Is this project an undergraduate, graduate, or faculty project?
Undergraduate
group
What campus are you from?
Daytona Beach
Authors' Class Standing
Emily Suh, Senior Marco Fagetti, Senior
Lead Presenter's Name
Marco Fagetti
Faculty Mentor Name
Morad Nazari
Abstract
Geometric mechanics is a dynamical formalism that allows for simultaneous treatment of rotational and translational motion without the drawbacks of attitude parameterization sets. While geometric mechanics is well suited to deal with full six degree-of-freedom motion or significant position-attitude coupling, this formalism has yet to be extensively applied to hardware systems. The broader research goals of this work aim to prove the practical viability of this theoretical framework by applying it to a class of Crazyflie drones, which are frequently used to assess Guidance, Navigation, and Control schemes. To efficiently achieve these goals, a reliable, collapsible drone cage is required to conduct such experiments in. As a result, the team has designed and constructed a modular cage that can be used to safely test drone behavior. Requirements from the drones’ suite of hardware necessitate a cage with dimensions of 3m x 3m x 7m, a fact which drove the collapsible nature of the design. Given the cage’s modularity, its size can be further scaled for future experiments. The work here discusses the construction and development methodology of the cage, and preliminary results for a path-tracking simulation illustrate how the cage and Crazyflie hardware interface to provide accurate truth-data.
Did this research project receive funding support from the Office of Undergraduate Research.
Yes, Spark Grant
Drone Cage Design and Implementation to Enable Small Drone Architecture Testing
Geometric mechanics is a dynamical formalism that allows for simultaneous treatment of rotational and translational motion without the drawbacks of attitude parameterization sets. While geometric mechanics is well suited to deal with full six degree-of-freedom motion or significant position-attitude coupling, this formalism has yet to be extensively applied to hardware systems. The broader research goals of this work aim to prove the practical viability of this theoretical framework by applying it to a class of Crazyflie drones, which are frequently used to assess Guidance, Navigation, and Control schemes. To efficiently achieve these goals, a reliable, collapsible drone cage is required to conduct such experiments in. As a result, the team has designed and constructed a modular cage that can be used to safely test drone behavior. Requirements from the drones’ suite of hardware necessitate a cage with dimensions of 3m x 3m x 7m, a fact which drove the collapsible nature of the design. Given the cage’s modularity, its size can be further scaled for future experiments. The work here discusses the construction and development methodology of the cage, and preliminary results for a path-tracking simulation illustrate how the cage and Crazyflie hardware interface to provide accurate truth-data.