Abstract
Unmanned Aerial Vehicles (UAVs) have multi-domain applications, fixed-wing UAVs being a widely used class. Despite the ongoing research on the topics of guidance and formation control of fixed-wing UAVs, little progress is known on implementation of semi-physical validation platforms (software-in-the-loop or hardware-in-the-loop) for such complex autonomous systems. A semi-physical simulation platform should capture not only the physical aspects of UAV dynamics, but also the cybernetics aspects such as the autopilot and the communication layers connecting the different components. Such a cyber-physical integration would allow validation of guidance and formation control algorithms in the presence of uncertainties, unmodelled dynamics, low-level control loops, communication protocols and unreliable communication: These aspects are often neglected in the design of guidance and formation control laws for fixed-wing UAVs. This paper describes the development of a semi-physical platform for multi-fixed wing UAVs where all the aforementioned points are carefully integrated. The environment adopts Raspberry Pi’s programmed in C++, which can be interfaced to standard autopilots (PX4) as a companion computer. Simulations are done in a distributed setting with a server program designed for the purpose of routing data between nodes, handling the user inputs and configurations of the UAVs. Gazebo-ROS is used as a 3D visualization tool.
Highlights
The availability of low-cost sensors, electronics, and air-frames has promoted a significant interest in Unmanned Aerial Vehicles (UAVs) among aircraft hobbyists, academic researchers, and industries [1,2]
Simulations are done in a distributed setting with a server program designed for the purpose of handling the user inputs and configurations of the UAVs
The dynamics of a network of fixed-wing UAVs can be described in the Euler–Lagrange framework by
Summary
The availability of low-cost sensors, electronics, and air-frames has promoted a significant interest in Unmanned Aerial Vehicles (UAVs) among aircraft hobbyists, academic researchers, and industries [1,2]. Most designs for the guidance laws are based on considering simplified first-order course dynamics [17,18,19,20]; the UAV course dynamics are the result of the low-level control layer (e.g., roll and pitch control loops) implemented in the autopilot layer on board of any UAV [21]. Experience, the lack of a realistic simulation platform for fixed-wing UAVs prevents assessing the following key points: The actual performance of guidance methods considerably depends on the fidelity of the UAV model used for design. As the guidance algorithm cannot always sit in the autopilot (due to intensive computation requirements), it should be implemented on extra hardware companion computer Such extra hardware should be embedded with wireless communication capabilities: a proper server program handling the data exchange for UAV formation algorithms should be put in place.
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