SUBJECT: Ph.D. Dissertation Defense
BY: Jonathan Warner
TIME: Wednesday, September 19, 2018, 1:00 p.m.
PLACE: MRDC Building, 4404
TITLE: Autonomous Flightworthiness Determination for Modular Lift Vehicles
COMMITTEE: Dr. Jonathan Rogers, Chair (ME)
Dr. Jun Ueda (ME)
Dr. Al Ferri (ME)
Dr. Patricio Vela (ECE)
Dr. Eric Johnson (AE)


Payload transportation via connected modular unmanned aerial vehicles (UAVs) is an emerging new area that offers unique advantages over other forms of aerial logistics. When considering rigidly attached modular vertical lift UAVs, differing payloads and vehicle attachment geometries have a significant effect on the composite aircraft’s dynamic response during takeoff and stabilization. With no prior knowledge of payload parameters or vehicle attachment geometry, there is no inherent flightworthiness guarantee for a specific connected configuration. On-ground flightworthiness determination can be used to ensure acceptable performance during vehicle take-off or to prescribe changes to the vehicle attachment geometry if necessary. This work introduces an algorithm to determine flightworthiness while in partial ground contact by estimating the vehicle attachment positions and payload weight. The algorithm utilizes a probabilistic estimate of vehicle placement about the payload derived through a Bayesian learning technique to generate the necessary data to deterministically estimate the attached vehicles’ positions. Following a description of the algorithm, simulation results are presented to illustrate the performance of the algorithm for a variety of modular aircraft configurations. The algorithm is experimentally validated through a series of tests using prototype modular vehicles.