Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains xiii, 155 p.; also includes graphics (some col.) Includes bibliographical references (p. 150-155). Available online via OhioLINK's ETD Center

The goal of the research activities presented in this thesis is the design of intelligent behaviours for a complex robotic system, which is composed of a swarm of autonomous units. Inspired by the organisational skills of social insects, we are particularly interested in the study of collective behaviours based on self-organisation. The problem of designing self-organising behaviours for a swarm of robots is tackled resorting to artificial evolution, which proceeds in a bottom-up direction by first defining the controllers at the individual level and then testing their effect at the collective level. In this way, it is possible to bypass the difficulties encountered in the decomposition of the global behaviour into individual ones, and the further encoding of the individual behaviours into the controllers' rules. In the experiments presented in this thesis, we show that this approach is viable, as it produces efficient individual controllers and robust self-organising behaviours. To the best of our knowledge, our experiments are the only example of evolved self-organising behaviours that are successfully tested on a physical robotic platform. Besides the engineering value, the evolution of self-organising behaviours for a swarm of robots also provides a mean for the understanding of those biological processes that were a fundamental source of inspiration in the first place. In this perspective, the experiments presented in this thesis can be considered an interesting instance of a synthetic approach to the study of collective intelligence and, more in general, of Cognitive Science.

Human-swarm interaction studies how human beings can interact with a robotswarm---a large number of robots cooperating with each other without any form of centralizedcontrol. In today's human-swarm interaction literature, the large majority of the works investigatehow human beings can issue commands to and receive feedback from a robot swarm. However, only a few ofthese works study the effect of the interaction with a robot swarm on human psychology (e.g. on thehuman stress or on the human workload). Understanding human psychology in human-swarm interaction isimportant because the human psychological state can have significant impact on the way humansinteract with robot swarms (e.g. a high level of stress can cause a human operator to freeze in themiddle of a critical task, such as a search-and-rescue task). Most existing works that study human psychology in human-swarm interaction conduct their experimentsusing robot swarms simulated on a computer screen. The use of simulation is convenient becauseexperimental conditions can be repeated perfectly in different experimental runs and becauseexperimentation using real robots is expensive both in money and time. However, simulation suffersfrom the so-called reality gap: the inherent discrepancy between simulation and reality. Itis therefore important to study whether this inherent discrepancy can affect humanpsychology---human operators interacting with a simulated robot swarm can react differently thanwhen interacting with a real robot swarm.A large literature in human-robot interaction has studied the psychological impact of theinteraction between human beings and single robots. This literature could in principle be highlyrelevant to human-swarm interaction. However, an inherent difference between human-robot interactionand human-swarm interaction is that in the latter, human operators interact with a large number ofrobots. This large number of robots can affect human psychology---human operators interacting with alarge number of robots can react differently than when interacting with a single robot or with asmall number of robots. It is therefore important to understand whether the large number of robotsthat composes a robot swarm affects human psychology. In fact, if this is the case, it would not bepossible to directly apply the results of human-robot interaction research to human-swarminteraction.We conducted several experiments in order to understand the effect of the reality gap and the effectof the group size (i.e. the number of robots that composes a robot swarm) on the humanpsychological state. In these experiments our participants are exposed to swarms of robots and arepurely passive---they do not issue commands nor receive feedback from the robots. Making theinteraction passive allowed us to study the effects of the reality gap and of the group size on thehuman psychological state without the risk that an interaction interface (such as a joystick)influences the psychological responses of the participants (and thus limiting the visibility of both thereality gap and group size effects). In the reality gap experiments, participants are exposed tosimulated robot swarms displayed either on a computer screen or in a virtual reality environment, and toreal robot swarms. In the group size experiments, participants are exposed to an increasing numberof real robots.In this thesis, we show that the reality gap and the group size affect the human psychological stateby collecting psychophysiological measures (heart rate and skin conductance), self-reported (viaquestionnaires) affective state measures (arousal and valence), self-reported workload (the amountof mental resource needed to carry out a task) and reaction time (the time needed to respond to astimulus). Firstly, we show with our results that our participants' psychophysiological measures,affective state measures, workload and reaction time are significantly higher when they interactwith a real robot swarm compared to when they interact with a robot swarm simulated on a computerscreen, confirming that the reality gap significantly affects the human psychological state.Moreover, we show that it is possible to mitigate the effect of the reality gap using virtualreality---our participants' arousal, workload and reaction time are significantly higher when theyinteract with a simulated robot swarm displayed in a virtual reality environment as opposed to whenit is displayed on a computer screen. Secondly, we show that our participants' psychophysiologicalmeasures and affective state measures increase when the number of robots they are exposed toincreases. Our results have important implications for research in human-swarm interaction. Firstly, for thefirst time, we show that experiments in simulation change the human psychological state compared toexperiments with real robots. Secondly, we show that a characteristic that is inherent to thedefinition of swarm robotics---the large number of robots that composes a robotswarm---significantly affects the human psychological state. Finally, our results show thatpsychophysiological measures, such as heart rate and skin conductance, provide researchers with moreinformation on human psychology than the information provided by using traditional self-reportedmeasures (collected via psychological questionnaires). / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Power quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the stability of electrical power networks using artificial intelligence techniques. An intelligent controller was designed to control and optimise simulated models for electrical system power stability. Fuzzy logic controller controlled the power generation, while particle swarm Optimisation (PSO) techniques optimised the system’s power quality in normal operation conditions and after faults. Different types of PSO were tested, then a multi-swarm (M-PSO) system was developed to give better Optimisation results in terms of accuracy and convergence speed. The developed Optimisation algorithm was tested on seven benchmarks and compared to the other types of single PSOs. The developed controller and Optimisation algorithm was applied to power system stability control. Two power electrical network models were used (with two and four generators), controlled by fuzzy logic controllers tuned using the Optimisation algorithm. The system selected the optimal controller parameters automatically for normal and fault conditions during the operation of the power network. Multi objective cost function was used based on minimising the recovery time, overshoot, and steady state error. A supervisory control layer was introduced to detect and diagnose faults then apply the correct controller parameters. Different fault scenarios were used to test the system performance. The results indicate the great potential of the proposed power system stabiliser as a superior tool compared to conventional control systems.

Prekiaujant vertybiniais popieriais, svarbiausia yra priimti teisingą sprendimą: pirkti arba parduoti. Daugelis investuotojų prieš priimdami sprendimą atkreipia dėmesį į pasirinktos akcijos kainos kitimo grafiką ir vadovaujasi juo. Tačiau ne kiekvienas investuotojas galėtų tiksliai apibūdinti savo pasirinktą grafinį modelį. Problemos aktualumas - Prognozuoti rinkas yra pakankamai sudėtinga, pastebimas žymus akcijų kursų svyravimas. Ženklūs akcijų kursų pasikeitimai skaičiuojami ne per metus ar mėnesius, o dienomis ar net valandomis. Investitoriams, finansų analitikams finansinėse rinkose sunku dirbti. Spekuliavimas akcijomis aktyviose akcijų rinkose yra labai rizikingas, bet pelningas užsiėmimas. Pasiūlius sprendimo priėmimo metodą investavimo procesas techniniu požiūriu supaprastės ir nereikalaus didelių sąnaudų, bei gilių žinių, leis platesniam ratui žmonių įeiti į akcijų rinką. Problema – Sudėtingas akcijų rinkų prognozavimas, kadangi pastebimas žymus akcijų kursų svyravimas, todėl rizikinga spekuliuoti akcijomis aktyviose akcijų rinkose. Baigiamojo darbo objektas – sprendimo priėmimo metodas finansinių rinkų prognozėms atlikti, remiantis neuroniniais tinklais ir spiečiaus algoritmu. Baigiamojo darbo tikslas – Spiečiaus intelekto taikymo finansų rinkose analizė ir optimizavimas. / One of the central problems in financial markets is to make the profitable stocks trading decisions using historical stocks' market data. This paper presents the decision-making methodology which is based on the application of neural networks and swarm intelligence technologies and is used to generate one-step ahead investment decisions. In brief, the proposed methodology draws from the analysis of historical stock prices variations. The variations are passed to neural networks and the recommendations for the next day are calculated. The stocks with the highest recommendations are considered for further experimental investigations. The core idea of this algorithm is to select three best neural networks for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. The experimental results presented in the paper show that the application of our proposed methodology lets to achieve better results than the average of the market. The theme of the Master’s degree paper is “Analysis and Optimization of Swarm Intelligent in Financial Markets”. The object of the Master’s degree paper is decision making method for financial markets, re neural network and swarm intelligence.

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Chien-Chung Shen, Dept. of Computer & Information Sciences. Includes bibliographical references.

Thesis (M. Sc.)Computer Science)--University of Pretoria, 2005. / Includes summary. Includes bibliographical references. Available on the Internet via the World Wide Web.

Localization is one of the most important capabilities of mobile robots. Thanks to the fast development of embedded computing hardware in recent years, many localization solutions, such as simultaneous localization and mapping (SLAM), have been vastly investigated. However, popular localization solutions rely heavily on the working environment and are not applicable to scenarios such as search and rescue in the wild, where the working environment is not accessible before the localization operation or where the environment lacks information on features and textures. The thesis thus proposes a design for an innovative localization sensor and a collaborative pose estimation scheme using the localization sensor in order to alleviate the reliance on information from the environment, while providing reliable and accurate pose estimates for mobile robots. The proposed collaborative pose estimation scheme is comprised of individual and collaborative landmark position estimation, localization sensor inter-calibration, and collaborative sensor pose estimation, among which the inter-calibration process ensures that the sensor provides capability to also estimate orientations. In the collaborative scheme, multiple instances of the proposed sensor collaborate to estimate their respective poses by measuring the relative distance and angle among them, where the measurement errors are characterized as Gaussian white noise. Two instances of the proposed localization sensor are implemented, and the collaborative scheme is tested with the instances in the thesis. Both sensor instances reliably and accurately estimate the position of a stationary landmark, and it is demonstrated that the collaboratively estimated position estimate is more accurate than its individual counterpart. Additionally, the two instances also demonstrate their ability to track and estimate the position of a moving landmark. Lastly, the inter-calibration is experimentally validated with the instances with satisfactory performance. The experimental results presented in this work confirm the feasibility and usability of the proposed collaborative pose estimation scheme in a wide range of robotic applications.

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.

No description available.