{"id":{"repo_id":"tdl","oai_identifier":"oai:tdl-ir.tdl.org:2152/26388"},"canonical_url":"https://search.dev.ndltd.org/etd/tdl/oai:tdl-ir.tdl.org:2152/26388","repository":{"repo_id":"tdl","name":"Texas Digital Library","base_url":"https://tdl-ir.tdl.org/server/oai/request"},"display":{"title":"Lossless convexification of quadrotor motion planning with experiments","abstract":"This thesis describes a motion planning method that is designed to guide an autonomous quadrotor. The proposed method is based on a novel lossless convexication, which was first introduced in (12), that allows convex representations of many non-convex control constraints, such as that of the quadrotors. The second contribution of this thesis is to include two separate methods to generate path constraints that capture non-convex position constraints. Using the convexied optimal trajectory generation problem with physical and path constraints, an algorithm is developed that generates fuel optimal trajectories given the initial state and desired final state. As a proof of concept, a quadrotor testbed is developed that utilize a state-of-the-art motion tracking system. The quadrotor is commanded via a ground station where the convexified optimal trajectory generation algorithm is successfully implemented together with a trajectory tracking feedback controller.","abstract_html":"This thesis describes a motion planning method that is designed to guide an autonomous quadrotor. The proposed method is based on a novel lossless convexication, which was first introduced in (12), that allows convex representations of many non-convex control constraints, such as that of the quadrotors. The second contribution of this thesis is to include two separate methods to generate path constraints that capture non-convex position constraints. Using the convexied optimal trajectory generation problem with physical and path constraints, an algorithm is developed that generates fuel optimal trajectories given the initial state and desired final state. As a proof of concept, a quadrotor testbed is developed that utilize a state-of-the-art motion tracking system. The quadrotor is commanded via a ground station where the convexified optimal trajectory generation algorithm is successfully implemented together with a trajectory tracking feedback controller.","abstract_has_math":false,"creators":["Pehlivantürk, Can"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Longoria, Raul G.","Açıkmeşe, Behçet"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-08","date_published":"2014-08","updated_at":"2026-07-27T21:19:27Z","subjects":["Quadrotor","UAV","Trajectory generation","Convexification"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2152/26388"],"render_values":[{"text":"http://hdl.handle.net/2152/26388","href":"http://hdl.handle.net/2152/26388","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/2152/26388","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Longoria, Raul G.","Açıkmeşe, Behçet"]},{"key":"dc:creator","label":"Author","values":["Pehlivantürk, Can"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-03-24T19:48:22Z"]},{"key":"dc:date.issued","label":"Date","values":["2014-08"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Quadrotor","UAV","Trajectory generation","Convexification"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2152/26388"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2152/26388"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis describes a motion planning method that is designed to guide an autonomous quadrotor. The proposed method is based on a novel lossless convexication, which was first introduced in (12), that allows convex representations of many non-convex control constraints, such as that of the quadrotors. The second contribution of this thesis is to include two separate methods to generate path constraints that capture non-convex position constraints. Using the convexied optimal trajectory generation problem with physical and path constraints, an algorithm is developed that generates fuel optimal trajectories given the initial state and desired final state. As a proof of concept, a quadrotor testbed is developed that utilize a state-of-the-art motion tracking system. The quadrotor is commanded via a ground station where the convexified optimal trajectory generation algorithm is successfully implemented together with a trajectory tracking feedback controller."]},{"key":"dc:title","label":"Title","values":["Lossless convexification of quadrotor motion planning with experiments"]}]}],"canonical_facts":{"dc:contributor":["Longoria, Raul G.","Açıkmeşe, Behçet"],"dc:creator":["Pehlivantürk, Can"],"dc:date.accessioned":["2026-03-24T19:48:22Z"],"dc:date.issued":["2014-08"],"dc:description.abstract":["This thesis describes a motion planning method that is designed to guide an autonomous quadrotor. The proposed method is based on a novel lossless convexication, which was first introduced in (12), that allows convex representations of many non-convex control constraints, such as that of the quadrotors. The second contribution of this thesis is to include two separate methods to generate path constraints that capture non-convex position constraints. Using the convexied optimal trajectory generation problem with physical and path constraints, an algorithm is developed that generates fuel optimal trajectories given the initial state and desired final state. As a proof of concept, a quadrotor testbed is developed that utilize a state-of-the-art motion tracking system. The quadrotor is commanded via a ground station where the convexified optimal trajectory generation algorithm is successfully implemented together with a trajectory tracking feedback controller."],"dc:identifier":["http://hdl.handle.net/2152/26388"],"dc:identifier.uri":["https://hdl.handle.net/2152/26388"],"dc:language":["en"],"dc:subject":["Quadrotor","UAV","Trajectory generation","Convexification"],"dc:title":["Lossless convexification of quadrotor motion planning with experiments"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T21:19:27Z"}