de systèmes nous a motivés à chercher des algorithmes de flocking et de consensus introduits dans la littérature qui peuvent être utiles pour répondre à ces défis. Cela nous a amenés à proposer quatre lois de commande en visant à être compatibles avec le modèle non linéaire des quadrirotors et pouvant être expérimentés sur des plates-formes réelles. Les lois de commande ont été exécutées à bord de chaque quadrirotor dans la formation et chaque quadrirotor interagit avec ses voisins pour assurer un vol en formation sans collision. Enfin, nous avons validé nos lois de commande par des simulations et des expériences en temps réel. Pour la simulation, nous avons utilisé un simulateur de multi quadrirotors développé au laboratoire Heudiasyc. Pour les expériences, nous avons mis en œuvre nos lois de contrôle sur des quadrirotors ArDrone2 évolués dans un environnement intérieur équipé d’un système de capture de mouvement (Optitrack). / Nowadays, applications of autonomous quadrotors are increasing rapidly. Surveillance and security of industrial sites, geographical zones for agriculture for example are some popular applications of Unmanned Aerial Vehicles (UAVs). Nowadays, researchers and scientists focus on the deployment of multi-UAVs for the inspection and the surveillance of large areas. The objective of this thesis is to design algorithms and techniques to perform a real-time distributed/decentralized multi-UAVs flight formation control, from a system of systems perspective. Firstly, we reviewed recent works of the literature about flight formation control and the control of quadrotors. We presented a brief introduction about systems of systems, their definition and characteristics. Then, we introduced the flight formation control with its most used structures in the literature, some existing works dealing with flocking. Finally, we presented the most used modeling methodologies for quadrotors and some control approaches that are used to stabilize quadrotors. Secondly, we used the behavioral-based control structure to achieve a multiple UAV flocking. We conceived a behavior intending to address the control design towards a successful achievement of the flocking task without fragmentation. The proposed behavior treats the flocking problem from a global perspective, that is, we included a tendency of separated UAVs to form a flock.System of systems challenges motivated us to look for flocking and consensus algorithms introduced in the literature that could be helpful to answer to these challenges. This led us to propose four flocking control laws aiming at being compatible with the nonlinear model of quadrotors and at being implemented on experimental platforms. The control laws were run aboard each quadrotor in the flock. By running the control law, each quadrotor interacts with its neighbors to ensure a collision-free flocking. Finally, we validated our proposed control laws by simulations and real-time experiments. For the simulation, we used a PC-based simulator of flock of multiple quadrotors which was developed at Heudiasyc laboratory. For experiments, we implemented our control laws on ArDrone2 quadrotors evolved in an indoor environment equipped with an Optitrack motion capture system.

Dans cette thèse, on s'intéresse à des systèmes stochastiques modélisant un des phénomènes biologiques les plus mystérieux, les mouvements collectifs de populations. Pour un groupe de N individus, vus comme des particules sans poids ni volume, on étudie deux types de comportements asymptotiques : d'un côté, en temps long, les propriétés d'ergodicité et de flocking, de l'autre, quand le nombre de particules N tend vers l'infini, les phénomènes de propagation du chaos. Le modèle, déterministe, de Cucker-Smale, un modèle cinétique de champ moyen pour une population sans structure hiérarchique, est notre point de départ : les deux premiers chapitres sont consacrés à la compréhension de diverses dynamiques stochastiques qui s'en inspirent, du bruit étant rajouté sous différentes formes. Le troisième chapitre, originellement une tentative d'amélioration de ces résultats, est basé sur la méthode du développement en amas, un outil de physique statistique. On prouve l'ergodicité exponentielle de certains processus non- markoviens à drift non-régulier. Dans la dernière partie, on démontre l'existence d'une solution, unique dans un certain sens, pour un système stochastique de particules associé au modèle chimiotactique de Keller et Segel. / In this thesis, stochastic dynamics modelling collective motions of populations, one of the most mysterious type of biological phenomena, are considered. For a system of N particle-like individuals, two kinds of asymptotic behaviours are studied: ergodicity and flocking properties, in long time, and propagation of chaos, when the number N of agents goes to infinity. Cucker and Smale, deterministic, mean-field kinetic model for a population without a hierarchical structure is the starting point of our journey: the fist two chapters are dedicated to the understanding of various stochastic dynamics it inspires, with random noise added in different ways. The third chapter, an attempt to improve those results, is built upon the cluster expansion method, a technique from statistical mechanics. Exponential ergodicity is obtained for a class of non-Markovian process with non-regular drift. In the final part, the focus shifts onto a stochastic system of interacting particles derived from Keller and Segel 2-D parabolic-elliptic model for chemotaxis. Existence and weak uniqueness are proven.

O aprendizado de máquina consiste de conceitos e técnicas que permitem aos computadores melhorar seu desempenho com a experiência, ou, em outras palavras, aprender com dados. Um dos principais tópicos do aprendizado de máquina é o agrupamento de dados que, como o nome sugere, procura agrupar os dados de acordo com sua similaridade. Apesar de sua definição relativamente simples, o agrupamento é uma tarefa computacionalmente complexa, tornando proibitivo o emprego de algoritmos exaustivos, na busca pela solução ótima do problema. A importância do agrupamento de dados, aliada aos seus desafios, faz desse campo um ambiente de intensa pesquisa. Também a classe de fenômenos naturais conhecida como comportamento coletivo tem despertado muito interesse. Isso decorre da observação de um estado organizado e global que surge espontaneamente das interações locais presentes em grandes grupos de indivíduos, caracterizando, pois, o que se chama auto-organização ou emergência, para ser mais preciso. Os desafios intrínsecos e a relevância do tema vêm motivando sua pesquisa em diversos ramos da ciência e da engenharia. Ao mesmo tempo, técnicas baseadas em comportamento coletivo vêm sendo empregadas em tarefas de aprendizado de máquina, mostrando-se promissoras e ganhando bastante atenção. No presente trabalho, objetivou-se o desenvolvimento de técnicas de agrupamento baseadas em comportamento coletivo. Faz-se cada item do conjunto de dados corresponder a um indivíduo, definem-se as leis de interação local, e então os indivíduos são colocados a interagir entre si, de modo que os padrões que surgem reflitam os padrões originalmente presentes no conjunto de dados. Abordagens baseadas em dinâmica de troca de energia foram propostas. Os dados permanecem fixos em seu espaço de atributos, mas carregam certa informação a energia , a qual é progressivamente trocada entre eles. Os grupos são estabelecidos entre dados que tomam estados de energia semelhantes. Este trabalho abordou também o aprendizado semissupervisionado, cuja tarefa é rotular dados em bases parcialmente rotuladas. Nesse caso, foi adotada uma abordagem baseada na movimentação dos próprios dados pelo espaço de atributos. Procurou-se, durante todo este trabalho, não apenas propor novas técnicas de aprendizado, mas principalmente, por meio de muitas simulações e ilustrações, mostrar como elas se comportam em diferentes cenários, num esforço em mostrar onde reside a vantagem de se utilizar a dinâmica coletiva na concepção dessas técnicas / Machine learning consists of concepts and techniques that enable computers to improve their performance with experience, i.e., enable computers to learn from data. Data clustering (or just clustering) is one of its main topics, which aims to group data according to their similarities. Regardless of its simple definition, clustering is a complex computational task. Its relevance and challenges make this field an environment of intense research. The class of natural phenomena known as collective behavior has also attracted much interest. This is due to the observation that global patterns may spontaneously arise from local interactions among large groups of individuals, what is know as self-organization (or emergence). The challenges and relevance of the subject are encouraging its research in many branches of science and engineering. At the same time, techniques based on collective behavior are being employed in machine learning tasks, showing to be promising. The objective of the present work was to develop clustering techniques based on collective behavior. Each dataset item corresponds to an individual. Once the local interactions are defined, the individuals begin to interact with each other. It is expected that the patterns arising from these interactions match the patterns originally present in the dataset. Approaches based on dynamics of energy exchange have been proposed. The data are kept fixed in their feature space, but they carry some sort of information (the energy), which is progressively exchanged among them. The groups are established among data that take similar energy states. This work has also addressed the semi-supervised learning task, which aims to label data in partially labeled datasets. In this case, it has been proposed an approach based on the motion of the data themselves around the feature space. More than just providing new machine learning techniques, this research has tried to show how the techniques behave in different scenarios, in an effort to show where lies the advantage of using collective dynamics in the design of such techniques

<p>Östby waterworks in the community of Kramfors has since 2003 had problems of fast clogging of the slow sand filters. As the clogging appears more often, they must be cleaned more frequently which has made the drinking water preparation difficult. Wintertime it has sometimes been impossible to clean the filters which has led to their closing and this has influenced the water quality negatively.</p><p>The waterworks is a surface waterworks which takes its raw water from the lake Sjöbysjön. The water is flocculated and filtered in a contact filter with the flocculent EKOFLOCK 91. Thereafter it is alkalinized before it reaches the outdoors placed slow sand filters. After the filters the water is alkalinized and disinfected before it reaches the customers.</p><p>This thesis work has looked into which factors influence the clogging and trials have been made in order to optimize the waterworks and thereby reduce the clogging. The raw water has been studied with aspect to biology and chemistry, the filtered water has been studied with aspect to chemistry and also the pressures in the slow sand filters have been studied.</p><p>The colour of the raw water from Sjöbysjön and its catchment area has increased since the beginning of the 90-ies and also the bio volume has increased in the lake.</p><p>The contents of aluminium before the slow sand filters are far higher than the contents after, which leads to the conclusion that aluminium is accumulated in the filters. The differential pressure over the sand bed increases with time after a cleaning. This indicates an accumulation of particles which increases with the load. Observations of the filter surface before cleaning showed that it was covered by a brown, jelly-like film. The internal resistance in the filter beds increases successively after a cleaning and one month after cleaning it is highest in the upper part of the sand bed. All this points to that flocculated aluminium is gathered in the slow sand filters, on the surface, causing clogging.</p><p>While the thesis work has been going on a process of alkalinizing before the contact filters has been reengaged and this has influenced the flocking of organic materials. When raising the pH the dose of flocculent was increased and this combined increased the amount of flocculated material. The contact filters could not bear this increased amount of flock load but showed instead a breakthrough.</p><p>In a few lab scale trials the flocking pH was varied as well as the dose flocculent to the raw water and after this the water was filtrated. A tendency was seen that the separation of aluminium, colour and turbidity increased with increasing pH and dose flocculent. At the pH 6.2 and the chemical dose of 60 g/m3 the content of aluminium, the colour and the turbidity showed the lowest values in the filtrate.</p> / <p>Vattenverket i Östby, Kramfors kommun, har sedan 2003 haft problem med för snabba igensättningar i långsamfiltren. Ju oftare de sätter igen desto mer frekvent måste de rensas vilket har försvårat dricksvattenberedningen. Vintertid har rensningar ibland inte kunnat utföras och långsamfiltren har därför stängts av vilket har påverkat vattenkvaliteten negativt.</p><p>Verket är ett ytvattenverk som tar sitt råvatten från Sjöbysjön. Vattnet flockas och filtreras i kontaktfilter med fällningskemikalien EKOFLOCK 91. Därefter mellanalkaliniseras det innan det går till de utomhus placerade långsamfiltren. Efter långsamfiltren efteralkaliniseras och desinficeras det innan det når konsumenterna.</p><p>I detta examensarbete har faktorer som påverkar igensättningarna undersökts och försök har också gjorts för att optimera driften och därmed minska igensättningarna. Råvattnet har undersökts avseende dess biologi och kemi, filtraten i verken har undersökts med avseende på deras kemi och tryckbildningar i långsamfiltren har studerats.</p><p>Färgtalet i råvattentäkten Sjöbysjön och dess tillrinningsområde har ökat sedan början av 90- talet och dessutom har biovolymen i sjön ökat.</p><p>Halterna aluminium före långsamfiltren är mycket högre än halterna efter, vilket antyder att aluminium ansamlas i filtren. Differentialtrycket över sandbädden ökar med tiden efter en rensning. Detta tyder på en ansamling av partiklar som ökar med belastningen. Observationer av filterytan innan rensning visade att den täcktes av en brun geléaktig hinna. Motståndet i filtren ökar successivt efter en rensning och är en månad efter rensning störst i den övre delen av sandbädden. Allt detta pekar på att aluminiumflock ansamlas i långsamfiltren, på ytan, och orsakar igensättningarna.</p><p>Under examensarbetets gång har en föralkalinisering återinförts i vattenverkets process och den har påverkat fällningen av det organiska materialet. Då pH höjts har också dosen fällningskemikalie ökats vilket har ökat mängden flock. Denna ökade flockmängd har kontaktfiltren inte kunnat bära utan istället släppt igenom.</p><p>I några försök i labbskala varierades fällnings-pH och dos fällningskemikalie till råvatten med en efterföljande filtrering. Tendenser som kunde ses var att avskiljningen av aluminium, färg och turbiditet ökade med ökat pH och ökad dos fällningskemikalie. Vid pH 6,2 och kemikaliedos på 60 g/m3 var aluminiumhalterna, färgen och turbiditeten som minst i filtratet.</p>

The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customised implementation of Reynolds’ flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the ”reality gap” issue) are used on the real robots.

Östby waterworks in the community of Kramfors has since 2003 had problems of fast clogging of the slow sand filters. As the clogging appears more often, they must be cleaned more frequently which has made the drinking water preparation difficult. Wintertime it has sometimes been impossible to clean the filters which has led to their closing and this has influenced the water quality negatively. The waterworks is a surface waterworks which takes its raw water from the lake Sjöbysjön. The water is flocculated and filtered in a contact filter with the flocculent EKOFLOCK 91. Thereafter it is alkalinized before it reaches the outdoors placed slow sand filters. After the filters the water is alkalinized and disinfected before it reaches the customers. This thesis work has looked into which factors influence the clogging and trials have been made in order to optimize the waterworks and thereby reduce the clogging. The raw water has been studied with aspect to biology and chemistry, the filtered water has been studied with aspect to chemistry and also the pressures in the slow sand filters have been studied. The colour of the raw water from Sjöbysjön and its catchment area has increased since the beginning of the 90-ies and also the bio volume has increased in the lake. The contents of aluminium before the slow sand filters are far higher than the contents after, which leads to the conclusion that aluminium is accumulated in the filters. The differential pressure over the sand bed increases with time after a cleaning. This indicates an accumulation of particles which increases with the load. Observations of the filter surface before cleaning showed that it was covered by a brown, jelly-like film. The internal resistance in the filter beds increases successively after a cleaning and one month after cleaning it is highest in the upper part of the sand bed. All this points to that flocculated aluminium is gathered in the slow sand filters, on the surface, causing clogging. While the thesis work has been going on a process of alkalinizing before the contact filters has been reengaged and this has influenced the flocking of organic materials. When raising the pH the dose of flocculent was increased and this combined increased the amount of flocculated material. The contact filters could not bear this increased amount of flock load but showed instead a breakthrough. In a few lab scale trials the flocking pH was varied as well as the dose flocculent to the raw water and after this the water was filtrated. A tendency was seen that the separation of aluminium, colour and turbidity increased with increasing pH and dose flocculent. At the pH 6.2 and the chemical dose of 60 g/m3 the content of aluminium, the colour and the turbidity showed the lowest values in the filtrate. / Vattenverket i Östby, Kramfors kommun, har sedan 2003 haft problem med för snabba igensättningar i långsamfiltren. Ju oftare de sätter igen desto mer frekvent måste de rensas vilket har försvårat dricksvattenberedningen. Vintertid har rensningar ibland inte kunnat utföras och långsamfiltren har därför stängts av vilket har påverkat vattenkvaliteten negativt. Verket är ett ytvattenverk som tar sitt råvatten från Sjöbysjön. Vattnet flockas och filtreras i kontaktfilter med fällningskemikalien EKOFLOCK 91. Därefter mellanalkaliniseras det innan det går till de utomhus placerade långsamfiltren. Efter långsamfiltren efteralkaliniseras och desinficeras det innan det når konsumenterna. I detta examensarbete har faktorer som påverkar igensättningarna undersökts och försök har också gjorts för att optimera driften och därmed minska igensättningarna. Råvattnet har undersökts avseende dess biologi och kemi, filtraten i verken har undersökts med avseende på deras kemi och tryckbildningar i långsamfiltren har studerats. Färgtalet i råvattentäkten Sjöbysjön och dess tillrinningsområde har ökat sedan början av 90- talet och dessutom har biovolymen i sjön ökat. Halterna aluminium före långsamfiltren är mycket högre än halterna efter, vilket antyder att aluminium ansamlas i filtren. Differentialtrycket över sandbädden ökar med tiden efter en rensning. Detta tyder på en ansamling av partiklar som ökar med belastningen. Observationer av filterytan innan rensning visade att den täcktes av en brun geléaktig hinna. Motståndet i filtren ökar successivt efter en rensning och är en månad efter rensning störst i den övre delen av sandbädden. Allt detta pekar på att aluminiumflock ansamlas i långsamfiltren, på ytan, och orsakar igensättningarna. Under examensarbetets gång har en föralkalinisering återinförts i vattenverkets process och den har påverkat fällningen av det organiska materialet. Då pH höjts har också dosen fällningskemikalie ökats vilket har ökat mängden flock. Denna ökade flockmängd har kontaktfiltren inte kunnat bära utan istället släppt igenom. I några försök i labbskala varierades fällnings-pH och dos fällningskemikalie till råvatten med en efterföljande filtrering. Tendenser som kunde ses var att avskiljningen av aluminium, färg och turbiditet ökade med ökat pH och ökad dos fällningskemikalie. Vid pH 6,2 och kemikaliedos på 60 g/m3 var aluminiumhalterna, färgen och turbiditeten som minst i filtratet.