Autonomous Flightworthiness Determination for Modular Vertical Lift Vehicles

Abstract

Payload transportation via connected modular unmanned aerial vehicles is an emerging new area that offers unique advantages over other forms of aerial logistics. When considering rigidly attached, modular, vertical lift, unmanned aerial vehicles, 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. Onground flightworthiness determination can be used to ensure acceptable performance during vehicle takeoff or to prescribe changes to the vehicle attachment geometry if necessary. This paper introduces an algorithm to determine flightworthiness while in partial ground contact by estimating the vehicle attachment positions and payload weight. The algorithm uses 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.