Dissertations / Theses on the topic 'Bio-inspired swarms'

In this thesis we propose methods and metrics for human interaction with bio-inspired robot teams. We refine the concept of a stakeholder and demonstrate how a human can use stakeholders to lead a swarm as well as switch the swarm between different collective behaviors. We extend the human interaction metrics of interaction time and interaction effort presented in [1] to swarm systems and introduce the concept of interaction effort. These metrics allow us to understand how well the system performs under human influence. We employ systems theory to estimate these metrics, which is useful because this can be done without performing user studies.

Bio-inspired swarming behaviors provide an effective decentralized way of coordinating robot teams. However, as robot swarms increase in size, bandwidth and time constraints limit the number of agents a human can communicate with and control. To facilitate scalable human interaction with large robot swarms it is desirable to monitor and influence the collective behavior of the entire swarm through limited interactions with a small subset of agents. However, it is also desirable to avoid situations where a small number of agent failures can adversely affect the collective behavior of the swarm. We present a bio-inspired model of swarming that exhibits distinct collective behaviors and affords limited human interaction to estimate and influence these collective behaviors. Using a simple naive Bayes classifier, we show that the global behavior of a swarm can be detected with high accuracy by sampling local information from a small number of agents. We also show that adding a bio-inspired form of quorum sensing to a swarm increases the scalability of human-swarm interactions and also provides an adjustable threshold on the swarm's vulnerability to agent failures.

In this report, we have explored swarm intelligence through a box-pushing taskwith physical robots called e-pucks. Research on social insects has been presentedtogether with dierent ways of controlling autonomous robots, where combiningthis knowledge has been essential in our quest to make a biological plausible antretrieving system.Inspired by ants and behavior-based robotics, we have created the system CRABS.It is based on Brooks' subsumption architecture to control six dierent behaviors,from a xed input-output scheme. The system is designed to easily handle addingor removal of behavior layers. Behavior modules can also be used separately andported to other software or hardware platforms.During this project we came across several hardware and software challenges in-vestigating cooperative behavior. With the use of the simulation tool Webots, wewere able to determine e-pucks' capabilities, and through this knowledge able todesign and construct an articial food source. This operated as the box-item in thebox-pushing task.Based on two types of sensors and two actuators (wheels), we had a strategy toaccomplish the box-pushing task following the biological principles of social insects.The guidelines of the ant retrieving model made CRABS a self-organized systemthat given three or more e-pucks, will always succeed in retrieving the box back tothe wall. The most remarkable view on this accomplishment is that is done throughthe use of only stigmergy and positive/negative feedback.One of the things we've experienced throughout this thesis is that hardware is a morework demanding and inconsistent platform than your usual software simulation.Everything is not given, and although Webots provided helpful shortcuts, a lot oftime and hard work was put down in order to get the system up and running. Withthat being said, we are pleased that we took the hardware rout and were able totest and validate our system on physical robots.

Swarm robotics is the collaboration of a large number of robots to accomplish a set of specified tasks. It has great potential for the generation of self-organizing adaptive systems, where simple behaviours at agent level result in complex behaviours at swarm level. These systems promise to be robust, flexible, and scalable, and have many innovative applications in the future. Elimination of a central controller and instead relying on local awareness and distributed decision making are the main distinguishing characteristics of such, systems which make them different from classical engineering and necessitate a different design methodology. The challenges to design a control mechanism for self-organizing swarm robotic systems mainly come from the difficulty of mapping between the macroscopic behaviours of a swarm and the microscopic behaviours of its individual agents, and also decision making based on local awareness. Nature presents the best examples of self-organising collective systems. They can be divided into two categories, animal collective behaviours, and cellular organs. Although studying animal collective behaviours paves the way for understanding the principles of self-organizing collective systems, cellular organs show more complex behaviours and structures. The goal of this research is to adapt cellular morphogenesis mechanisms for collective behaviours in a swarm of minimalist robots. The trade of between the size of a swarm and the complexity of involved robots necessitates using simpler and cheaper robots. In addition, miniaturization of robots for future micro-robotic applications require to minimize the number of on-board devices in robots. In this thesis, we focus on developing minimalist algorithms inspired by biological morphogenesis for collective swarm behaviours, including collective flocking, target following, and target enclosure. The proposed algorithms are applicable to highly restricted robots without global positioning, directional sensing, motion feedback, and long-range communication devices. At first, I show how morphogens can retain the integrity and original shape of a swarm of robots without directional sensing, while the swarm moves and interacts with the environment. Then, a coordinated motion strategy is presented in order to preserve connectivity of a real swarm of minimalist robots following a target in their environment. Finally, a new approach is presented for target enclosure with a control over the shape of aggregation around the target. In this approach, a morphogen gradient produced by a target reacts with a second one diffusing through the edge of aggregation in order to spot weak points of the aggregation. The last two algorithms implemented in a real swarm of Kilobots.

This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external noise. The angular pattern depends on the number of antenna elements, their geometrical arrangement, and their relative amplitude and phases. In the present work different antenna geometries are tested and compared when their array weights are optimized by different techniques. First, the Genetic Algorithm and Particle Swarm Optimization algorithms are used to find the best set of phases between antenna elements to obtain a desired antenna pattern. This pattern must meet several restraints, for example: Maximizing the power of the main lobe at a desired direction while keeping nulls towards interferers. A series of experiments show that the PSO achieves better and more consistent radiation patterns than the GA in terms of the total area of the antenna pattern. A second set of experiments use the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization algorithms to find the array weights that configure a rectangular array. The results suggest an advantage in performance by reducing the number of iterations taken by the PSO, thus lowering the computational cost. During the development of this thesis, it was found that the initial states and particular parameters of the optimization algorithms affected their overall outcome. The third part of this work deals with the meta-optimization of these parameters to achieve the best results independently from particular initial parameters. Four algorithms were studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal Sampling and Pattern Search performed better to set the initial parameters and obtain the best performance of the bio-inspired methods studied.

Les systèmes autonomes sont récemment devenus une solution efficace pour des applications telles que l'exploration et la surveillance d'environnements. Dans ces situations, l'utilisation de plusieurs robots pourrait améliorer l'efficacité des solutions proposées, bien que cela nécessite des stratégies d'organisation qui soient à la fois robuste, flexible et adaptables à la taille de la flotte de robots. En robotique en essaim, ces qualités sont assurées par la décentralisation, la redondance (plusieurs/tous les robots effectuent la même tâche), des interactions locales et des règles simples. Les interactions et communications locales sont une composante clef de la robotique en essaim. Jusqu'ici, la communication n'a été utilisée que pour des tâches relativement simples, tels que signaler les préférences ou l'état d'un robot. Cependant, la communication peut être bien plus riche et similaire aux langages humains. Dans ces conditions, elle permettrait aux essaims de robots de gérer de nouvelles situations qui ne seraient pas prévues par leurs concepteurs. De riches communications sont donc nécessaires pour obtenir des essaims entièrement autonomes, en particulier dans des environnements inconnus. Dans cette thèse, nous proposons une approche pour faire émerger des communications riches dans des essaims de robots en utilisant les jeux de langages comme protocole de communication et l'agrégation probabiliste comme cas d'étude. L'agrégation probabiliste est un prérequis pour de nombreuses tâches en robotique en essaim mais elle est aussi extrêmement sensible aux conditions expérimentales. Elle requiert donc un réglage spécifique de ses paramètres pour chaque nouvelle condition, y compris les changements d'échelle ou de densité. Avec notre approche, nous avons observé que l'exécution simultanée du jeu de nommage et de l'agrégation mène, dans certaines conditions, à un nouveau comportement d'agglomération en plusieurs groupes, chacun avec son propre nom, qui est contrôlable via les paramètres de l'agrégation. En poussant ces interactions plus loin, nous démontrons que les dynamiques sociales du jeu de nommage peuvent sélectionner des paramètres d'agrégation efficaces. Cette sélection culturelle crée donc des contrôleurs résilients, qui évoluent en-ligne en fonction du contexte courant
Automatically-controlled artificial systems have recently been used in numerous settings including environmental monitoring and explorations, with great success. In such cases, the use of multiple robots could increase efficiency, although we should ensure that their communication and organisation strategies are robust, exible, and scalable. These qualities can be ensured through decentralisation, redundancy (many/all robots perform the same task), local interaction, and simplistic rules, as is the case in swarm robotics. One of the key components of swarm robotics is local interaction or communication. The later has, so far, only been used for relatively simple tasks such as signalling a robot's preference or state. However, communication has more potential because the emergence of meaning, as it exists in human language, could allow robots swarms to tackle novel situations in ways that may not be a priori obvious to the experimenter. This is a necessary feature for having swarms that are fully autonomous, especially in unknown environments. In this thesis, we propose a framework for the emergence of meaningful communications in swarm robotics using language games as a communication protocol and probabilistic aggregation as a case study. Probabilistic aggregation can be a prerequisite to many other swarm behaviours but, unfortunately, it is extremely sensitive to experimental conditions, and thus requires specific parameter tuning for any setting such as population size or density.With our framework, we show that the concurrent execution of the naming game and of probabilistic aggregation leads, in certain conditions, to a new clustering and labelling behaviour that is controllable via the parameters of the aggregation controller. Pushing this interplay forward, we demonstrate that the social dynamics of the naming game can select efficient aggregation parameters through environmental pressure. This creates resilient controllers as the aggregation behaviour is dynamically evolved online according to the current environmental setting

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores
This dissertation presents a set of top-down modulation mechanisms for the modulation of the swarm-based visual saliency computation process proposed by Santana et al. (2010) in context of path detection and tracking. In the original visual saliency computation process, two swarms of agents sensitive to bottom-up conspicuity information interact via pheromone-like signals so as to converge on the most likely location of the path being sought. The behaviours ruling the agents’motion are composed of a set of perception-action rules that embed top-down knowledge about the path’s overall layout. This reduces ambiguity in the face of distractors. However, distractors with a shape similar to the one of the path being sought can still misguide the system. To mitigate this issue, this dissertation proposes the use of a contrast model to modulate the conspicuity computation and the use of an appearance model to modulate the pheromone deployment. Given the heterogeneity of the paths, these models are learnt online. Using in a modulation context and not in a direct image processing, the complexity of these models can be reduced without hampering robustness. The result is a system computationally parsimonious with a work frequency of 20 Hz. Experimental results obtained from a data set encompassing 39 diverse videos show the ability of the proposed model to localise the path in 98.67 % of the 29789 evaluated frames.

La dissémination d’information dans les réseaux VANET est une opération fondamentale pour la sécurité routière. Il est dès lors nécessaire de concevoir et mettre en oeuvre des algorithmes efficaces et adaptatifs pour la dissémination d’informations sélectives et pertinentes.Dans ce travail, des approches Bio-inspirées sont proposées, à partir des comportements auto-organisés des essaims comme les colonies de fourmis et d’abeilles. Ces approches visent à fournir à chaque véhicule des informations en provenance de son environnement et alerter les conducteurs. Dans la première approche, le système de communication direct et indirect des fourmis est utilisé. Les fourmis partagent les informations sur les sources de nourriture avec des membres de la colonie en sécrétant la phéromone sur leurs chemins. La deuxième approche est inspirée par le système de communication des abeilles. Les abeilles partagent les informations à propos des sources de nourriture avec les autres membres de la ruche par des messages spécifiques, selon l’importance de ces sources.Une nouvelle mesure de "pertinence" associée aux messages est définie, par analogie à la sécrétion des phéromones des fourmis et au niveau de l’intensité des messages pour les abeilles, pour disséminer des informations de sécurité dans une zone géographique. Les simulations sont effectuées en utilisant le simulateur NS2 pour mesurer l’efficacité des approches proposées sous différentes conditions, en particulier en termes de densités et vitesses des véhicules
Information dissemination in Vehicular Ad hoc Networks (VANETs) is a fundamental operation to increase the safety awareness among vehicles on roads. Thus, the design and implementation of efficient and scalable algorithms for relevant information dissemination constitutes a major issue that should be tackled.In this work, bio-inspired information dissemination approaches are proposed, that use self-organization principles of swarms such as Ant and Honey Bee colonies. These approaches are targeted to provide each vehicle with the required information about its surrounding and assist drivers to be aware of undesirable road conditions. In the first approach, Ant’s direct and indirect communication systems are used. Ants share information about food findings with colony members by throwing pheromone on the returning to the nest. The second, an RSU-based approach is inspired by the Bee communication system. Bees share profitable food sources with hive-mates in their hive by specific messages.A “relevance” value associated to the emergency messages is defined as an analogue to pheromone throwing in Ant colony, and as an analogue to profitability level in Bee colony, to disseminate safety information within a geographical area. Simulations are conducted using NS2 network simulator and relevant metrics are evaluated under different node speeds and network densities to show the effectiveness of the proposed approaches

Construction is a complex activity that requires the cooperation of multiple workers. Every year, construction activities cause injuries and casualties. To make construction safer, new solutions could be provided by robotics. Robots could be employed not only to replace human workers, but also to make construction in harsh environments safe and cost-effective, paving the way for enhanced underwater infrastructure, deeper underground mining, and planetary colonization. In this thesis, we focus on the topic of collective construction, which involves the cooperation of multiple robots, by presenting a collective robot construction method of our own. Collective construction can be a more viable option than employing individual, complex robots, by potentially allowing the effective realization of large structures, while offering resilience through redundancy, analogous to insect colonies. Our approach offers a novel solution in the design trade-off between choosing the number of robots involved vs. the complexity of the robots involved. On the one hand, capable and complex robots are expensive, limiting the cost effectiveness of realizing large swarms which provide redundancy and increase the system’s resilience to faults. On the other hand, simple and inexpensive robots can be manufactured in large numbers and offer high redundancy, at the cost of limited individual capa bilities and lower performance. We use two types of robots: intelligent scaffolding and worker robots. The intelligent scaffolding acts as regular scaffolding, allowing the worker robots to navigate the structure they assemble, while also guiding and monitoring the construction of the structure. The worker robots move and connect scaffolding and building material while only knowing the local commands necessary to complete their task. This approach is loosely inspired by termite mounds, in which termites use the process of stigmergy in which they mark construction pellets with pheromones to affect the progress of construction, while navigating the struc ture that they build. Thanks to intelligent scaffolding, construction robots have a simple design that allows minimalist onboard computation and communication equipment. In this thesis, we produced a minimum viable prototype demonstrating this concept. Intelligent scaffolding is realized through smart blocks that can be laid and connected to each other. The smart blocks are capable of simple computation and communication once laid. The construction robot uses local navigation methods by line-following across the scaffolding and building blocks of the system. The blocks and construction robot both have a modular design, simplifying the process of manufacturing and repairs while maintaining a low cost. The robot and blocks use magnets to increase the margin of error during block manipulation and allow for the assembly and removal of scaffolding as well as its reuse between build sites. To communicate with the robot, the intelligent scaffolding blocks send local IR signals, similar to TV remote signals, when the robot is on top of them, minimizing the risk of global interference and keeping the system portable. To monitor the connectivity of the system throughout the life cycle of the structure, electrical connections run through each of the blocks, which indicate the status of the structure and can be used to diagnose the location of breaks in the structure for maintenance.

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.

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.

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.

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.

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.

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.

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

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)

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Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico
Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria
A aplica??o tradicional da sele??o de atributos em diversas ?reas como minera??o de dados, aprendizado de m?quina e reconhecimento de padr?es visa melhorar a acur?cia dos modelos constru?dos com a base de dados, ao retirar dados ruidosos, redundantes ou irrelevantes, e diminuir o custo computacional do modelo, ao encontrar um subconjunto representativo dos dados que diminua sua dimensionalidade sem perda de desempenho. Com o desenvolvimento das pesquisas com comit?s de classificadores e a verifica??o de que esse tipo de modelo possui melhor desempenho que os modelos individuais, dado que os classificadores base sejam diversos, surge uma nova aplica??o ?s pesquisas com sele??o de atributos, que ? a de encontrar subconjuntos diversos de atributos para a constru??o dos classificadores base de comit?s de classificadores. O presente trabalho prop?e uma abordagem que maximiza a diversidade de comit?s de classificadores atrav?s da sele??o de subconjuntos de atributos utilizando um modelo independente do algoritmo de aprendizagem e de baixo custo computacional. Isso ? feito utilizando metaheur?sticas bioinspiradas com crit?rios de avalia??o baseados em filtro

abstract: Existing robotic excavation research has been primarily focused on lunar mining missions or simple traffic control in confined tunnels, however little work attempts to bring collective excavation into the realm of human infrastructure. This thesis explores a decentralized approach to excavation processes, where traffic laws are borrowed from swarms of fire ants (Solenopsis invicta) or termites (Coptotermes formosanus) to create decision rules for a swarm of robots working together and organizing effectively to create a desired final excavated pattern. First, a literature review of the behavioral rules of different types of insect colonies and the resulting structural patterns over the course of excavation was conducted. After identifying pertinent excavation laws, three different finite state machines were generated that relate to construction, search and rescue operations, and extraterrestrial exploration. After analyzing these finite state machines, it became apparent that they all shared a common controller. Then, agent-based NetLogo software was used to simulate a swarm of agents that run this controller, and a model for excavating behaviors and patterns was fit to the simulation data. This model predicts the tunnel shapes formed in the simulation as a function of the swarm size and a time delay, called the critical waiting period, in one of the state transitions. Thus, by controlling the individual agents' behavior, it was possible to control the structural outcomes of collective excavation in simulation. To create an experimental testbed that could be used to physically implement the controller, a small foldable robotic platform was developed, and it's capabilities were tested in granular media. In order to characterize the granular media, force experiments were conducted and parameters were measured for resistive forces during an excavation cycle. The final experiment verified the robot's ability to engage in excavation and deposition, and to determine whether or not to begin the critical waiting period. This testbed can be expanded with multiple robots to conduct small-scale experiments on collective excavation, such as further exploring the effects of the critical waiting period on the resulting excavation pattern. In addition, investigating other factors like tuning digging efficiency or deposition proximity could help to transition the proposed bio-inspired swarm excavation controllers to implementation in real-world applications.
Dissertation/Thesis
Masters Thesis Mechanical Engineering 2018

yes
An efficient multi-object evolutionary algorithms are proposed for optimizing frequency characteristics of antennas based on an interfacing created by Matlab environment. This interface makes a link with CST Microwave studio where the electromagnetic investigation of antenna is realized. Very small, compact printed monopole antenna is optimized for ultra- wideband (UWB) applications. Two objective functions are introduced; the first function intends to increase the impedance bandwidth, and second function to tune the antenna to resonate at a particular frequency. The two functions operate in the range of 3.2 to 10.6 GHz and depend on the level of return loss. The computed results provide a set of proper design for UWB system in which the bandwidth achieved is 7.5GHz at the resonance frequency 4.48GHz, including relatively stable gain and radiation patterns across the operating band.

abstract: Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities. To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.
Dissertation/Thesis
Doctoral Dissertation Mechanical Engineering 2017