Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Daniel Golan, Junior Bryan Gonzalez, Graduate Ethan Thomas, Junior Patrick Kennedy, Senior Ryan Taylor, Senior Kyle Fox, Senior Joseph Perry, Freshman Om Acharaya, Freshman
Lead Presenter's Name
Daniel Golan
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
Sergey Drakunov
Abstract
Utilizing small Unmanned Aerial Systems (SUAS) in mapping and cartography applications holds transformative potential, particularly in challenging and GPS-denied environments. Traditionally, mapping involved manual efforts using diverse tools, but there has been a fundamental shift towards autonomous vehicles capable of achieving efficient results in less time and with reduced human effort. Autonomous mapping typically relies on single a UAV employing Simultaneous Localization and Mapping (SLAM) or photogrammetry alongside GPS. This research project seeks to leverage swarm robotics to map intricate landscapes and rugged terrains using SUASs with a faster, more accurate, and precise approach, eliminating dependence on GPS for global positioning. The mapping scope encompasses hard-to-access locations like cliffs, abandoned structures, and forests, as well as areas impractical for manual surveying, such as construction sites and expansive indoor spaces like warehouses, factories, or historical buildings resistant to modifications for survey purposes. The swarm will exhibit an emergent-like behavior to map any location efficiently, ensuring collision-free navigation among sUAS and ground objects.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Ignite Grant
Swarm UAVs for Area Mapping in GPS-denied Locations
Utilizing small Unmanned Aerial Systems (SUAS) in mapping and cartography applications holds transformative potential, particularly in challenging and GPS-denied environments. Traditionally, mapping involved manual efforts using diverse tools, but there has been a fundamental shift towards autonomous vehicles capable of achieving efficient results in less time and with reduced human effort. Autonomous mapping typically relies on single a UAV employing Simultaneous Localization and Mapping (SLAM) or photogrammetry alongside GPS. This research project seeks to leverage swarm robotics to map intricate landscapes and rugged terrains using SUASs with a faster, more accurate, and precise approach, eliminating dependence on GPS for global positioning. The mapping scope encompasses hard-to-access locations like cliffs, abandoned structures, and forests, as well as areas impractical for manual surveying, such as construction sites and expansive indoor spaces like warehouses, factories, or historical buildings resistant to modifications for survey purposes. The swarm will exhibit an emergent-like behavior to map any location efficiently, ensuring collision-free navigation among sUAS and ground objects.