Autonomous And Cooperative Multi-UAV Guidance In Adversarial Environment

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dc.contributor Zengin , Ugur en_US 2007 -08 -23T01 :56 :09Z 2011 -08 -24T21 :39 :50Z 2007 -08 -23T01 :56 :09Z 2011 -08 -24T21 :39 :50Z 2007 -08 -23T01 :56 :09Z May 2007 en_US
dc.identifier.uri http : / /hdl .handle .net /10106 /139
dc.description.abstract The research presented in this dissertation is aimed at developing rule -based autonomous and cooperative guidance strategies for UAVs to perform missions such as path planning , target tracking and rendezvous while reducing their risk /threat exposure level , and avoiding threats and /or obstacles by utilizing measurement information provided by sensors . First , a mathematical formulation is developed to represent the area of operation that contains various types of threats , obstacles , restricted areas , in a single framework . Once constructed , there will be no need to distinguish between adversaries as the framework already contains the information on what needs to be avoided and the level of penalty for a given position in the area . This framework provides the mathematical foundation for the guidance strategies to make intelligent decisions during the execution of the mission and also provides scalar metrics to assess the performance of a guidance strategy in a given mission . The autonomous guidance strategies are developed by using a rule -based expert system approach with the requirements of completing assigned mission or task , avoiding obstacle /restricted -areas , minimizing threat exposure level , considering the dynamic and communication constraints of the UAVs and avoiding collision . All these requirements and objectives are quantified and prioritized to facilitate the development of guidance algorithms that can be executed in real - -time . Cooperation of UAVs is modeled by minimizing a cost function , which is constructed based on the level of threat exposure for each UAV and distance of each UAV relative to the target . This improves the performance of the system in the terms of increasing the total area of coverage of the sensors onboard the UAVs , increasing the flexibility of the UAVs to search for better trajectories in terms of obstacle avoidance and threat exposure minimization , and improving the estimation by providing additional measurement . Finally , the performances of the algorithms are evaluated in a simulation environment , which includes the dynamics of each vehicle involved , the models of sensor measurement and data communication with different sampling rates , and the discrete execution of the algorithms . The simulation results demonstrate that the proposed algorithms successfully generates the trajectories that satisfy the given mission objectives and requirements . en_US
dc.language.iso EN en_US
dc.publisher Aerospace Engineering en_US
dc.title Autonomous And Cooperative Multi -UAV Guidance In Adversarial Environment en_US
dc.type Ph .D . en_US


Autonomous And Cooperative Multi-UAV Guidance In Adversarial Environment. Available electronically from http : / /hdl .handle .net /10106 /139 .

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