A Novel Co-simulation Framework for Verification and Validation of GNC Algorithms for Autonomous UAV

Abstract

The use of autonomous Unmanned Aerial Vehicles (UAVs) in commercial applications has the potential to disrupt several industries. To effectively cover the broad spectrum of possible applications, UAV integrators require the ability to develop drone platforms that meet the requirements specified for the missions to accomplish. This is not limited to the correct sizing of the UAV physical subsystems, but also on the Guidance, Navigation, and Control (GNC) algorithms that enable the UAV to operate autonomously in an efficient way. System simulation can provide a valuable support to both activities. In the predesign phase, it allows exploring the design space, analyzing the performance of multiple UAV variants, and selecting the most promising concepts. Once this phase is completed, the resulting dynamic and multi-physics performance model of the UAV presents a sufficient fidelity to support the continuous development of GNC algorithms. Thanks to a novel co-simulation framework proposed in this paper, the performance model can then be integrated in a co-simulation framework to include perception sensors, mission environment, and GNC algorithms. Two use cases based on the framework are presented and discussed.