{"id":49041,"date":"2023-04-13T10:33:10","date_gmt":"2023-04-13T14:33:10","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/simcenter\/?p=49041"},"modified":"2026-03-26T06:47:53","modified_gmt":"2026-03-26T10:47:53","slug":"vv-co-simulation-autonomous-uav","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/simcenter\/vv-co-simulation-autonomous-uav\/","title":{"rendered":"A new V&V co-simulation framework for autonomous UAV!"},"content":{"rendered":"\n

In May 2020, a commercial drone test flight was conducted near Riga International Airport in Latvia. During the test flight, the company lost communications with the drone and track of its location.
This 26 kg drone had enough fuel to stay in the air for 90 hours. It led to severe consequences, such as the closure of airspace over an international airport.1<\/sup> While in Arizona, a military drone of more than $1M got lost, which caused heavy financial repercussions.2<\/sup><\/p>\n\n\n\n

Unmanned aerial vehicles (UAVs) are meant to perform different kinds of missions, which is one of the key aspects of drones. This broad spectrum of applications requires drone manufacturers to develop a modular platform adaptable to mission needs. Hence the need to customize both hardware and software components, specifically the guidance, navigation and control (GNC) algorithm.<\/p>\n\n\n\n

During the verification and validation (V&V), one shall ensure the software meets the specifications and requirements to fulfill its intended purpose. Testing GNC on a real drone can be time-consuming and expensive. The alternative to reducing the cost is to use a simulation tool to conduct a preliminary virtual test campaign.<\/p>\n\n\n\n

<\/div>\n\n\n
\n
\"Real
Figure 1. Physical versus virtual testing.<\/figcaption><\/figure><\/div>\n\n\n
<\/div>\n\n\n\n

Co-simulation framework for V&V<\/h2>\n\n\n\n

Simulation models must provide high-fidelity results while CPU time remains acceptable (enough for real-time execution) to bring added value. The plant model of the drone, its environmental perception as well as the controller shall be simulated.<\/p>\n\n\n\n

Let\u2019s begin with the plant model: since the drone encompasses various physical domains such as mechanical (equation of motion, propulsion), electrical (motor, battery) and control, it falls into the category of complex multi-physical system. Consequently, a tool handling multi-physics is required to model those different subsystems.<\/p>\n\n\n\n

<\/div>\n\n\n
\n
\"Drone
Figure 2. Drone physical domains.<\/figcaption><\/figure><\/div>\n\n\n
<\/div>\n\n\n\n

Simcenter Amesim is dedicated to the modeling and simulation of dynamic and multi-physics systems. The tool enables to achieve performance analysis as well as performing virtual system integration. Another key benefit is the scalability modeling approach, since the model accuracy increases as the design matures (i.e., tabulated approach or analytical equation). Furthermore, Simcenter Amesim is an open mechatronic simulation platform with various interfaces with Functional Mock-Up Interface (FMI) and MATLAB Simulink. In addition to the real-time applications support, software in the loop (SIL) and hardware in the loop (HIL) simulation can be performed.<\/p>\n\n\n\n

<\/div>\n\n\n
\n
\"Quadcopter
Figure 3. Quadcopter plant model in Simcenter Amesim with post-processing tools.<\/figcaption><\/figure><\/div>\n\n\n
<\/div>\n\n\n\n

To perform autonomous missions, the drone must correctly perceive the environment. Hence the modeling of both the 3D environment and drone\u2019s sensors via Simcenter Prescan. This tool is dedicated to modeling the vehicle\u2019s surrounding environment (road, building, pedestrian, other vehicles) and vehicle\u2019s sensor simulation (several levels of fidelity from ideal to physics-based). Changing the lighting and weather conditions (sun position, rain, snow, fog) can be achieved via a simple click.<\/p>\n\n\n\n

<\/div>\n\n\n
\n
\"Environment
Figure 4. Environment creation, sensor object configuration and animation in Simcenter Prescan.<\/figcaption><\/figure><\/div>\n\n\n
<\/div>\n\n\n\n

The objective is now to couple the drone plant model (UAV subsystem performances), the environment perception model and the GNC algorithm together. Three variants have been developed; the difference is based on how to connect the GNC algorithm:<\/p>\n\n\n\n