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1
Agarwal, Swarna [Verfasser], and Heinrich [Akademischer Betreuer] Planck. "Nanoskalig strukturierte Textilfiltermedien für die Trennung von Öl-Wasser-Emulsionen / Swarna Agarwal. Betreuer: Heinrich Planck." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2012. http://d-nb.info/1021923419/34.
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McNabb, Andrew W. "Parallel Particle Swarm Optimization and Large Swarms." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2480.
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Optimization is the search for the maximum or minimum of a given objective function. Particle Swarm Optimization (PSO) is a simple and effective evolutionary algorithm, but it may take hours or days to optimize difficult objective functions which are deceptive or expensive. Deceptive functions may be highly multimodal and multidimensional, and PSO requires extensive exploration to avoid being trapped in local optima. Expensive functions, whose computational complexity may arise from dependence on detailed simulations or large datasets, take a long time to evaluate. For deceptive or expensive objective functions, PSO must be parallelized to use multiprocessor systems and clusters efficiently. This thesis investigates the implications of parallelizing PSO and in particular, the details of parallelization and the effects of large swarms. PSO can be expressed naturally in Google's MapReduce framework to develop a simple and robust parallel implementation that automatically includes communication, load balancing, and fault tolerance. This flexible implementation makes it easy to apply modifications to the algorithm, such as those that improve optimization of difficult objective functions and improve parallel performance. Results show that larger swarms help with both of these goals, but they are most effective if arranged into sparse topologies with lower overhead from communication. Additionally, PSO must be modified to use communication more efficiently in a large sparse swarm for objective functions where information ideally flows quickly through a large swarm. Swarm size is usually fixed at a modest number around 50, but particularly in a parallel computational environment, much larger swarms are much more effective for deceptive objective functions. Likewise, swarms much smaller than 50 are more effective for expensive but less deceptive functions. In general, swarm size should be carefully chosen using all available information about the objective function and computational environment.
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Riyaz, Firasath Maurer Peter M. Marks Robert J. "Evolving a Disjunctive Predator Prey Swarm using PSO Adapting Swarms with Swarms/." Waco, Tex. : Baylor University, 2005. http://hdl.handle.net/2104/1465.
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Ashcraft, C. Chace. "Moderating Influence as a Design Principle for Human-Swarm Interaction." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7406.
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Robot swarms have recently become of interest in both industry and academia for their potential to perform various difficult or dangerous tasks efficiently. As real robot swarms become more of a possibility, many desire swarms to be controlled or directed by a human, which raises questions regarding how that should be done. Part of the challenge of human-swarm interaction is the difficulty of understanding swarm state and how to drive the swarm to produce emergent behaviors. Human input could inhibit desirable swarm behaviors if their input is poor and has sufficient influence over swarm agents, affecting its overall performance. Thus, with too little influence, human input is useless, but with too much, it can be destructive. We suggest that there is some middle level, or interval, of human influence that allows the swarm to take advantage of useful human input while minimizing the effect of destructive input. Further, we propose that human-swarm interaction schemes can be designed to maintain an appropriate level of human influence over the swarm and maintain or improve swarm performance in the presence of both useful and destructive human input. We test this theory by implementing a piece of software to dynamically moderate influence and then testing it with a simulated honey bee colony performing nest site selection, simulated human input, and actual human input via a user study. The results suggest that moderating influence, as suggested, is important for maintaining high performance in the presence of both useful and destructive human input. However, while our software seems to successfully moderate influence with simulated human input, it fails to do so with actual human input.
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Nagavalli, Sasanka. "Algorithms for Timing and Sequencing Behaviors in Robotic Swarms." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1215.
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Robotic swarms are multi-robot systems whose global behavior emerges from local interactions between individual robots and spatially proximal neighboring robots. Each robot can be programmed with several local control laws that can be activated depending on an operator’s choice of global swarm behavior (e.g. flocking, aggregation, formation control, area coverage). In contrast to other multi-robot systems, robotic swarms are inherently scalable since they are robust to addition and removal of members with minimal system reconfiguration. This makes them ideal for applications such as search and rescue, environmental exploration and surveillance. Practical missions often require a combination of swarm behaviors and may have dynamically changing mission goals. However, a robotic swarm is a complex distributed dynamical system, so its state evolution depends on the timing as well as sequence of the supervisory inputs. Thus, it is difficult to predict the effects of an input on the state evolution of the swarm. More specifically, after becoming aware of a change in mission goals, it is unclear at what time a supervisory operator must convey this information to the swarm or which combination of behaviors to use to accomplish the new goals. The main challenges we address in this thesis are characterizing the effects of input timing on swarm performance and using this theory to inform automated composition of swarm behaviors to accomplish updated mission goals. We begin by formalizing the notion of Neglect Benevolence — the idea that delaying the application of an input can sometimes be beneficial to overall swarm performance — and using the developed theory to demonstrate experimentally that humans can learn to approximate optimal input timing. In an adversarial setting, we also demonstrate that by altering only the timing of consensus updates for a subset of the swarm, we can influence the agreement point of the entire swarm. Given a library of swarm behaviors, automated behavior composition consists of identifying a behavior schedule that must specify (1) the appropriate sequence of behaviors and (2) the corresponding duration of execution for each behavior. Applying our notion of Neglect Benevolence, it is clear these two parts are intricately interdependent. By first assuming the durations are known, we present an algorithm to identify the optimal behavior sequence to achieve a desired swarm mission goal when our library contains general swarm behaviors. By restricting our library to consensus-based swarm behaviors, we then relax the assumption on known durations and present an algorithm to simultaneously find the sequence and durations of swarm behaviors to time-optimally accomplish multiple unordered goals.
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Grosh, John Rolfes. "Multi-Human Management of a Hub-Based Colony: Efficiency and Robustness in the Cooperative Best M-of-N Task." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8544.
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Swarm robotics is an emerging field that is expected to provide robust solutions to spatially distributed problems. Human operators will often be required to guide a swarm in the fulfillment of a mission. Occasionally, large tasks may require multiple spatial swarms to cooperate in their completion. We hypothesize that when latency, bandwidth, operator dropout, and communication noise are significant factors, human organizations that promote individual initiative perform more effectively and resiliently than hierarchies in the cooperative best-m-of-n task. Simulations automating the behavior of hub-based swarm robotic agents and groups of human operators are used to evaluate this hypothesis. To make the comparisons between the team and hierarchies meaningful, we explore parameter values determining how simulated human operators behave in teams and hierarchies to optimize the performance of the respective organizations. We show that simulation results generally support the hypothesis with respect to the effect of latency and bandwidth on organizational performance.
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Grandi, Raffaele <1976>. "Coordination and Control of Autonomous Mobile Robots Swarms by using Particle Swarm Optimization Algorithm and Consensus Theory." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5904/1/Grandi_Raffaele_tesi.pdf.
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This thesis presents some different techniques designed to drive a swarm of robots in an a-priori unknown environment in order to move the group from a starting area to a final one avoiding obstacles. The presented techniques are based on two different theories used alone or in combination: Swarm Intelligence (SI) and Graph Theory. Both theories are based on the study of interactions between different entities (also called agents or units) in Multi- Agent Systems (MAS). The first one belongs to the Artificial Intelligence context and the second one to the Distributed Systems context. These theories, each one from its own point of view, exploit the emergent behaviour that comes from the interactive work of the entities, in order to achieve a common goal. The features of flexibility and adaptability of the swarm have been exploited with the aim to overcome and to minimize difficulties and problems that can affect one or more units of the group, having minimal impact to the whole group and to the common main target. Another aim of this work is to show the importance of the information shared between the units of the group, such as the communication topology, because it helps to maintain the environmental information, detected by each single agent, updated among the swarm. Swarm Intelligence has been applied to the presented technique, through the Particle Swarm Optimization algorithm (PSO), taking advantage of its features as a navigation system. The Graph Theory has been applied by
exploiting Consensus and the application of the agreement protocol with the aim to maintain the units in a desired and controlled formation. This approach has been followed in order to conserve the power of PSO and to control part of its random behaviour with a distributed control algorithm
like Consensus.
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Grandi, Raffaele <1976>. "Coordination and Control of Autonomous Mobile Robots Swarms by using Particle Swarm Optimization Algorithm and Consensus Theory." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5904/.
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This thesis presents some different techniques designed to drive a swarm of robots in an a-priori unknown environment in order to move the group from a starting area to a final one avoiding obstacles. The presented techniques are based on two different theories used alone or in combination: Swarm Intelligence (SI) and Graph Theory. Both theories are based on the study of interactions between different entities (also called agents or units) in Multi- Agent Systems (MAS). The first one belongs to the Artificial Intelligence context and the second one to the Distributed Systems context. These theories, each one from its own point of view, exploit the emergent behaviour that comes from the interactive work of the entities, in order to achieve a common goal. The features of flexibility and adaptability of the swarm have been exploited with the aim to overcome and to minimize difficulties and problems that can affect one or more units of the group, having minimal impact to the whole group and to the common main target. Another aim of this work is to show the importance of the information shared between the units of the group, such as the communication topology, because it helps to maintain the environmental information, detected by each single agent, updated among the swarm. Swarm Intelligence has been applied to the presented technique, through the Particle Swarm Optimization algorithm (PSO), taking advantage of its features as a navigation system. The Graph Theory has been applied by
exploiting Consensus and the application of the agreement protocol with the aim to maintain the units in a desired and controlled formation. This approach has been followed in order to conserve the power of PSO and to control part of its random behaviour with a distributed control algorithm
like Consensus.
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Gazi, Veysel. "Stability Analysis of Swarms." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1029812963.
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Abdenebaoui, Larbi [Verfasser], Hans-Jörg [Akademischer Betreuer] [Gutachter] Kreowski, and Jürgen [Gutachter] Pannek. "Graph-Transfromational Swarms : A Graph-Transformational Approach to Swarm Computation / Larbi Abdenebaoui ; Gutachter: Hans-Jörg Kreowski, Jürgen Pannek ; Betreuer: Hans-Jörg Kreowski." Bremen : Staats- und Universitätsbibliothek Bremen, 2016. http://d-nb.info/112770141X/34.
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Swartz, Clinton Keith. "Digital data collection and analysis: what are the effects on students' understanding of chemistry concepts." Montana State University, 2012. http://etd.lib.montana.edu/etd/2012/swartz/SwartzC0812.pdf.
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In this project, digital data collection and analysis methods were implemented to determine their effects on student understanding of chemistry concepts, data analysis and conclusion making skills, and motivation. Teacher attitude and motivation were also determined. The students included in the project were from a 10th grade chemistry class, which included 25 students. Students completed a non-treatment unit in which data collection and analysis were completed without the use of technology. Digital data collection and analysis were then added to experiments and class activities during two treatment units. The digital data collection and analysis tools included data collection interfaces and probes, graphing software and simulations. The non-treatment unit and treatment units were then compared to determine the effectiveness of the intervention. Students understanding of chemistry concepts, data analysis and conclusion making, and motivation increased slightly after the treatment units. Teacher attitude and motivation also showed an increase. This project showed that the use of digital data collection and analysis has positive effects on both the students and the teacher.
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Taranova, D. V. "Swarm robotics." Thesis, Sumy State University, 2016. http://essuir.sumdu.edu.ua/handle/123456789/45874.
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Collective behavior, exhibited by a large number of animals that have the same size and work together toward the one goal is called swarm behavior. This term is applied to insects, but can also be used to any other entity or animal. If we look on it wider, we will see that swarming is a collective motion of self-driven entities.
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Katz, Benji. "Swarm Sounds." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1527780021318426.
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Engzell, Waldén Frithiof. "Chasing Swans." Thesis, KTH, Arkitektur, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261662.
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The last few years have seen an increasingly infected public debate about the appearance of contemporary buildings. Regardless of where one personally stands, this ought to be cause for architects to examine their own understanding of aesthetics. This project is an exercise in articulating and examining questions of architectural form. The project is divided into three parts; Theory, Observation, and Design. The first aims to establish a broad overview of the theoretical discourse surrounding the subject of aesthetics in architecture, as well as formulate a position within it. The second part is an observation of aesthetic experiences in practice, in which three existing works of architecture are broken down into component formal properties, and the experiences these evoke are put into words. The final part is an exercise in articulating the reasoning behind aesthetic design decisions. A small self-imposed design task serves as the catalyst for a sequence of explicitly formulated and documented design choices, leading from inception to finished design.
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Soylu, Umit. "Multi-Target Tracking for Swarm vs. Swarm UAV Systems." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17462.
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Approved for public release; distribution is unlimitedUnmanned systems, including unmanned aerial vehicles (UAVs), are developing technologies that are becoming increasingly important. This thesis provides a model for generating a common operational picture (COP) for unmanned systems that is applicable in todayﾒs technology, and presents results and analysis based on simulation studies. This thesis specifically investigates a swarm versus swarm unmanned systems scenario in which opposing teams of UAVs approach each other. Different methodologies for generating a COP from the perspective of a given team are investigated, and a simulation is designed to explore the performance of the selected strategies for performing multi-target tracking. The results of the simulation show the performance of the presented approach where targets are assumed in the field of view of the tracking agents, false detections may or may not be present, and all entities maneuver according to nondeterministic motion models.
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Bratton, Daniel. "Simple and adaptive particle swarms." Thesis, Goldsmiths College (University of London), 2010. http://research.gold.ac.uk/4752/.
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The substantial advances that have been made to both the theoretical and practical aspects of particle swarm optimization over the past 10 years have taken it far beyond its original intent as a biological swarm simulation. This thesis details and explains these advances in the context of what has been achieved to this point, as well as what has yet to be understood or solidified within the research community. Taking into account the state of the modern field, a standardized PSO algorithm is defined for benchmarking and comparative purposes both within the work, and for the community as a whole. This standard is refined and simplified over several iterations into a form that does away with potentially undesirable properties of the standard algorithm while retaining equivalent or superior performance on the common set of benchmarks. This refinement, referred to as a discrete recombinant swarm (PSODRS) requires only a single user-defined parameter in the positional update equation, and uses minimal additive stochasticity, rather than the multiplicative stochasticity inherent in the standard PSO. After a mathematical analysis of the PSO-DRS algorithm, an adaptive framework is developed and rigorously tested, demonstrating the effects of the tunable particle- and swarm-level parameters. This adaptability shows practical benefit by broadening the range of problems which the PSO-DRS algorithm is wellsuited to optimize.
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Mårtensson, Christopher, and Linus Sjövall. "Consensus Algorithms - Flocking and Swarms." Thesis, KTH, Optimeringslära och systemteori, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105773.
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An interesting eld of mathematics is the study of swarming and ocking. By using graph theory, one can describe a system of agents that transfer information between each other. With the help of certain algorithms it is possible to update the agent's information in order to reach consensus between the agents. If the information relates to the position, the velocity, or the acceleration of each agent, a behaviour similar to that of animals ocks or insect swarms is observed. Several other applications also exist, for example in systems of multiple robots when no central coordination is possible or simply not desired. In this paper dierent algorithms used to change the agent's information state will be studied and researched in order to determine the requirements under which the entire set of agents achieve consensus. First the case where agents receive information from a non-changing set of agents will be studied. Specically a particular algorithm, where each agent's information is determined by a linear function depending on the information state of all other agents from which information is received, will be considered. A requirement for this particular algorithm to reach consensus is that every agent both receives information and also sends information to every other agent, directly or indirectly through other agents. If all information transfers are weighed equally, the consensus achieved will be the average of all initial information states. Consensus can also be reached under looser conditions where there exists an agent that sends information to every other agent, directly or indirectly. The changes of the system's behaviour when one uses dierent consensus algorithms will be discussed, and computer simulations of these will be provided. An interesting case is where the information (often referring to location, velocity or acceleration) is received only from agents within a given distance and thus the information is received from dierent agents at dierent times. This results in nonlinear algorithms and mostly simulations and interpretations will be given. An observation is that whether consensus is achieved or not depends partially on the initial information states of the agents and the maximum distance for information transfer.Ett intressant omrade inom matematiken ar att beskriva fenomenet med ockar och svarmar. Med hjalp av grafteori kan man beskriva ett system av agenter som skickar information mellan varandra och med hjalp av algoritmer som beskriver hur varje agents informationen ska uppdateras sa att konsensus nas. Om informationen beskriver en position eller foryttning i rummet kan man observera ett beteende som liknar det hos djurockar eller insektssv armar. Manga andra tillampningsomraden nns ocksa, till exempel i system av robotar nar det saknas central styrning och internt beslutstagande ar onskvart. I denna rapport kommer olika algoritmer for att uppdatera en agents status att undersokas for att bestamma vilka krav som nns for att konsensus skall nas. Forsta delen kommer att behandla ett enklare fall dar varje agent tar emot information fran en oforanderlig uppsattning agenter. Specikt sa kommer en algoritm, dar en agents status bestams av en linjar funktion som beror pa statusen hos de agenter fran vilka information mottages, att studeras. Ett krav for att denna algoritm ska na konsensus ar att varje agent bade skickar och tar emot information fran samtliga ovriga agenter, direkt eller indirekt via andra agenter. Om alla informations overforingar vags lika sa kommer alla agenter na medelvardet av agenternas initialvarden. Konsensus kan ocksa nas under mindre restriktiva villkor, om det nns en agent som skickar information till alla andra noder (direkt eller indirekt). Forandringar av systemets beteende vid olika uppdateringsalgoritmer kommer att studeras och datorsimuleringar av dessa fenomen kommer att ges. Ett intressant fall ar da informationen (ofta position, hastighet eller acceleration) endast kan tas emot fran de agenter som nns inom ett givet avstand. darmed forandras den uppsattning agenter med vilka information overfors med tiden. Detta resulterar i olinjara algoritmer och framforallt kommer simulationer och tolkningar av dessa att ges. En observation ar att om konsensus nas eller inte beror starkt pa densiteten bland agenterna i utgangslaget samt det maximala avstand vid vilket informationsoverforing kan ske.
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Henderson, Robert. "Swarms: Spatiotemporal grouping across domains." Springer, 2016. http://hdl.handle.net/10150/622353.
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First Online: 21 March 2016. 12 month embargo.This paper presents cross-domain evidence that natural language makes use of (at least) two ways of individuating collective entities that differ in terms of how they cohere. The first kind, which I call swarm reference, picks out higher-order collective entities defined in terms of the spatial and temporal configuration of their constituent individuals. The second, which corresponds to canonical cases of group reference (e.g. committee, team, etc.), makes use of non-spatiotemporal notions. To motivate this distinction, I present systematic differences in how these two types of collective reference behave linguistically, both in the individual and event domains. These differences support two primary results. First, they are used as tests to isolate a new class of collective nouns that denote swarm individuals, both in English, as well as other languages like Romanian. I then consider a crosslinguistically common type of pluractionality, called event-internal in the previous literature (Cusic 1981, Wood 2007), and show that its properties are best explained if the relevant verbs denote swarm events. By reducing event-internal pluractionality to a type of collective reference also available for nouns, this work generates a new strong argument that pluractionality involves the same varieties of plural reference in the event domain that are seen in the individual domain.
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Pitonakova, Lenka. "Design patterns for robot swarms." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/410360/.
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Demand for autonomous multi-robot systems, where robots can cooperate with each other without human intervention, is set to grow rapidly in the next decade. Today, technologies such as self-driving cars and fleets of robotic assistants in hospitals and warehouses are being developed and used. In the future, robot swarms could be deployed in retrieval, reconnaissance and construction missions. Distributed collective systems have desirable properties, such as low cost of individual robots, robustness, fault tolerance and scalability. One of the main challenges in swarm robotics is that `bottom-up' approach to behaviour design is required. While the swarm performance is specied on the collective level of the swarm, robot designers need to program control algorithms of individual robots, while taking into account complex robot-robot interactions that allow emergence of collective intelligence. In order to be able to develop such systems, we need a methodology that aligns bottom-up design decisions with top-down design specifications. In this thesis, a novel approach to understanding and designing robot swarms that perform foraging and task allocation is proposed. Based on thousands of different simulation experiments, the Information-Cost-Reward framework is formulated, that relates the way in which a swarm obtains and uses information, to its ability to use that information in order to obtain reward efficiently. Secondly, based on the information-based understanding of swarm performance, design patterns for robot swarms are formalised. The design patterns are modular aspects of robot behaviour that dene when and how information should be obtained, exchanged or updated by robots, given particular swarm mission characteristics. Multiple design patterns can be unambiguously combined together in order to create a suitable robot control strategy. The design patterns specify robot behaviour in a newly developed Behaviour-Data Relations Modeling Language, where relationships between robot behaviour and data stored in and outside of robots are explicitly defined. This allows the design patterns to define behaviour of robots that cooperate and share information.
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Jung, Shin-Young. "Shaping Swarms Through Coordinated Mediation." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/5516.
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A swarm is a group of uninformed individuals that exhibit collective behaviors. Without any information about the external world, a swarm has limited ability to achieve complex goals. Prior work on human-swarm interaction methods allow a human to influence these uninformed individuals through either leadership or predation as informed agents that directly interact with humans. These methods of influence have two main limitations: (1) although leaders sustain influence over nominal agents for a long period of time, they tend to cause all collective structures to turn in to flocks (negating the benefit of other swarm formations) and (2) predators tend to cause collective structures to fragment. In this thesis, we present the use of mediators as a novel form for human-swarm influence and use mediators to shape the perimeter of a swarm. The mediator method uses special agents that operate from within the spatial center of a swarm. This approach allows a human operator to coordinate multiple mediators to modulate a rotating torus into various shapes while sustaining influence over the swarm, avoiding fragmentation, and maintaining the swarm's connectivity. The use of mediators allows a human to mold and adapt the torus' behavior and structure to a wide range of spatio-temporal tasks such as military protection and decontamination tasks. Results from an experiment that compares previous forms of human influence with mediator-based control indicate that mediator-based control is more amenable to human influence for certain types of problems.
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Diukman, Anner Gaby. "Swarm Observations Implementing Integration Theory to Understand an Opponent Swarm." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17358.
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Approved for public release; distribution is unlimited.Swarm counter measure systems currently use enhanced weapons and sensor capabilities to address the threat of opponent swarms. However, there is a gap in current defense capabilities to counter swarm attacks, because brute force, or the enhancement of current defense systems by adding to defense capabilities are inadequate because of the inherent robustness, flexibility and adaptation of swarm attacks. Because of this, an overarching model is sought to understand the underlying command and control mechanism of an observed swarm threat, so that mechanisms that determine swarm behaviors can be understood. This will enable the development of countermeasures to counter swarms using specialized systems or tactics for certain behavior types. Integration theory provides an abstract model adequate throughout disparate swarm intelligence-domains (such as biology, computer algorithms, physics, and sociology). Integration theory, used with agent based modeling and analytical methods such as fractal dimensions, entropy, correlation and spatiotemporal structures, shows that it is possible to differentiate among the underlying C2 mechanisms by observing the swarm movement patterns. Adopting a swarm analytical observation approach is advised to promote the implementation of effective future countermeasures.
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Ekberg, Pontus. "Swarm-Intelligent Localization." Thesis, Uppsala University, Department of Information Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-108042.
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In wireless sensor networks we often want to know where individual sensor nodesare physically positioned in order to make sense of the data that they report. Theprocess of obtaining such position information for the nodes is known as localization.Simple solutions to the problem of localization are to either place the nodes manuallyat specified places, or to use some special localization hardware such as GPSreceivers. However, these solutions can be impractical or too costly, especially forlarge networks. Instead we can use some algorithm to try to compute the nodes'positions based on available data. We present a new distributed algorithm, which wecall Swarm-Intelligent Localization (SIL), for computing these positions. Our algorithmassumes that a fraction of the nodes, the so-called anchors, have an a prioriknowledge of their positions, and that noisy range measurements can be madebetween neighbouring nodes in the network. The average computational complexityper node running SIL is constant in the network size, and linear in the connectivity ofthe network. We evaluate the algorithm through simulations of different networktopologies with varying parameters, such as network size, range measurement errors,fraction of anchors and connectivity. The results of the simulations indicate that inmost cases SIL can successfully locate the majority of sensor nodes with reasonableaccuracy, even in the face of difficulties such as large distance measurement errors.
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Devarakonda, SaiPrasanth. "Particle Swarm Optimization." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1335827032.
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Swain, Jo Elyn Christiansen. "The influence of relational trust between the superintendent and union president." Diss., Montana State University, 2007. http://etd.lib.montana.edu/etd/2007/swain/SwainJ1207.pdf.
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Shang, Beining. "Hardware variation in robotic swarm and behavioural sorting with swarm chromatography." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/417270/.
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Social insects can achieve remarkable outcomes, various examples can be found in ants, bees, etc. Inspired by social insects, swarm robotic research considers coordinating a group of relatively simple and autonomous robots to finish tasks collaboratively based on direct or indirect interactions. Such systems can offer advantages of robustness, flexibility and scalability, just like social insects. For many years, various researchers have endeavoured to design intelligent artificial swarms and many hardware-based swarm robots have been implemented. One assumption that made by a majority of swarm robotic researchers, particularly in software simulation is that a robotic swarm is a group of identical robots, there is no difference between any two of them. However, differences among hardware robots are unavoidable, which exist in robotic sensors, actuators, etc. These hardware differences, albeit small, can affect the robots’ response to the environment. Moreover, hardware differences can provoke robots’ heterogeneity which then profoundly influence swarm performance due to the non-linearity in the controller and uncertainty in the environment. Nevertheless, questions about how hardware differences influence swarm performance and how to make use of them remain a research challenge. In this work, the issue of hardware variation in swarm robots is investigated. Specifically swarm robots with hardware variations are modelled and simulated in a line following scenario. It is found that even small hardware variations can result in behavioural heterogeneity. Although the variations can be compensated by the software controller in training, the hardware variations and resulting differences in training are amplified in the interactions between the robot and the environment. To know how exactly hardware variation influence robotic behaviours, a novel approach, inspired by the chromatography method in chemistry, is proposed to sort swarm robots according to their hardware circumstances. This method is based on a large number of interactions between robots and the environment. Individual robot’s unique hardware circumstance determines its unique decision making and reaction during each robotic controlling step, and these unique microscopic reactions accumulate and contribute to the robot’s macroscopic behaviour. The behavioural sorting results show that the behaviour of an individual robot is not determined by a single parameter but by the combination of multiple hardware factors. Different combinations of hardware parameters can help robots achieve similar behaviours. The efficiency of the behavioural sorting method is investigated, particularly the influence of the robot’s controller and environmental factor. By simulating various combinations of robots with different integration lengths of the controller and arenas with different pattern densities, it is discovered that if the robots’ ability to memorise previous events is coupled with the density of the sorting arena, better sorting results can be achieved. This work is regarded as an initial investigation into the issue of unavoidable hardware differences between swarm robots. Given the research outcome and that real swarms will necessarily show hardware variations, it is therefore necessary to contemplate current swarm algorithms in the context of diverse robot populations. In addition, a new research field of swarm chromatography for sorting robotic behaviours to improve swarm efficiency is initiated.
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Scheepers, Christiaan. "Multi-guided particle swarm optimization : a multi-objective particle swarm optimizer." Thesis, University of Pretoria, 2017. http://hdl.handle.net/2263/64041.
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An exploratory analysis in low-dimensional objective space of the vector evaluated particle swarm optimization (VEPSO) algorithm is presented. A novel visualization technique is presented and applied to perform the exploratory analysis. The exploratory analysis together with a quantitative analysis revealed that the VEPSO algorithm continues to explore without exploiting the well-performing areas of the search space. A detailed investigation into the influence that the choice of archive implementation has on the performance of the VEPSO algorithm is presented. Both the Pareto-optimal front (POF) solution diversity and convergence towards the true POF is considered during the investigation. Attainment surfaces are investigated for their suitability in efficiently comparing two multi-objective optimization (MOO) algorithms. A new measure to objectively compare algorithms in multi-dimensional objective space, based on attainment surfaces, is presented. This measure, referred to as the porcupine measure, adapts the attainment surface measure by using a statistical test along with weighted intersection lines. Loosely based on the VEPSO algorithm, the multi-guided particle swarm optimization (MGPSO) algorithm is presented and evaluated. The results indicate that the MGPSO algorithm overcomes the weaknesses of the VEPSO algorithm and also outperforms a number of state of the art MOO algorithms on at least two benchmark test sets.Thesis (PhD)--University of Pretoria, 2017.
Computer Science
PhD
Unrestricted
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Semeghini, Michele. "Swarm constructability: designing through embedded tectonic behaviors in swarm robotic systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8505/.
Full textAbstract:
Con questa tesi di laurea si muovono i primi passi di una ricerca applicata finalizzata alla costruzione-deposizione di materiale da parte di sciami di mini-robot dal comportamento indipendente che si coordinano tramite segnali lasciati e rilevati nell’ambiente in cui si muovono.
Lo sviluppo di tecniche di progettazione e fabbricazione digitale ha prodotto un aumento nel grado di interconnessione tra tecnologia e design, dunque, di nuove possibilità tettoniche. Le relazioni tettoniche tradizionali stanno infatti subendo una trasformazione radicale, potendo essere esplicitamente informate e dunque mediate attraverso gli strumenti digitali dall’ideazione alla produzione. Questa mediazione informata del contenuto tettonico (che opera costantemente) è distintivo di un approccio material-based alla progettazione che aumenta l’integrazione tra struttura, materia e forma entro le tecnologie di fabbricazione (R.Oxman).
Dei numerosi processi di fabbricazione per l’architettura che si servono di tecnologia robotica, pochi sono capaci di superare la logica gerarchica, rigida e lineare-sequenziale che serve di fatto agli obiettivi di automazione ed ottimizzazione. La distribuzione di forme di intelligenza semplificata ad un numero elevato di unità robot è quindi qui proposta come alternativa al modello appena descritto. Incorporando semplici decisioni di carattere architettonico negli agenti-robot che costituiscono il sistema distribuito di entità autonome, la loro interazione e le decisioni prese individualmente producono comportamento collettivo e l’integrazione delle suddette relazioni tettoniche.
Nello sviluppo del progetto, si è fatto così riferimento a modelli comportamentali collettivi (di sciame) osservabili in specie comunitarie che organizzano strutture materiali -come termiti e vespe- ed in organismi semplici -come le muffe cellulari della specie Physarum polycephalum.
Per queste specie biologiche il processo di costruzione non dipende da un ‘piano generale’ ma è guidato esclusivamente da azioni dei singoli individui che comunicano lasciando tracce chimiche nell’ambiente e modificano il loro comportamento rilevando le tracce lasciate dagli altri individui.
A questo scopo, oltre alle simulazioni in digitale, è stato indispensabile sviluppare dei prototipi funzionali di tipo fisico, ovvero la realizzazione di mini-robot dal movimento indipendente, in grado di coordinarsi tra loro tramite segnali lasciati nell’ambiente e capaci di depositare materiale.
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Pendleton, Brian O. "Human-Swarm Interaction: Effects on Operator Workload, Scale, and Swarm Topology." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3999.
Full textAbstract:
Robots, including UAVs, have found increasing use in helping humans with dangerous and difficult tasks. The number of robots in use is increasing and is likely to continue increasing in the future. As the number of robots increases, human operators will need to coordinate and control the actions of large teams of robots. While multi-robot supervisory control has been widely studied, it requires that an operator divide his or her attention between robots. Consequently, the use of multi-robot supervisory control is limited by the number of robots that a human or team of humans can reasonably control. Swarm robotics -- large numbers of low-cost robots displaying collective behaviors -- offers an alternative approach by providing the operator with a small set of inputs and parameters that alter the behavior of a large number of autonomous or semi-autonomous robots. Researchers have asserted that this approach is more scalable and offers greater promise for managing huge numbers of robots. The emerging field of Human-Swarm Interaction (HSI) deals with the effective management of swarms by human operators. In this thesis we offer foundational work on the effect of HSI (a) on the individual robots, (b) on the group as a whole, and (c) on the workload of the human operator. We (1) show that existing general swarm algorithms are feasible on existing robots and can display collective behaviors as shown in simulations in the literature, (2) analyze the effect of interaction style and neighborhood type on the swarm's topology, (3) demonstrate that operator workload stays stable as the size of the swarm increases, but (4) find that operator workload is influenced by the interaction style. We also present considerations for swarm deployment on real robots.
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Ilaya, Omar, and o. ilaya@student rmit edu au. "Cooperative Control for Multi-Vehicle Swarms." RMIT University. Aerospace, Mechanical & Manufacturing Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091027.112852.
Full textAbstract:
The cooperative control of large-scale multi-agent systems has gained a significant interest in recent years from the robotics and control communities for multi-vehicle control. One motivator for the growing interest is the application of spatially and temporally distributed multiple unmanned aerial vehicle (UAV) systems for distributed sensing and collaborative operations. In this research, the multi-vehicle control problem is addressed using a decentralised control system. The work aims to provide a decentralised control framework that synthesises the self-organised and coordinated behaviour of natural swarming systems into cooperative UAV systems. The control system design framework is generalised for application into various other multi-agent systems including cellular robotics, ad-hoc communication networks, and modular smart-structures. The approach involves identifying suitable relationships that describe the behaviour of the UAVs within the swarm and the interactions of these behaviours to produce purposeful high-level actions for system operators. A major focus concerning the research involves the development of suitable analytical tools that decomposes the general swarm behaviours to the local vehicle level. The control problem is approached using two-levels of abstraction; the supervisory level, and the local vehicle level. Geometric control techniques based on differential geometry are used at the supervisory level to reduce the control problem to a small set of permutation and size invariant abstract descriptors. The abstract descriptors provide an open-loop optimal state and control trajectory for the collective swarm and are used to describe the intentions of the vehicles. Decentralised optimal control is implemented at the local vehicle level to synthesise self-organised and cooperative behaviour. A deliberative control scheme is implemented at the local vehicle level that demonstrates autonomous, cooperative and optimal behaviour whilst the preserv ing precision and reliability at the local vehicle level.
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Moritz, Ruby Louisa Viktoria. "Cooperation in self-organized heterogeneous swarms." Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-161633.
Full textAbstract:
Cooperation in self-organized heterogeneous swarms is a phenomenon from nature with many applications in autonomous robots. I specifically analyzed the problem of auto-regulated team formation in multi-agent systems and several strategies to learn socially how to make multi-objective decisions. To this end I proposed new multi-objective ranking relations and analyzed their properties theoretically and within multi-objective metaheuristics. The results showed that simple decision mechanism suffice to build effective teams of heterogeneous agents and that diversity in groups is not a problem but can increase the efficiency of multi-agent systems.
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Othman, Wan Amir Fuad Wajdi. "Formation and organisation in robot swarms." Thesis, Sheffield Hallam University, 2009. http://shura.shu.ac.uk/20156/.
Full textAbstract:
A swarm is defined as a large and independent collection of heterogeneous or homogeneous agents operating in a common environment and seemingly acting in a coherent and coordinated manner. Swarm architectures promote decentralisation and self-organisation which often leads to emergent behaviour. The emergent behaviour of the swarm results from the interactions of the swarm with its environment (or fellow agents), but not as a direct result of design. The creation of artificially simulated swarms or practical robot swarms has become an interesting topic of research in the last decade. Even though many studies have been undertaken using a practical approach to swarm construction, there are still many problems need to be addressed. Such problems include the problem of how to control very simple agents to form patterns; the problem of how an attractor will affect flocking behaviour; and the problem of bridging formation of multiple agents in connecting multiple locations. The central goal of this thesis is to develop early novel theories and algorithms to support swarm robots in. pattern formation tasks. To achieve this, appropriate tools for understanding how to model, design and control individual units have to be developed. This thesis consists of three independent pieces of research work that address the problem of pattern formation of robot swarms in both a centralised and a decentralised way. The first research contribution proposes algorithms of line formation and cluster formation in a decentralised way for relatively simple homogenous agents with very little memory, limited sensing capabilities and processing power. This research utilises the Finite State Machine approach. In the second research contribution, by extending Wilensky's (1999) work on flocking, three different movement models are modelled by changing the maximum viewing angle each agent possesses during the course of changing its direction. An object which releases an artificial potential field is then introduced in the centre of the arena and the behaviours of the collective movement model are studied. The third research contribution studies the complex formation of agents in a task that requires a formation of agents between two locations. This novel research proposes the use of L-Systems that are evolved using genetic algorithms so that more complex pattern formations can be represented and achieved. Agents will need to have the ability to interpret short strings of rules that form the basic DNA of the formation.
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Liu, Yang. "Stability analysis of asynchronous foraging swarms /." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486402544588482.
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Pimenta, Luciano Cunha de Araujo. "Techniques for Controlling Swarms of Robots." Universidade Federal de Minas Gerais, 2009. http://hdl.handle.net/1843/GASP-7Y5F4W.
Full textAbstract:
This thesis addresses the problem of controlling very large groups of robots, refereed as swarms. Scalable solutions in which there is no need for labelling the robots are proposed. All the robots run the same software and the success of the task execution does not depend on specific members of the group. Robustness to dynamic addition and deletion of agents is also an advantage of our approaches. In the first methodology, we model the swarm as a fluid immersed in a region where a field of external forces, which is free of local minima, is defined. In this case, the Smoothed Particle Hydrodynamics (SPH) method is applied to model the robotic fluid', more specifically, to model the interactions among the robots of the group. The Finite Element Method (FEM) is also used in this work to compute the fields that determine external forces. This approach is instantiated in a pattern generation task and also in a coverage task. In the second methodology, a problem of optimal environment coverage using robots equipped with sensors is addressed by means of tools from the Locational Optimization theory. Three important extensions of well-known results in the literature are presented: (i) sensors with different footprints, (ii) disk-shaped robots, and (iii) nonconvex polygonal environments. Both approaches are verified in simulations. The first technique is also implemented and tested in actual robots.Esta tese aborda o problema de controle de grandes grupos de robôs, referidos como enxames. São propostas soluções escaláveis as quais não necessitam da identificação única dos robôs. Todos os robôs executam o mesmo código e o sucesso na execução de uma tarefa não depende de membros específicos do grupo. Robustez à adição e remoção dinâmica de agentes também é uma vantagem das abordagens propostas. Na primeira metodologia, o enxame é modelado como um fluido imerso numa região onde um campo de forças externas livre de mínimos locais é definido. Neste caso, utiliza-se o método de Hidrodinâmica de Partículas Suavizadas (HPS) para modelar o fluido robótico'', mais especificamente, para modelar as interações entre robôs do grupo. O Método de Elementos Finitos (MEF) também é utilizado neste trabalho para calcular os campos vetoriais que determinam as forças externas. Esta abordagem é instanciada num problema de geração de padrões e também num problema de cobertura de ambientes. Na segunda metodologia, um problema de cobertura ótima de ambientes utilizando robôs equipados com sensores é tratado por meio de ferramentas provenientes da teoria de Otimização Locacional. São apresentadas três extensões importantes de resultados já conhecidos na literatura: (i) sensores com diferentes campos de visão, (ii) robôs com formato circular e (iii) ambientes poligonais não-convexos. Ambas metodologias são verificadas em simulações. A primeira metodologia é também implementada e testada em robôs reais.
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Adams, Joshua S. "Transmitter Localization Using Autonomous Robotic Swarms." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/632.
Full textAbstract:
The purpose of this research is to design a proof of concept system that is capable of locating a hidden radio transmitter and to investigate methods of multi-agent formation control with a specific interest in the effectiveness of these methods on the overall objective of locating this transmitter. A system is proposed and developed in which autonomous agents work together to locate this transmitter and their responsiveness is analyzed while using formations based both on a behavioral system and a system derived from centroidal Voronoi tessellations. Many software adaptations to the existing MASnet program are required, as well as some hardware adaptations, including development of a robust simulation platform that may be used in conjunction with the MASnet system, and exploration of a distributed formation system. While this work does not accomplish the overall goal of the MASnet platform -- to be able to locate and control a diffusion process -- it does further understanding of the way autonomous agents interact with their environment and develop tools that aid future research in the program, as well as introduce exciting new areas to which the platform can be applied.
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Swan, Laura Elizabeth. "Role of the mGRIP1 homologue DGrip in the Drosophila neuromuscular system." [S.l.] : [s.n.], 2005. http://webdoc.sub.gwdg.de/diss/2005/swan.
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Al-kazemi, Buthainah Sabeeh No'man. "Multiphase particle swarm optimization." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2002. http://wwwlib.umi.com/cr/syr/main.
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Garattoni, Lorenzo. "Cognitive Abilities in Swarm Robotics: Developing a swarm that can collectively sequence tasks." Doctoral thesis, Universite Libre de Bruxelles, 2021. https://dipot.ulb.ac.be/dspace/bitstream/2013/317235/5/contratLG.pdf.
Full textAbstract:
Can robots of a swarm cooperate to solve together a complex cognitive problem that none of them can solve alone? TS-Swarm is a robot swarm that autonomously sequences tasks at run time and can therefore operate even if the correct order of execution is unknown at design time. The ability to sequence tasks endows robot swarms with unprecedented autonomy and is an important step towards the uptake of swarm robotics in a range of practical applications.Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
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Jayabalan, Adhavan. "Decentralized Persistent Connectivity Deployment in Robot Swarms." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1231.
Full textAbstract:
Robot swarms are often considered suitable for tasks that are large-scale and long-term. Large-scale missions force the robots to spread spatially. In these type of tasks, actively maintaining connectivity allows the swarm to coordinate. Similarly, long-term nature of the task requires robots to work for a long time. This is affected by the limited energy level of the robot. However current studies normally focus only on connectivity or energy awareness. Therefore, in this work, we propose an approach to tackle the problem of maintaining global connectivity (swarm-level property) considering finite battery life (individual property). We are specifically focusing on growing the communication network and keeping it alive for a long period. We construct a logical tree over the connectivity graph. The logical tree is constructed by using a subset of robots from the swarm. The tree is grown by adding robots as necessary. The tree is also periodically reconfigured to cope with dynamic robot motion. This enables the swarm to grow the tree efficiently. In addition, robots exchange their roles based on their available energy levels. This allows robots with low energy levels to navigate to dedicated charging stations for recharging thus allowing the swarm to maintain the communication network. We evaluate our approach in a wide set of experiments with a realistic robot simulator named ARGoS.
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Dickie, Alistair James. "Modeling robot swarms using agent-based simulation." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02Jun%5FDickie.pdf.
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Chen, Jianing. "Cooperation in swarms of robots without communication." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8319/.
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Swarm robotics aims to use a large group of relatively simple robots to solve tasks that can hardly be achieved by a single robot in the group. Compared to single robot systems with increased capability, a swarm robotic system may have advantages in robustness, flexibility and scalability. However, designing cooperative behaviors for a swarm robotic system is a challenging problem, especially when the robots may not have communication capabilities and thus only know local information. For a swarm of miniature mobile robots that cannot communicate explicitly, this thesis studies fully decentralized solutions of two problems. For the problem of cooperative transport, the thesis presents a strategy to push an object that is large enough to occlude the robots' perception of the goal of the transportation. For the problem of pattern formation, the thesis investigates algorithms based on the Brazil nut effect that can organize the swarm of robots into an annular formation. These problems are studied using physics-based computer simulations as well as experimental implementations based on the e-puck robotic platform. The simplicity of the solutions make them suitable for applications that require the individual robots to be as simple as possible. Example application scenarios could be micro robot swarms working in the human body.
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Lang, Andreas. "Face Detection using Swarm Intelligence." Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-64415.
Full textAbstract:
Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group
of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science,
particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying
structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J.
Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes
the ability to solve complex problems.
The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm
intelligence. The process developed for this purpose consists of a combination of various known structures, which are then
adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example
application program.
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Albin, Aaron Thomas. "Musical swarm robot simulation strategies." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42862.
Full textAbstract:
Swarm robotics for music is a relatively new way to explore algorithmic composition as well as new modes of human robot interaction. This work outlines a strategy for making music with a robotic swarm constrained by acoustic sound, rhythmic music using sequencers, motion causing changes in the music, and finally human and swarm interaction. Two novel simulation programs are created in this thesis: the first is a multi-agent simulation designed to explore suitable parameters for motion to music mappings as well as parameters for real time interaction. The second is a boid-based robotic swarm simulation that adheres to the constraints established, using derived parameters from the multi-agent simulation: orientation, number of neighbors, and speed. In addition, five interaction modes are created that vary along an axis of direct and indirect forms of human control over the swarm motion. The mappings and interaction modes of the swarm robot simulation are evaluated in a user study involving music technology students. The purpose of the study is to determine the legibility of the motion to musical mappings and evaluate user preferences for the mappings and modes of interaction in problem solving and in open-ended contexts. The findings suggest that typical users of a swarm robot system do not necessarily prefer more inherently legible mappings in open-ended contexts. Users prefer direct and intermediate modes of interaction in problem solving scenarios, but favor intermediate modes of interaction in open-ended ones. The results from this study will be used in the design and development of a new swarm robotic system for music that can be used in both contexts.
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Keshtkar, Abolfazl. "Swarm intelligence-based image segmentation." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27525.
Full textAbstract:
One of the major difficulties met in image segmentation lies in the varying degrees of homogeneousness of the different regions in a given image. Hence, it is more efficient to adopt adaptive threshold type methodologies to identify the regions in the images. Throughout the last decade, many image processing tools and techniques have emerged based on the former technology which we called conventional and new technologies such as intelligent-based image processing techniques and algorithm. In some cases, a combination of both technologies is adapted to form a hybrid image processing technique.
Intelligent-based techniques are increasing nowadays. Due to the rapid growth of agent-based technology's environments which are adopting numerous agent-based applications, tools, models and softwares to enhance and improve the quality of the agent based approach. In case of intelligent techniques to doing image processing; swarm intelligence techniques rarely have been used in term of image segmentation or boundary detection. However, there are many factors that make this task challenging. These factors include not only the limited such increasing number of agents in the environment, and the presence of techniques., but also how to efficiently find the right threshold in the image, develop a flexible design, and fully autonomous system that support different platform. A flexible architecture and tools need to be defined that overcomes these problems and permits a smooth and valuable image processing based on these new techniques in image processing. It would satisfy the needs of end users. This thesis illustrates the theoretical background, design, swarm based intelligent techniques and implementation of a fully agent-based model system that is called SIBIS (Swarm Intelligent Based Image Segmentation).
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Corbett, Ian Rudy Paul. ""SWANG!" for symphonic wind orchestra." Diss., UMK access, 2005.
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Thesis (D.M.A.)--Conservatory of Music. University of Missouri--Kansas City, 2005."A dissertation in music composition." Advisor: Paul Rudy. Typescript. Vita. Title from "catalog record" of the print edition Description based on contents viewed Nov. 12, 2007 Online version of the print edition.
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Stoops, David. "Rule discovery from swarm systems." Thesis, University of Ulster, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.550967.
Full textAbstract:
Rules determine how a system operates within the boundaries of its given environment. This principle is present within both natural and man-made systems alike. The term "man-made system" refers to systems which have been developed, implementing a specifically designed rule-set for its functionality. Whereas "natural systems" are those rules presented and seen within various species or environments within the natural world. The research presented within this thesis looks at the methods and results of discovering rules from swarm systems. The swarm is a phenomenon seen within the natural world where same species individuals group together to accomplish a task using their behaviours. This has many aspects of difficulty to it, various species employ differing rules, and a species can employ individuals with differing rule-sets. The primary swarm to be investigated is the Bird flock, with a developed simulation being used as a test-bed. The BOlD simulation presents a computer program incorporating a visual representation of the birds, each employing 3 basic rules. The challenge for this work is to develop a method of providing a generic rule discovery system for swarms. This encompasses the ability to collect and manipulate data, and discover the rules present within differing species and roles within swarms. An additional objective is to present a novel method for verifying or validating the rules, employing performance testing. This type of testing provides much insight into the validity of the rules by determining how well they perform when tested against the original rule-set. The data collected from the BOlD simulation was transformed into the selected attributes for mining, with a rule algorithm being used to discover rules. The rules which were discovered provided an insight into the behaviours of the swarm, with the performance testing proving that they were not as effective or efficient as the original rules employed within the simulation. The presented research provides the ability to gather a further insight into swarm behaviours, and presents a varying method for understanding the interactions we view every day within these swarms.
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Sun, Yanxia. "Improved particle swarm optimisation algorithms." Thesis, Paris Est, 2011. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000395.
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D. Tech. Electrical Engineering.Particle Swarm Optimisation (PSO) is based on a metaphor of social interaction such as birds flocking or fish schooling to search a space by adjusting the trajectories of individual vectors, called "particles" conceptualized as moving points in a multidimensional space. This thesis presents several algorithms/techniques to improve the PSO's global search ability. Simulation and analytical results confirm the efficiency of the proposed algorithms/techniques when compared to the other state of the art algorithms.
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Lang, Andreas. "Face Detection using Swarm Intelligence." Technische Universität Chemnitz, 2010. https://monarch.qucosa.de/id/qucosa%3A19439.
Full textAbstract:
Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group
of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science,
particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying
structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J.
Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes
the ability to solve complex problems.
The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm
intelligence. The process developed for this purpose consists of a combination of various known structures, which are then
adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example
application program.:1 Introduction
1.1 Face Detection
1.2 Swarm Intelligence and Particle Swarm Optimisation Fundamentals
3 Face Detection by Means of Particle Swarm Optimisation
3.1 Swarms and Particles
3.2 Behaviour Patterns
3.2.1 Opportunism
3.2.2 Avoidance
3.2.3 Other Behaviour Patterns
3.3 Stop Criterion
3.4 Calculation of the Solution
3.5 Example Application
4 Summary and Outlook
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Djaneye-Boundjou, Ouboti Seydou Eyanaa. "Particle Swarm Optimization Stability Analysis." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1386413941.
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Hettiarachchi, Suranga D. "Distributed evolution for swarm robotics." Laramie, Wyo. : University of Wyoming, 2007. http://proquest.umi.com/pqdweb?did=1445057141&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.
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Tiboni, Ivan. "I principi della swarm intelligence." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4051/.
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