Payload transportation via connected modular unmanned aerial vehicles (UAVs) is an emerging new area that offers unique advantages over other forms of aerial transportation. When considering vertical lift UAVs, differing payloads and rotor attachment geometries have a significant effect on the vehicle dynamic response during takeoff and stabilization. With no prior knowledge of payload parameters or rotor 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 the vehicle take-off or to prescribe changes to the rotor attachment geometry if necessary. This paper introduces an algorithm to determine flightworthiness while in ground contact by exploiting the relationship between the equivalent thrust vector and the mass center location. The algorithm utilizes a probabilistic estimate of rotor location derived through a Bayesian learning technique to maximize the amount of information gained from a subsequent deterministic rotor classification algorithm. Following a description of the algorithm, simulation results are presented to illustrate the performance of the algorithm for a variety of modular aircraft configurations.
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