Is this project an undergraduate, graduate, or faculty project?
Undergraduate
group
What campus are you from?
Daytona Beach
Authors' Class Standing
Daniel Golan, Junior Bryan Gonzales, Senior Kyle Fox, Senior Adam Duke, Senior Ryan Ebrahimi, Senior Ryan Taylor, Senior
Lead Presenter's Name
Daniel Golan
Faculty Mentor Name
Sergey Drakunov
Abstract
Using micro–Unmanned Aerial Vehicles (UAVs) or Micro Aerial Vehicles (MAVs) for mapping and cartography applications has the potential to change the way UAVs and MAVs are used, especially in treacherous and GPS-denied locations. Mapping in the past would take place with a person using various tools to map or survey any given area, but there has been a fundamental switch to autonomous vehicles that can accomplish the task in less time and with less effort. Autonomous mapping practices are typically completed using an individual UAV that maps using Simultaneous Localization and Mapping (SLAM) or photogrammetry. This project aims to use swarm robotics to map complex environments and harsh terrain using MAVs with a quicker, more accurate, and more precise method. The scope of the mapping procedure will cover difficult-to-reach locations such as cliffs, abandoned buildings, and forests. It also covers areas that would take too long for a surveyor to do by hand, such as construction sites and large indoor spaces like warehouses, factories, or historical buildings that cannot be modified for surveying. The swarm will utilize an emergent-like behavior to map in any given location without collisions of MAVs or ground objects.
Did this research project receive funding support from the Office of Undergraduate Research.
Yes, Collaborative Grant
Swarm Micro UAVs for Area Mapping in GPS-denied Areas
Using micro–Unmanned Aerial Vehicles (UAVs) or Micro Aerial Vehicles (MAVs) for mapping and cartography applications has the potential to change the way UAVs and MAVs are used, especially in treacherous and GPS-denied locations. Mapping in the past would take place with a person using various tools to map or survey any given area, but there has been a fundamental switch to autonomous vehicles that can accomplish the task in less time and with less effort. Autonomous mapping practices are typically completed using an individual UAV that maps using Simultaneous Localization and Mapping (SLAM) or photogrammetry. This project aims to use swarm robotics to map complex environments and harsh terrain using MAVs with a quicker, more accurate, and more precise method. The scope of the mapping procedure will cover difficult-to-reach locations such as cliffs, abandoned buildings, and forests. It also covers areas that would take too long for a surveyor to do by hand, such as construction sites and large indoor spaces like warehouses, factories, or historical buildings that cannot be modified for surveying. The swarm will utilize an emergent-like behavior to map in any given location without collisions of MAVs or ground objects.