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dc.contributor.advisorKing, Scott A.
dc.contributor.advisorGarcia, Luis
dc.contributor.advisorKing, Scott A.
dc.contributor.advisorGarcia, Luis
dc.contributor.authorKrell, Evan Andrew
dc.contributor.authorKrell, Evan Andrew
dc.date.accessioned2020-04-18T02:31:11Z
dc.date.accessioned2020-04-18T02:31:11Z
dc.date.available2020-04-18T02:31:11Z
dc.date.available2020-04-18T02:31:11Z
dc.date.issued2018-12
dc.date.issued2018-12
dc.identifier.urihttps://hdl.handle.net/1969.6/87820
dc.identifier.urihttps://hdl.handle.net/1969.6/87820
dc.description.abstractState of the art unmanned surface vehicles typically exhibit rudimentary autonomy, apart from navigation controllers. Sophisticated autopilots have enabled these vehicles to follow a path as a sequence of coordinates called waypoints, and related control tasks such as target-following, station keeping, and obstacle avoidance are well established. However, humans are typically making all the mission planning decisions. These increasingly capable platforms could offer an intelligent remote presence for the marine environment, but are used as tools with specific orders rather than as agents responsible for intelligently investigating its environment. This research attempts to increase the autonomy of unmanned surface vehicles by considering them as being controlled by multiple specialized intelligent agents, specifically, the Analyst, the Surveyor, and the Navigator. The Analyst role studies data from its environment to specify objectives. The Surveyor is responsible for conducting mission planning to efficiently meet as many objectives as possible while ensuring missions are within constraints such as time and energy limits. Missions are then executed by the Navigator. The major challenge in increasing autonomy is the high computational complexity of many of the tasks involved, such as path planning. An emphasis is placed on heuristic and metaheuristic algorithms that sacrifice optimality to make autonomy feasible. Examples of Surveyor and Analyst agents have been implemented and initial results of the techniques used to fulfill their roles are examined.en_US
dc.format.extent93 pagesen_US
dc.language.isoen_USen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rightsThis material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher.en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectmetaheuristic algorithmsen_US
dc.subjectmission planningen_US
dc.subjectpath planningen_US
dc.subjectroboticsen_US
dc.subjectunmanned surface vehiclesen_US
dc.titleAutonomous mission planning for unmanned surface vehicles piloted by multiple specialized agents using heuristic and metaheuristic techniquesen_US
dc.typeTexten_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorTexas A & M University--Corpus Christien_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
dc.contributor.committeeMemberSheta, Alaa
dc.contributor.committeeMemberSheta, Alaa
dc.description.departmentComputing Sciencesen_US
dc.description.collegeCollege of Science and Engineeringen_US
dc.type.genreThesisen_US


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States