In the modern world, various missions are being carried out under the assistance of autonomous flight vehicles due to their ability to operate in a wide range of flight conditions. Regardless, these autonomous vehicles are prone to GPS signal loss in urban environments due to obstructions that cause scintillation, multi-path, and shadowing. These effects that decrease the GPS functionality can deteriorate the accuracy of GPS positioning causing losses in signal tracking leading to a decrease in navigation performance. These effects are modeled into the simulation environment and are used as part of the path planning algorithm to provide better navigation strategies. This thesis aims to provide an implementation of A* algorithm in combination with RRT* path planning algorithm to detect and avoid areas with degraded GPS signals. The trajectory generation will consider a quadcopter vehicle dynamics when generating paths. A model of the quadcopter is used to illustrate the validation of this approach in a simulation environment with the GPS model integrated.
","abstract_html":"<p>In the modern world, various missions are being carried out under the assistance of autonomous flight vehicles due to their ability to operate in a wide range of flight conditions. Regardless, these autonomous vehicles are prone to GPS signal loss in urban environments due to obstructions that cause scintillation, multi-path, and shadowing. These effects that decrease the GPS functionality can deteriorate the accuracy of GPS positioning causing losses in signal tracking leading to a decrease in navigation performance. These effects are modeled into the simulation environment and are used as part of the path planning algorithm to provide better navigation strategies. This thesis aims to provide an implementation of A* algorithm in combination with RRT* path planning algorithm to detect and avoid areas with degraded GPS signals. The trajectory generation will consider a quadcopter vehicle dynamics when generating paths. A model of the quadcopter is used to illustrate the validation of this approach in a simulation environment with the GPS model integrated.</p>","abstract_has_math":false,"creators":["Raminedi, Ayush"],"institution":null,"degree_name":"Master of Science in Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-04-01T07:00:00Z","date_published":"2024-04-01T07:00:00Z","updated_at":"2026-07-27T19:25:10Z","subjects":["RRT*","A*","DOP","Simulation Environment","Quadcopter","Monte Carlo","Urban Environments","Occupancy Map","Multi-Path Effects","NLDI","UAV","Aeronautical Vehicles","Aviation Safety and Security","Navigation, Guidance, Control and Dynamics","Robotics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/808","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Raminedi, Ayush"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["RRT*","A*","DOP","Simulation Environment","Quadcopter","Monte Carlo","Urban Environments","Occupancy Map","Multi-Path Effects","NLDI","UAV","Aeronautical Vehicles","Aviation Safety and Security","Navigation, Guidance, Control and Dynamics","Robotics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/808"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In the modern world, various missions are being carried out under the assistance of autonomous flight vehicles due to their ability to operate in a wide range of flight conditions. Regardless, these autonomous vehicles are prone to GPS signal loss in urban environments due to obstructions that cause scintillation, multi-path, and shadowing. These effects that decrease the GPS functionality can deteriorate the accuracy of GPS positioning causing losses in signal tracking leading to a decrease in navigation performance. These effects are modeled into the simulation environment and are used as part of the path planning algorithm to provide better navigation strategies. This thesis aims to provide an implementation of A* algorithm in combination with RRT* path planning algorithm to detect and avoid areas with degraded GPS signals. The trajectory generation will consider a quadcopter vehicle dynamics when generating paths. A model of the quadcopter is used to illustrate the validation of this approach in a simulation environment with the GPS model integrated.
"]},{"key":"dc:title","label":"Title","values":["Implementation Of Path Planning Methods To Detect And Avoid GPS Signal Degradation In Urban Environments"]}]}],"canonical_facts":{"dc:creator":["Raminedi, Ayush"],"dc:description.abstract":["In the modern world, various missions are being carried out under the assistance of autonomous flight vehicles due to their ability to operate in a wide range of flight conditions. Regardless, these autonomous vehicles are prone to GPS signal loss in urban environments due to obstructions that cause scintillation, multi-path, and shadowing. These effects that decrease the GPS functionality can deteriorate the accuracy of GPS positioning causing losses in signal tracking leading to a decrease in navigation performance. These effects are modeled into the simulation environment and are used as part of the path planning algorithm to provide better navigation strategies. This thesis aims to provide an implementation of A* algorithm in combination with RRT* path planning algorithm to detect and avoid areas with degraded GPS signals. The trajectory generation will consider a quadcopter vehicle dynamics when generating paths. A model of the quadcopter is used to illustrate the validation of this approach in a simulation environment with the GPS model integrated.
"],"dc:identifier":["https://commons.erau.edu/edt/808"],"dc:subject":["RRT*","A*","DOP","Simulation Environment","Quadcopter","Monte Carlo","Urban Environments","Occupancy Map","Multi-Path Effects","NLDI","UAV","Aeronautical Vehicles","Aviation Safety and Security","Navigation, Guidance, Control and Dynamics","Robotics"],"dc:title":["Implementation Of Path Planning Methods To Detect And Avoid GPS Signal Degradation In Urban Environments"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Aerospace Engineering"]},"updated_at":"2026-07-27T19:25:10Z"}