Automated pre-flight and in-flight testing of aerial vehicles by machine learning

Abstract

Data captured during evolutions performed by aerial vehicles prior to one or more missions, and data regarding outcomes of the missions, may be used to train a machine learning system to predict data regarding an outcome of a mission of an aerial vehicle based on the performance of the aerial vehicle during one or more evolutions. The data may be captured by sensors provided aboard an aerial vehicle, or in association with a testing facility, and may include data captured during both pre-flight and/or in-flight evolutions performed by the aerial vehicle. The evolutions may include any pre-flight operation of motors, propellers and/or control surfaces, or any other components, as well as the in-flight operation of such components. If a machine learning system determines that a mission is unlikely to succeed, the mission may be canceled, delayed until further inspections may be performed, or assigned to another aerial vehicle.