Tesis sobre el tema "Autonomous network control"
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Dutta, Rajdeep. "Cooperative control of autonomous network topologies". Thesis, The University of Texas at San Antonio, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10151348.
Texto completoIn this dissertation, we present novel solutions to cooperative control of autonomous multi-agent network topologies pertaining to the area of hostile target tracking by multiple unmanned aerial vehicles (UAVs). The present work assumes an undirected graph comprising point-mass UAVs with time-varying communication topology among agents. The level of information sharing ability among agents in a multi-agent network, i.e. the network connectivity, plays pivotal role in group dynamics. A neighborhood information based decentralized controller is proposed in order to drive UAVs into a symmetric formation of polygon shape surrounding a mobile target, simultaneously with maintaining and controlling connectivity during the formation process. Appropriate controller parameter selection schemes, both for controller weights and gains, are adapted for dynamic topologies to maintain the connectivity measure above zero at all times. A challenging task of tracking a desired connectivity profile along with the formation control, is accomplished by using time-varying controller gains throughout agents dynamics. We next present a generalized formation controller, which in fact generates a family of UAV trajectories satisfying the control criteria. The proposed decentralized controller contains additional tuning parameters as fractional powers on proportional and derivative terms, rendering flexibility in achieving the control objective. The proposed controller with proper fractional powers, results in gradual state changes in UAV dynamics by using limited control inputs. Moreover, we extend our work by addressing a ground target tracking and reacquiring problem using the visual information gathered by flying UAV. The proposed guidance law uses line-of-sight guidance to track the target pushing it towards the image center captured by UAV, and exploits UAV-target mutual information to reacquire the target in case it steers away from the field-of-view for a short time. The convergence of the closed loop systems under the proposed controllers are shown using Lyapunov theory. Simulation results validate the effectiveness and novelty of the proposed control laws.
In addition to the above, this work focuses on categorizing multi-agent topologies in concern with the network dynamics and connectivity to analyze, realize, and visualize multi-agent interactions. In order to explore various useful agents reconfiguration possibilities without compromising the network connectivity, the present work aims at determining distinct topologies with the same connectivity or isoconnected topologies. Different topologies with identical connectivity are found out with the help of analytic techniques utilizing matrix algebra and calculus of variation. Elegant strategies for preserving connectivity in a network with a single mobile agent and rest of the stationary members, are proposed in this work as well. The proposed solutions are validated with the help of sufficient examples. For visual understanding of how agents locations and topology configurations influence the network connectivity, a MATLAB based graphical user interface is designed to interact with multi-agent graphs in a user-friendly manner.
To this end, the present work succeeds to determine solutions to challenging multi-UAV cooperative control problems, such as: (1) Symmetric formation control surrounding a mobile target; (2) Maintaining, improving and controlling the network connectivity during a mission; and (3) Categorizing different multi-agent topologies to unravel useful reconfiguration options for a group. The proposed theories with appropriate analysis, and the simulation results suffice to show the contribution and novelty of this work.
Tung, Charles P. (Charles Patrick) 1974. "A distributed processing network for autonomous micro-rover control". Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47542.
Texto completoIncludes bibliographical references (leaf 77).
by Charles P. Tung.
B.S.
M.Eng.
Hemlin, Karl y Frida Persson. "Remote Control Operation of Autonomous Cars Over Cellular Network Using PlayStation Controller". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254218.
Texto completoGarratt, Matthew Adam y m. garratt@adfa edu au. "Biologically Inspired Vision and Control for an Autonomous Flying Vehicle". The Australian National University. Research School of Biological Sciences, 2008. http://thesis.anu.edu.au./public/adt-ANU20090116.154822.
Texto completoDalamagkidis, Konstantinos. "Autonomous vertical autorotation for unmanned helicopters". [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0003147.
Texto completoYoumans, Elisabeth A. "Neural network control of space vehicle orbit transfer, intercept, and rendezvous maneuvers". Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06062008-162101/.
Texto completoDarr, Matthew John. "DEVELOPMENT AND EVALUATION OF A CONTROLLER AREA NETWORK BASED AUTONOMOUS VEHICLE". UKnowledge, 2004. http://uknowledge.uky.edu/gradschool_theses/192.
Texto completoPuttige, Vishwas Ramadas Engineering & Information Technology Australian Defence Force Academy UNSW. "Neural network based adaptive control for autonomous flight of fixed wing unmanned aerial vehicles". Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/43736.
Texto completoLivianu, Mathew Joseph. "Human-in-the-loop neural network control of a planetary rover on harsh terrain". Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26576.
Texto completoCommittee Chair: Dr. Ayanna Howard; Committee Member: Dr. Patricio Vela; Committee Member: Dr. Yoria Wardi. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Kam, Khim Yee. "High bandwidth communications links between heterogeneous autonomous vehicles using sensor network modeling and extremum control approaches". Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://edocs.nps.edu/npspubs/scholarly/theses/2008/Dec/08Dec%5FKam.pdf.
Texto completoThesis Advisor(s): Kaminer, Isaac I. ; Lee, Deok Jin. "December 2008." Description based on title screen as viewed on January 29, 2009. Includes bibliographical references (p. 57-58). Also available in print.
de, Freitas Edison Pignaton, Tales Heimfarth, Armando Morado Ferreira, Flávio Rech Wagner, Carlos Eduardo Pereira y Tony Larsson. "An agent framework to support sensor networks’ setup and adaptation". Högskolan i Halmstad, Inbyggda system (CERES), 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-14691.
Texto completoCheng, Yongqiang. "Wireless mosaic eyes based robot path planning and control : autonomous robot navigation using environment intelligence with distributed vision sensors". Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4421.
Texto completoKrishnakumar, Sita Srinivasaraghavan. "Intelligent actor mobility in wireless sensor and actor networks". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24735.
Texto completoCommittee Chair: Abler, Randal T.; Committee Member: Copeland, John A.; Committee Member: Haas, Kevin; Committee Member: Moore II, Elliot; Committee Member: Owen III, Henry L.
Iyengar, Navneet. "Providing QoS in Autonomous and Neighbor-aware multi-hop Wireless Body Area Networks". University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439306913.
Texto completoRojas, Castro Dalia Marcela. "The RHIZOME architecture : a hybrid neurobehavioral control architecture for autonomous vision-based indoor robot navigation". Thesis, La Rochelle, 2017. http://www.theses.fr/2017LAROS001/document.
Texto completoThe work described in this dissertation is a contribution to the problem of autonomous indoor vision-based mobile robot navigation, which is still a vast ongoing research topic. It addresses it by trying to conciliate all differences found among the state-of-the-art control architecture paradigms and navigation strategies. Hence, the author proposes the RHIZOME architecture (Robotic Hybrid Indoor-Zone Operational ModulE) : a unique robotic control architecture capable of creating a synergy of different approaches by merging them into a neural system. The interactions of the robot with its environment and the multiple neural connections allow the whole system to adapt to navigation conditions. The RHIZOME architecture preserves all the advantages of behavior-based architectures such as rapid responses to unforeseen problems in dynamic environments while combining it with the a priori knowledge of the world used indeliberative architectures. However, this knowledge is used to only corroborate the dynamic visual perception information and embedded knowledge, instead of directly controlling the actions of the robot as most hybrid architectures do. The information is represented by a sequence of artificial navigation signs leading to the final destination that are expected to be found in the navigation path. Such sequence is provided to the robot either by means of a program command or by enabling it to extract itself the sequence from a floor plan. This latter implies the execution of a floor plan analysis process. Consequently, in order to take the right decision during navigation, the robot processes both set of information, compares them in real time and reacts accordingly. When navigation signs are not present in the navigation environment as expected, the RHIZOME architecture builds new reference places from landmark constellations, which are extracted from these places and learns them. Thus, during navigation, the robot can use this new information to achieve its final destination by overcoming unforeseen situations.The overall architecture has been implemented on the NAO humanoid robot. Real-time experimental results during indoor navigation under both, deterministic and stochastic scenarios show the feasibility and robustness of the proposed unified approach
Silva, Joelson Coelho da. "Uma proposta de controle neural adaptativo para a navegação de veículos autônomos". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1999. http://hdl.handle.net/10183/18631.
Texto completoThe robotic equipments were created initially to actuate in closed industrial environments. Improvements have been acquieved in this area. Nowadays, they are no longer limited to perform simple and repetitive tasks in controlled places. New equipments, capable of acting in open environments and doing the most several activities, are being developed. For so much, it is necessary that its control systems accomplish an effective interaction with the world where they are inserted. Therefore, new systems controllers with capacity of a continuous adaptation to the dynamic environments are essential. Artificial neural networks, due to their capacity of dealing wit non-linear problems – mathematically difficult to be solved – are being used to control these kind of processes. Guide a mobile vehicle through an open or controlled environments is a highly non-linear procedure; therefore, the use of an artificial neural nets is quite promising. In spite of its great versatility, they have just been used as mapping systems. Most of them need a training phase so that they can store the diversity of system’s possible states. When they actuate, they simply map their input values (current state) to the solutions previously stored. However, this is not the best approach for open systems, i.e. systems whose situations and possibilities cannot be totally enumerated and that can change in time. This work presents an adaptive neural control methodology to guide a mobile vehicle to its target in environments with fixed or mobile obstacles. Differently from the traditional approaches, the need of a previous training phase of the neural network doesn't exist. The chosen model of artificial neural net promotes a continuous adaptation of the system while it actuates. Sensors are used to provide informations to the net. This way it generates partial solutions that makes the autonomous vehicle gets closer of its goal, until, finally, reach it.
Svensson, August. "Range-based Wireless Sensor Network Localization for Planetary Rovers". Thesis, Luleå tekniska universitet, Rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83213.
Texto completoDvořáček, Jiří. "Autonomní a dispečerské řízení distribuovaných zdrojů v distribuční síti VN". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442469.
Texto completoHallqvist, Erik y Sebastian Håkansson. "Networked control of autonomous ground vehicles". Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199347.
Texto completoEhrlin, E. y M. Törnqvist. "Networked control of autonomous ground vehicles". Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199313.
Texto completoAthari, Kayvan. "Networked control of autonomous ground vehicles". Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199389.
Texto completoBakutis, Vladas y Qiao Jin. "Networked Control of Autonomous Ground Vehicles". Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200572.
Texto completoUnnikrishnan, Suraj. "Adaptive Envelope Protection Methods for Aircraft". Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11478.
Texto completoRush, Jonathan Reginald. "Evolving cellular neural networks for autonomous robot control". Thesis, University of Salford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308293.
Texto completoOrihuela, Swartling Johanna y Magnus Pontusson. "Cooperative networked control of autonomous ground vehicles". Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199264.
Texto completoLycke, Jens y Fredrik Svensson. "Cooperative networked control of autonomous ground vehicles". Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199265.
Texto completoLiu, Bing. "Contrôle et optimisation des systèmes de transport intelligents dans le voisinage des intersections". Thesis, Ecole centrale de Lille, 2016. http://www.theses.fr/2016ECLI0008/document.
Texto completoThis thesis is devoted to study the potential applications of autonomous vehicles and V2X communications to construct the intelligent transportation systems. Firstly, the behavior of platoon in connected vehicle environment is studied. A platoon control algorithm is designed to obtain safe spacing as well as accordance of velocity and acceleration for vehicles in the same lane. Secondly, in larger scale, the platoons around an intersection are considered. The throughput in a traffic signal period can be improved by taking advantage of the redundant road capacity. Within diverse constraints, vehicles can choose to accelerate to join in the preceding platoon or to decelerate to depart from the current one. Thirdly, an unsignalized intersection in VANET is considered. In light traffic conditions, vehicles can regulate their velocities before arriving at the intersection according to the conflict zone occupancy time (CZOT) stored at the manager, so that they could get through the intersection without collision or stop. The delay can be reduced accordingly. Finally, an universal autonomous intersection management algorithm, which can work even with heavy traffic, is developed. The vehicle searches for safe entering windows in the CZOT. Then based on the found windows and the motion of preceding vehicle, the trajectories of vehicles can be planned using a segmented dynamic programming method. All the designed algorithms are successfully tested and verified by simulations in various scenarios
Smart, Royce Raymond y roycesmart@hotmail com. "Evolutionary Control of Autonomous Underwater Vehicles". RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090331.143104.
Texto completoAmir, Mohammad. "Semantically-enriched and semi-Autonomous collaboration framework for the Web of Things. Design, implementation and evaluation of a multi-party collaboration framework with semantic annotation and representation of sensors in the Web of Things and a case study on disaster management". Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/14363.
Texto completoAlliche, Abderrahmane Redha. "Contrôle du réseau cloud basé intelligence artificielle". Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4022.
Texto completoThe exponential growth of Internet traffic in recent decades has prompted the emergence of Content Delivery Networks (CDNs) as a solution for managing high traffic volumes through data caching in cloud servers located near end-users. However, challenges persist, particularly for non-cacheable services, necessitating the use of cloud overlay networks. Due to a lack of knowledge about the underlay network, cloud overlay networks introduce complexities such as Triangle inequality violations (TIV) and dynamic traffic routing challenges.Leveraging the Software Defined Networks (SDN) paradigm, Deep Reinforcement Learning (DRL) techniques offer the possibility to exploit collected data to better adapt to network changes. Furthermore, the increase of cloud edge servers presents scalability challenges, motivating the exploration of Multi-Agent DRL (MA-DRL) solutions. Despite its suitability for the distributed packet routing problem in cloud overlay networks, MA-DRL faces non-addressed challenges such as the need for realistic network simulators, handling communication overhead, and addressing the multi-objective nature of the routing problem.This Ph.D. thesis delves into the realm of distributed Multi-Agent Deep Reinforcement Learning (MA-DRL) methods, specifically targeting the Distributed Packet Routing problem in cloud overlay networks. Throughout the thesis, we address these challenges by developing realistic network simulators, studying communication overhead in the non-overlay general setting, and proposing a distributed MA-DRL framework tailored to cloud overlay networks, focusing on communication overhead, convergence, and model stability
Janet, Jason Andre. "Pattern analysis, tracking and control for autonomous vehicles using neural networks". Raleigh, NC : North Carolina State University, 1998. http://www.lib.ncsu.edu/etd/public/etd-45309999852811/etd.pdf.
Texto completoJanet, Jason Andre. "Pattern Analysis, Tracking and Control for Autonomous Mobile Robots Using Neural Networks". NCSU, 1998. http://www.lib.ncsu.edu/theses/available/etd-19981003-104929.
Texto completoAutonomous vehicles require that all on-board processes be efficient in time, complexity and data storage. Infact, an ideal system employs multi-funcitonal models where ever possible. The research documented hereproposes that the Region-Feature Neural Network (RFNN) and the Hyper-Ellipsoid Clustering (HEC)Kohonen neural network (or HECNN) are viable pattern analysis and control engines that contribute to thesolution of a variety of problems. The theoretical development of the RFNN and HECNN, along with severalproof-of-concept applications are presented in detail. The RFNN is a feed-forward, back-propagation modelthat is more general than standard textbook models because it also accomodates receptive fields and weightsharing. The RFNN uses a modified version of adaptive learning rates, called "shocking" to reduce training timeand maintain stability. Small-scale benchmark problems like the XOR and XOP problems are used todemonstrate the utility of the "shocking" model. Due to its modularity, the RFNN allows the user to constructflexible, multi-layered, feed-forward architectures as well as add to and prune from an architecture even aftertraining has begun. The RFNN also permits the user to include previously learned features, called "analogies" tofurther expedite the training process on new problems or whenever new classes are added. The HECNN is aself-organizing neural network that incorporates hyperellipsoid clustering by use of the Mahalanobis distance tolearn elongated shapes and obtain a stochastic measurement of data-node association. The number of nodes canalso be regulated in a self-organizing manner by measuring how well each node models the statistical propertiesof its associated data. This measurement, called "compactness", determines where and whether to add neuralunits or prune them completely. We make several enhancements to the Kolmogorov-Smirnov compactness testto control the triggering of mitosis and/or pruning. Because fewer nodes are needed for an HECNN than for aKohonen that uses only Euclidean distance, the data size is smaller for the HEC Kohonen, even forhigh-dimensional problems. The large-scale pattern analysis problems presented here for the RFNN includesonar pattern recognition and outdoor landmark recognition. For the HECNN, we focus on sonar patternrecognition and (topographical) map building. Both the RFNN and the HECNN can be generalized to solve orcontribute to the solution of other pattern recognition problems. Both are also multifunctional in that theyaccommodate compact geometric motion planning (MP), self-referencing (SR) and tracking algorithms.Additionally, we propose the "traversability vector" (t-vector) as an efficient bridge between the HECNN andboth motion planning and self-referencing for mobile robots. As with the RFNN and HECNN, the t-vector is amodular and multi-functional tool that minimizes the computation requirements and data size as it detects pathobstructions, Euclidean optimal via points, and geometric beacons, as well as identify which geometric featuresare visible to sensors in environments that can be static or dynamic. Tracking is made possible with Julier andUhlmann's unscented filter. The unscented filter particularly compliments the HECNN in that it performslow-level (non-linear) tracking more efficiently and more accurately than its predecessor, the extended Kalmanfilter (EKF). By estimating and propagating error covariances through system transformations, the unscentedfilter eliminates the need to derive Jacobian matrices. The inclusion of stochastic information inherent to the HECmap rendered the JUKF an excellent tool for our HEC-based map building, global self-localization, motionplanning and low-level tracking.
Glöde, Isabella. "Autonomous control of a mobile robot with incremental deep learning neural networks". Master's thesis, Pontificia Universidad Católica del Perú, 2021. http://hdl.handle.net/20.500.12404/18676.
Texto completoRichard, Mark G. "Cooperative control of distributed autonomous systems with applications to wireless sensor networks". Thesis, Monterey, Calif. : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Jun/09Jun%5FRichard.pdf.
Texto completoThesis Advisor(s): Lee, Deok Jin ; Kaminer, Issac I. "June 2009." Description based on title screen as viewed on 13 July 2009. Author(s) subject terms: Unmanned Aerial Vehicle, UAV, extremum seeking, simulink, high bandwidth communication links, SNR Model, coordinated control, cooperative control, decentralized control, wireless sensor network. Includes bibliographical references (p. 51). Also available in print.
Водоп'янов, Сергій В’ячеславович. "Методи побудови автономних комп’ютерних сегментів аеровузлової мережі". Thesis, Національний авіаційний університет, 2018. http://er.nau.edu.ua/handle/NAU/37402.
Texto completoУ дисертаційній роботі розроблено методи побудови автономних сегментів інформаційно-обчислювальної системи реального часу для роботи в умовах критичного застосування, значних коливань навантаження та виникнення екстремальних ситуацій в аеровузлових мережах складеного типу з високим степенем гетерогенності. Розроблено математичну модель бортової локальної мережі з мобільними вузлами та спорадичною зміною структури, коли одні вузли виходять з зони дії мережі, нові вузли з’являються. Виведено розрахункові формули для середнього часу очікування заявок у пам'яті, середньої довжини черги тощо для змішаного – еластичного та нееластичного трафіку. Розраховані можливі розміри черг у буферній пам'яті мережних вузлів за наявністю заявок (сигнальних пакетів) з необмеженим часом очікування та заявок з обмеженим часом очікування у чергах. За результатами розрахунків можна обирати максимально можливий коефіцієнт використання мережі, при якому зростання черги у буферній пам'яті є припустимим. Запропоновані структури сегментів аеровузлової мережі і локальних обчислювальних мереж для систем критичного застосування, які можуть служити в основі побудови інформаційно-обчислювальної підсистеми АС ОрПР, тобто розгалуженої аеровузлової мережі з автономними супутниковими та авіаційними бортовими мережними сегментами.
Beckman, Erik y Linus Harenius. "Monitored Neural Networks for Autonomous Articulated Machines". Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48708.
Texto completoAhmad, Syed Amaar. "Autonomous Link-Adaptive Schemes for Heterogeneous Networks with Congestion Feedback". Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/46725.
Texto completoPh. D.
Grebner, Anna-Maria Stephanie. "Autonomous obstacle avoidance and positioning control of mobile robots using fuzzy neural networks". Master's thesis, Pontificia Universidad Católica del Perú, 2018. http://tesis.pucp.edu.pe/repositorio/handle/123456789/12893.
Texto completoTesis
Pérez, Guirao María Dolores. "Cross layer, cognitive, cooperative pulse rate control for autonomous, low power, IR-UWB networks". Aachen Shaker, 2008. http://d-nb.info/993570593/04.
Texto completoButtar, Sarpreet Singh. "Applying Artificial Neural Networks to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work". Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87117.
Texto completoWatson, Simon Andrew. "Mobile platforms for underwater sensor networks". Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/mobile-platforms-for-underwater-sensor-networks(00f93130-f9d6-4479-80ab-58a0c60327c0).html.
Texto completoRypkema, Nicholas Rahardiyan. "Distributed autonomy and formation control of a drifting swarm of autonomous underwater vehicles". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101474.
Texto completoThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 163-168).
Recent advances in autonomous underwater vehicle (AUV) technology have led to their wide- spread acceptance and adoption for use in scientific, commercial, and defence applications in the underwater domain. At the same time, research progress in swarm robotics has seen swarm intelligence algorithms in use with greater eect on real-world robots in the field. A group of AUVs utilizing swarm intelligence concepts has the potential to address issues more effectively than a single AUV, and such a group can potentially open up new areas of application. Examples include the monitoring and tracking of highly dynamic oceanographic phenomena such as phytoplankton blooms and the use of an AUV swarm as a virtual acoustic receiver for sea-bottom seismic surveying or the monitoring of naturally occurring acoustic radiation from cracking ice. However, the limitations of the undersea environment places unique constraints on the use of existing swarm robotics approaches with AUVs. In particular, algorithms must be distributed and robust in the face of localization error and degraded communications. This work presents an investigation into one particular swarm strategy for a group of AUVs, termed formation control, with consideration to the constraints of the underwater domain. Four formation control algorithms, each developed and tested within the MOOS-IvP framework, are presented. In addition, a 'formation quality' metric is introduced. This metric is used in conjunction with a measure of formation energy expenditure to compare the efficacy of each behaviour during construction of a desired formation, and formation maintenance while it drifts in ocean currents. This metric is also used to compare robustness of each algorithm in the presence of vehicle failure and changing communication rate.
by Nicholas Rahardiyan Rypkema.
S.M.
Sattigeri, Ramachandra Jayant. "Adaptive Estimation and Control with Application to Vision-based Autonomous Formation Flight". Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16272.
Texto completoYoung, Forrest C. "Phoenix autonomous underwater vehicle (AUV) : networked control of multiple analog and digital devices using LonTalk /". Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA342308.
Texto completo"December 1997." Thesis advisor(s): Xiaoping Yun, Don Brutzman. DTIC Descriptors: Underwater Vehicles, Autonomous Navigation, Digital Communications, Signal Processing, Robotics, Real Time, Robots, Computer Architecture, Theses, Analog to Digital Converters, Digital To Analog Converters. Author(s) subject terms: Autonomous Underwater Vehicle, AUV, Networked Control, Lon Works Technology, LonTalk, LonBuilder. Includes bibliographical references (p. 93-94). Also available online.
Pérez, Guirao María Dolores [Verfasser]. "Cross-Layer, Cognitive, Cooperative Pulse Rate Control for Autonomous, Low Power, IR-UWB Networks / María Dolores Pérez Guirao". Aachen : Shaker, 2009. http://d-nb.info/112637864X/34.
Texto completoKannan, Suresh Kumar. "Adaptive Control of Systems in Cascade with Saturation". Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7566.
Texto completoJugade, Shriram. "Shared control authority between human and autonomous driving system for intelligent vehicles". Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2507.
Texto completoRoad traffic accidents have always been a concern to the driving community which has led to various research developments for improving the way we drive the vehicles. Since human error causes most of the road accidents, introducing automation in the vehicle is an efficient way to address this issue thus making the vehicles intelligent. This approach has led to the development of ADAS (Advanced Driver Assistance Systems) functionalities. The process of introducing automation in the vehicle is continuously evolving. Currently the research in this field has targeted full autonomy of the vehicle with the aim to tackle the road safety to its fullest potential. The gap between ADAS and full autonomy is not narrow. One of the approach to bridge this gap is to introduce collaboration between human driver and autonomous system. There have been different methodologies such as haptic feedback, cooperative driving where the autonomous system adapts according to the human driving inputs/intention for the corrective action each having their own limitations. This work addresses the problem of shared control authority between human driver and autonomous driving system without haptic feedback using the fusion of driving inputs. The development of shared control authority is broadly divided into different stages i.e. shared control framework, driving input assessment, driving behavior prediction, fusion process etc. Conflict resolution is the high level strategy introduced in the framework for achieving the fusion. The driving inputs are assessed with respect to different factors such as collision risk, speed limitation,lane/road departure prevention etc in the form of degree of belief in the driving input admissibility using sensor data. The conflict resolution is targeted for a particular time horizon in the future using a sensor based driving input prediction using neural networks. A two player non-cooperative game (incorporating admissibility and driving intention) is defined to represent the conflict resolution as a bargaining problem. The final driving input is computed using the Nash equilibrium. The shared control strategy is validated using a test rig integrated with the software Simulink and IPG CarMaker. Various aspects of shared control strategy such as human-centered, collision avoidance, absence of any driving input, manual driving refinement etc were included in the validation process
Ruini, Fabio. "Distributed control for collective behaviour in micro-unmanned aerial vehicles". Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1549.
Texto completoAbdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering". Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.
Texto completoTanguy, Roger. "Un reseau mobiles autonomes pour l'apprentissage de la communication". Paris 6, 1987. http://www.theses.fr/1987PA066640.
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