Abstract
Unmanned aerial vehicles are gaining popularity in an ever-increasing range of applications, mainly because they are able to navigate autonomously. In this work, we describe a simulation framework that can help engineering students who are starting out in the field of aerial robotics to acquire the necessary competences and skills for the development of autonomous drone navigation systems. In our framework, drone behavior is defined in a graphical way, by means of very intuitive state machines, whereas low-level control details have been abstracted. We show how the framework can be used to develop a navigation system proposal according to the rules of the “ESII Drone Challenge” student competition. We also show how the proposal can be evaluated in different test scenarios.
Highlights
Unmanned aerial vehicles have become immensely popular over the last few years
A wide variety of techniques for autonomous drone navigation can be found in the literature, ranging from those based on computer vision systems [1] to those based on the use of laser or LiDAR sensors [2], as well as the use of GPS and IMUs, and different combinations of all these types of onboard equipment [3]
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Summary
Unmanned aerial vehicles (or drones) have become immensely popular over the last few years. MATLAB [6], Simulink, and Stateflow provide support for deploying flight control algorithms for commercial drones, from professional devices (such as “Pixhawk” autopilots [7]) to recreational toys (such as Parrot or DJI Ryze Tello minidrones). They integrate the well-known “model-based design” methodology [8], which has proven to be very appropriate for the design of cyber-physical systems. Electronics 2021, 10, 7 has proven to be very appropriate for the design of cyber-physical systems This methodology basically relies on graphical tools to develop a system model whose behavior is sriemlieuslaotnedgbraepfohriecaglotionoglsinttoodpervoedluopctiaonsy. In the following subse three framework subsystems and their interfaces with the navigation
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