The behaviour of sanderlings, Calidris alba, was studied at Teesmouth, N. E. England. Flock sizes and within-flock spacing were related to factors such as substrate type, season, and prey distribution. Sightings of individually-identifiable colour-ringed birds showed that turnover was high. Individuals varied in their vagility and in the way in which they allocated their time between feeding sites. There was a high degree of consistency between winters in the distributions of individual birds. When individual sighting frequencies were taken into account, associations between individual sanderlings, in terms of both flock membership and of occurrences at the same sites on the same days, were non-significantly different from those expected by chance. Previously used methods for detecting non-random associations gave inadequate null models. Each individual appeared to make a decision about where to feed independently of the decisions made by any other particular individuals .A number of aspects of the dynamics of flocks were examined, including flock cohesion; how flocks built up and broke down; whether birds tended to move to the largest or smallest, the nearest or furthest flocks; the effects of disturbance on flock dynamics; and the relationship between group size and the distance flown between flocks. The responses of sanderlings to experimental disturbances tended to support the hypothesis that responses should be varied according to the costs and benefits of different courses of action rather than the hypothesis that birds should take flight as soon as a predator is sighted. Birds often break off from performing an activity in order to raise their heads (scan). Preening birds had shorter inter-scan intervals than feeding birds. Flock size and spacing explained only a small proportion of the variance in vigilance. Vigilance was greater in autumn than in winter. There was some evidence for both feeding and preening birds avoiding very short inter-scan intervals but not for the avoidance of long intervals. Sequences of inter-scan interval durations of preening birds were non-random.

<div>This thesis applies flocking algorithms for the distributed consensus control of a multi-agent system composed of four-Mecanum-wheeled robots. The working mechanism of flocking is an artificial potential field consisting of attractive/repulsive forces and velocity alignment. The potential function of the attractive and repulsive force is introduced to control the connected distance among agents in the network. A consensus is a group of robots in a communication network to achieve common goals, which are the agreement of position and heading angle in this thesis. The main contribution of this thesis is our proposed feasible methods to achieve consensus control for general multi-agent systems of four-Mecanum-wheeled robots.</div><div><br></div><div>With the fast development of information technology and the growing demand for data exchange around the world, the sensors and actuators of agents become more complicated and require more resources. Local communication among agents reduces the need for high material costs and lengthy installation time. This thesis established a controllable model of four-Mecanum-wheeled robots in a local communication network. An assumption is that all robots can obtain information on the relative position and heading angle difference between themselves and their neighbors. A few robots with installed GPS are directly connected to the central host. Our flocking methods under the assumed communication conditions adjust the velocities of robots by controlling the speed of Mecanum wheels.</div><div><br></div><div>This thesis simulated the proposed leader-following flocking algorithms for cases of connected formation and snake formation with different numbers of leaders. The simulation results regarding the position consensus and heading angles consensus are provided to illustrate the robustness of the proposed algorithms.</div><div><br></div>

No description available.

The implementation of autonomous, flocking crowds of background characters in the feature film “Robots” is discussed. The techniques for obstacle avoidance and goal seeking are described. An overview of the implementation of the system as part of the production pipeline for the film is also provided.

abstract: I investigate two models interacting agent systems: the first is motivated by the flocking and swarming behaviors in biological systems, while the second models opinion formation in social networks. In each setting, I define natural notions of convergence (to a ``flock" and to a ``consensus'', respectively), and study the convergence properties of each in the limit as $t \rightarrow \infty$. Specifically, I provide sufficient conditions for the convergence of both of the models, and conduct numerical experiments to study the resulting solutions. / Dissertation/Thesis / Masters Thesis Mathematics 2018

This thesis investigates bacterial motility from the mechanism permitting individual selfpropulsion to the complex collective flocking motility in Escherichia coli and Bacillus subtilis cells. Understanding bacterial swimming has intrigued scientists for decades and recently there has been a growing interest in collective swimming behaviour. The first part of this thesis reviews the characteristics of E. coli and B. subtilis cells subsequently describing the governing physics and constraints of self-propulsion in the low Reynolds regime. The second part of this thesis presents three self-contained experimental sections, examining individual swimming in non-conventional body shaped cells and subsequently focusing on concentrated bacterial swimming in normal cells. We first investigated motility in mutant spherical E. coli cells KJB24 motivated by simulations, which often model bacteria as self-propelled spheres. Somewhat unexpectedly these spherical cells do not exhibit runs and tumbles but diffuse slower than expected. As an introduction to working with microbiology and to familiarise with microbiology techniques we investigated why these spherical cells do not swim. Secondly we investigated how cellular motility varies as a function of body length by inhibiting cell division in wild-type E. coli with cephalexin; which remained motile despite body elongation. Fluorescent flagella visualization provided evidence of multiple bundle formations along the lateral walls as a mechanism to sustain motility. The average swimming velocity, body and flagella rotation rates, the number of flagella and number of flagella bundles were extracted experimentally as a function of length. The extracted experimental parameters for normal sized cells were consistent with Purcell’s model. We explored simple adaptations and scaling of this model to describe motility for filamentous cells, which agrees with experimental values. The main focus is on collective behaviour of B. subtilis by examining the onset from individual swimming to collective motility using time-lapse microscopy. Results demonstrated a smooth transition where cells self-organize into domains expanding rapidly by recruiting cells. We present advancements in B. subtilis fluorescent flagella staining which revealed unexpected multiple flagella bundle arrangements during runs, contradictory to general conjectures. Novel visualisation of flagella filaments during reversal events is presented in both E. coli and B. subtilis cells, providing experimental evidence for complex flagella ‘flipping’. Cellular reversal is hypothesized as a mechanism for quorum polarity facilitating collective swimming. We present novel flagella imaging in the setting of collective behaviour showing evidence to support quorum polarity. Subsequently we extracted the run length distributions of cells as a function of concentration, yielding a decreasing trend with increasing concentration. Using particle tracking we quantitatively extracted the mean squared displacement of swimming cells versus passive tracers at different concentrations during collective swimming, these novel results are discussed in respect to recent simulations. These presented experiments provide new insights into collective behaviour improving current understanding of this phenomenon.

Every year, certain animal and insect species flock together to make long-range migrations to reach their feeding or breeding grounds. A number of interesting observations can be made regarding this phenomenon. First, individuals tend to create large flocks, which can include millions of individuals in fishes, for these migrations. Second, migrations typically cover long distances. Third, despite all kinds of disturbances affecting the individuals during these migrations, the flocks can reach the very same breeding or feeding grounds with remarkable accuracy. Biological studies indicated that these animals mainly use the magnetic field of earth (among many other environmental cues) to determine the direction of their travel. It was also claimed that migrating in flocks has been the key factor behind the accuracy of reaching the same grounds at the end of the migration. In this thesis, we take a constructivist approach towards investigating the effects of flocking in long-range travels using a swarm of physical and simulated mobile robots. Specifically, we extend a self-organized flocking behavior that was developed by Turgut et al. (2008) that allows the long-range migration of a robotic swarm in space using the magnetic field of the earth. Using this behavior, we analyze how the accuracy of the robotic swarm reaching a particular &quot / breeding ground&quot / is affected by four factors / namely, (1) averaging through the heading alignment, (2) noise in sensing the homing direction, (3) differences in the characteristics of the individuals, and (4) disturbances caused by the proximal interactions of the robots during flocking. Through systematic experiments with physical and simulated robots, we analyze how these factors affect the accuracy along with the flock size and different sources of noise.

No description available.

Context. Simulation of nature is something that is used to immerse the player into the world of games. By adding details in the world such as birds circling in the sky or small fishes swimming in a flock, developers can improve the gaming experience for the user. More precise simulations are something that should be aspired for. This thesis will explore the boid flocking algorithm and evaluate what settings users perceive as realistic behavior for simulating schools of fish. Objectives. This thesis proposes that there should be a set of variables that reflect a more realistic behavior and through gathering data from volunteers and mapping their answers, conclude if that statement is true. Methods. A boid simulation will be run in a number of different scenarios, each differing in variables that are vCohesion, vSeparationand vAmount that make changes to the overall behavior. This behavior is then recorded and compared next to each other in a perceptual experiment with the objective of finding out the preferred settings interms of realism. Results. The experiment showed that the preferred value of vSeperation was around 50 to 60 world units. The value of vCohesion and vAmount was random to what was perceived, so their impact on realism was not significant enough. Conclusions. After running the experiment it was apparent that there was a preferred value on some of the variables that were examined. The larger impact on realism was in the distance each boid wanted to keep from its neighbor, the vision range of each boid defined what was considered a neighborhood. The range on this variable was not of much importance and did not impact what the user perceived as realistic.

O aprendizado de máquina consiste em conceitos e técnicas que permitem aos computadores melhorar seu desempenho com a experiência, ou em outras palavras, aprender com dados. Duas de suas principais categorias são o aprendizado não-supervisionado e o semissupervisionado, que respectivamente consistem em inferir padrões em bases cujos dados não têm rótulo (classe) e classificar dados em bases parcialmente rotuladas. Embora muito estudado, trata-se de um campo repleto de desafios e com muitos tópicos abertos. Sistemas dinâmicos coletivos, por sua vez, são sistemas constituídos por muitos indivíduos, cada qual um sistema dinâmico por si só, de modo que todos eles agem coletivamente, ou seja, a ação de cada indivíduo é influenciada pela ação dos vizinhos. Uma característica notável desses sistemas é que padrões globais podem surgir espontaneamente das interações locais entre os indivíduos, fenômeno conhecido como emergência. Os desafios intrínsecos e a relevância do tema vêm motivando sua pesquisa em diversos ramos da ciência e da engenharia. Este trabalho de doutorado consiste no desenvolvimento e análise de modelos dinâmicos coletivos para o aprendizado de máquina, especificamente suas categorias não-supervisionada e semissupervisionada. As tarefas de segmentação de imagens e de detecção de comunidades em redes, que de certo modo podem ser entendidas como tarefas do aprendizado de máquina, são também abordadas. Em especial, desenvolvem-se modelos nos quais a movimentação dos objetos é determinada pela localização e velocidade de seus vizinhos. O sistema dinâmico assim modelado é então conduzido a um estado cujo padrão formado por seus indivíduos realça padrões subjacentes do conjunto de dados. Devido ao seu caráter auto-organizável, os modelos aqui desenvolvidos são robustos e as informações geradas durante o processo (valores das variáveis do sistema) são ricas e podem, por exemplo, revelar características para realizar soft labeling e determinar classes sobrepostas. / Machine learning consists of concepts and techniques that enable computers to improve their performance with experience, i.e., learn from data. Unsupervised and semi-supervised learning are important categories of machine learning, which respectively consists of inferring patterns in datasets whose data have no label (class) and classifying data in partially-labeled datasets. Although intensively studied, machine learning is still a field full of challenges and with many open topics. Collective dynamical systems, in turn, are systems made of a large group of individuals, each one a dynamical system by itself, such that all of them behave collectively, i.e., the action of each individual is influenced by the action of its neighbors. A remarkable feature of those systems is that global patterns may spontaneously emerge from the local interactions among individuals, a phenomenon known as emergence. Their relevance and intrinsic challenges motivate research in various branches of science and engineering. In this doctorate research, we develop and analyze collective dynamical models for their usage in machine-learning tasks, specifically unsupervised and semi-supervised ones. Image segmentation and network community detection are also addressed, as they are related to machine learning as well. In particular, we propose to work on models in which the objects motion is determined by the location and velocity of their neighbors. By doing so, the dynamical system reaches a configuration in which the patterns developed by the set of individuals highlight underlying patterns of the dataset. Due to their self-organizing nature, it is also expected that the models can be robust and the information generated during the process (values of the system variables) can be rich and reveal, for example, features to perform soft labeling and determine overlapping classes.