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2S-PSO a dual state particle swarm optimizer /Hardin, Charles Timothy. January 1900 (has links)
Thesis (Ph.D.)--University of Louisville, 2007. / Adviser: Adel S. Elmaghraby. Includes bibliographical references.
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Distributed evolution for swarm roboticsHettiarachchi, Suranga D. January 2007 (has links)
Thesis (Ph.D.)--University of Wyoming, 2007. / Title from PDF title page (viewed on June 22, 2009). Includes bibliographical references (p. 179-183).
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Applications of biologically inspired algorithms to complex systems /Kassabalidis, Ioannis N. January 2002 (has links)
Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 103-113).
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The cognitive authority of collective intelligence /Goldman, James L. Atwood, Michael E. January 2010 (has links)
Thesis (Ph.D.)--Drexel University, 2010. / Includes abstract and vita. Includes bibliographical references (leaves XXX-XXX).
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A parallel computing test bed for performing an unsupervised fluoroscopic analysis of knee joint kinematics /Ramanatha, Renu. January 2009 (has links)
Thesis (M.S.)--Boise State University, 2009. / Includes abstract. Includes bibliographical references (leaves 58-60).
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A parallel computing test bed for performing an unsupervised fluoroscopic analysis of knee joint kinematicsRamanatha, Renu. January 2009 (has links)
Thesis (M.S.)--Boise State University, 2009. / Title from t.p. of PDF file (viewed May 6, 2010). Includes abstract. Includes bibliographical references (leaves 58-60).
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Analysis of the particle swarm optimization algorithmWilke, Daniel N. January 2005 (has links)
Thesis (M.Eng.)(Mechanical)--University of Pretoria, 2005. / Title from opening screen (viewed 20 March, 2006). Summaries in English and Afrikaans. Includes bibliographical references.
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Evaluation of using swarm intelligence to produce facility layout solutionsThai, Andrew Bao, January 2007 (has links) (PDF)
Thesis (M.Eng.)--University of Louisville, 2007. / Title and description from thesis home page (viewed May 9, 2007). Department of Computer Engineering and Computer Science. Vita. "May 2007." Includes bibliographical references (p. 47-49).
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Συνεργατικός έλεγχος δικτυωμένων ρομποτικών επίγειων οχημάτωνΚάνταρος, Ιωάννης 12 November 2012 (has links)
Ο σκοπός αυτής της διατριβής είναι να αναπτυχθούν σχέδια συντονισμού σχετικά με την κίνηση των ρομποτικών πρακτόρων με σκοπό την κάλυψη μιας περιοχής κάτω από RF επικοινωνιακούς περιορισμούς . Οι κόμβοι εκτελούν την κίνηση σε ξεχωριστά χρονικά βήματα σύμφωνα με τις διανεμημένες πληροφορίες που αποκτώνται από τους κόμβους που συνδέονται στον προκαθορισμένο αριθμό hops έως ότου φθάσουν στη βέλτιστη τοπολογία όσον αφορά την κάλυψη της περιοχής. Τα ρομπότ υποτίθεται ότι είναι εξοπλισμένα με έναν αισθητήρα για λόγους κάλυψης και με έναν ράδιο πομποδέκτη έτσι ώστε να μεταδοθούν οι πληροφορίες. Ωστόσο, η ακτίνα επικοινωνίας δεν απαιτείται να είναι τουλάχιστον διπλάσια της ακτίνας του αισθητήρα επισκόπησης, κάτι που προσθέτει έναν πρόσθετο περιορισμό στο γενικό πρόβλημα. Τα σχέδια συντονισμού αναπτύσσονται εξασφαλίζοντας την συνολική RF συνδεσιμότητα του δικτύου επιτυγχάνοντας τη βέλτιστη κάλυψη περιοχής. Τα αποτελέσματα ελέγχονται περαιτέρω μέσω των μελετών προσομοιώσεων. / The purpose of this thesis is to develop coordination schemes concerning the motion of robotic agents for area coverage purposes under RF communications constraints. The nodes perform motion in discrete time steps according to distributed information acquired from nodes which are connected at predefined number of hops until they reach optimum area configuration. Robots are supposed to be equipped with sensor for coverage purposes and with radio transceiver so as information to be transmitted. However, communication radius is not demanded to be at least equal to twice the sensing one, imposing an extra constraint in the overall problem. Coordination schemes are developed ensuring end-to-end RF connectivity of the network while attaining optimum area coverage. Results are further verified via simulations studies.
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Algorithms for Timing and Sequencing Behaviors in Robotic SwarmsNagavalli, Sasanka 01 May 2018 (has links)
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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