{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108446"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108446","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Neural network modeling of the dynamics of autonomous underwater vehicles for Kalman filtering and improved localization","abstract":"Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles are used for a variety of underwater operations and deep-sea explorations. One of the major challenges faced by these vehicles is localization i.e., the ability of these vehicles to identify their location with respect to a reference point. The kinematic Extended Kalman filters have been used in localization in a method known as dead reckoning. The accuracy of the localization systems can be improved if a dynamic model is used instead of the kinematic model. The previously derived dynamic model was implemented in real time in UUVSim, a simulation environment. The dynamic model was tested against the kinematic model on various test courses and it was found that the dynamic model was more stable and accurate than the kinematic model. One of the major drawbacks of the dynamic model was that it required the use of numerous coefficients. The process of determining these coefficients was extensive, requiring significant experimentation time. This research explores the use of a Neural Network architecture to replace these dynamic equations. Initial experiments have showed promising results for the Neural Network although modifications will be required before the controller can be made universally applicable.","abstract_html":"Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles are used for a variety of underwater operations and deep-sea explorations. One of the major challenges faced by these vehicles is localization i.e., the ability of these vehicles to identify their location with respect to a reference point. The kinematic Extended Kalman filters have been used in localization in a method known as dead reckoning. The accuracy of the localization systems can be improved if a dynamic model is used instead of the kinematic model. The previously derived dynamic model was implemented in real time in UUVSim, a simulation environment. The dynamic model was tested against the kinematic model on various test courses and it was found that the dynamic model was more stable and accurate than the kinematic model. One of the major drawbacks of the dynamic model was that it required the use of numerous coefficients. The process of determining these coefficients was extensive, requiring significant experimentation time. This research explores the use of a Neural Network architecture to replace these dynamic equations. Initial experiments have showed promising results for the Neural Network although modifications will be required before the controller can be made universally applicable.","abstract_has_math":false,"creators":["Balasubramanian, Sharan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Norris, William Robert"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-10-07T20:59:36Z","date_published":"2020-10-07T20:59:36Z","updated_at":"2026-07-22T22:24:48Z","subjects":["Kalman Filter","Neural Networks","Dynamic Modeling","Autonomous Underwater Vehicle","RexROV","EKF"],"languages":["en"],"rights":["Copyright 2020 Sharan Balasubramanian"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108446","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Norris, William Robert"]},{"key":"dc:creator","label":"Author","values":["Balasubramanian, Sharan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-10-07T20:59:36Z","2020-07-08","2020-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Kalman Filter","Neural Networks","Dynamic Modeling","Autonomous Underwater Vehicle","RexROV","EKF"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Sharan Balasubramanian"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108446"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles are used for a variety of underwater operations and deep-sea explorations. One of the major challenges faced by these vehicles is localization i.e., the ability of these vehicles to identify their location with respect to a reference point. The kinematic Extended Kalman filters have been used in localization in a method known as dead reckoning. The accuracy of the localization systems can be improved if a dynamic model is used instead of the kinematic model. The previously derived dynamic model was implemented in real time in UUVSim, a simulation environment. The dynamic model was tested against the kinematic model on various test courses and it was found that the dynamic model was more stable and accurate than the kinematic model. One of the major drawbacks of the dynamic model was that it required the use of numerous coefficients. The process of determining these coefficients was extensive, requiring significant experimentation time. This research explores the use of a Neural Network architecture to replace these dynamic equations. Initial experiments have showed promising results for the Neural Network although modifications will be required before the controller can be made universally applicable.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-10-02 without embargo terms","The student, Sharan Balasubramanian, accepted the attached license on 2020-07-06 at 15:45.","The student, Sharan Balasubramanian, submitted this Thesis for approval on 2020-07-06 at 15:50.","This Thesis was approved for publication on 2020-07-08 at 08:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15496 on 2020-10-02 at 15:11:46","Made available in DSpace on 2020-10-07T20:59:36Z (GMT). No. of bitstreams: 2 BALASUBRAMANIAN-THESIS-2020.pdf: 1214736 bytes, checksum: 5533fbab547925241778f5df5956c65a (MD5) LICENSE.txt: 4219 bytes, checksum: 99b3e00d111f494324d61b6c322280dc (MD5) Previous issue date: 2020-07-08"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Neural network modeling of the dynamics of autonomous underwater vehicles for Kalman filtering and improved localization"]}]}],"canonical_facts":{"dc:contributor":["Norris, William Robert"],"dc:creator":["Balasubramanian, Sharan"],"dc:date":["2020-10-07T20:59:36Z","2020-07-08","2020-08"],"dc:description":["Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles are used for a variety of underwater operations and deep-sea explorations. One of the major challenges faced by these vehicles is localization i.e., the ability of these vehicles to identify their location with respect to a reference point. The kinematic Extended Kalman filters have been used in localization in a method known as dead reckoning. The accuracy of the localization systems can be improved if a dynamic model is used instead of the kinematic model. The previously derived dynamic model was implemented in real time in UUVSim, a simulation environment. The dynamic model was tested against the kinematic model on various test courses and it was found that the dynamic model was more stable and accurate than the kinematic model. One of the major drawbacks of the dynamic model was that it required the use of numerous coefficients. The process of determining these coefficients was extensive, requiring significant experimentation time. This research explores the use of a Neural Network architecture to replace these dynamic equations. Initial experiments have showed promising results for the Neural Network although modifications will be required before the controller can be made universally applicable.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-10-02 without embargo terms","The student, Sharan Balasubramanian, accepted the attached license on 2020-07-06 at 15:45.","The student, Sharan Balasubramanian, submitted this Thesis for approval on 2020-07-06 at 15:50.","This Thesis was approved for publication on 2020-07-08 at 08:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15496 on 2020-10-02 at 15:11:46","Made available in DSpace on 2020-10-07T20:59:36Z (GMT). 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