Abstract

Unmanned aerial vehicles (UAVs) are disrupting multiples industries such as logistic, agriculture, construction, etc. This broad spectrum of applications requires drone manufacturers to develop a modular platform adaptable to mission needs. This customization applies on the hardware components as well as on the software components and more specifically on the guidance, navigation and controls (GNC). The software development lifecycle goes through a series of stages, from requirement analysis to maintenance. Among these, the verification and validation (V&V) consists of checking that a software system meets requirements and specifications. Testing navigation algorithms with real UAV platforms is an expensive and time-consuming process. Using a simulation tool to test such algorithms provides an alternative to these problems. This paper presents the technique of using a system simulation tool to test control algorithms of an autonomous octocopter UAV, with four coaxial contra-rotating propellers, used in the frame of the European research project COMP4DRONES. The software Simcenter Amesim, a multi-physics system simulation tool, is used to model the different subsystems of the UAV: batteries, propulsion chain and flight dynamics. Those plant models have been favorably compared against experimental data provided by the manufacturers first. Next the overall performance model has been validated with respect to experimental data coming from flight tests. The overall plant model was then integrated in a co-simulation framework capable of modelling drone’s navigation sensors (camera, LIDAR, etc.), mission environment and GNC algorithms to simulate the drone’s behavior under different scenarios: precision landing maneuvers, obstacles avoidance and cluster flight. This framework enables UAV integrators to conduct exhaustive flight tests, easily change the environment by adding more obstacles, perform extreme tests and assess the impact on the drone stability in case of failure. Running GNC algorithms virtual validation ensure the drone behaves properly in multiple environments and conditions, and consequently improve the product performance.