The development of skills related to computer programming and robotics and the introduction of computational thinking principles in high schools are worldwide trends today. An effective way of initiating young students in this world consists in proposing them stimulating challenges. This work presents a robotic platform that has been successfully used to develop a competition (called Drone Challenge) in which students had to program the navigation system for a simulated unmanned aerial vehicle (or drone). Both the competition and the supporting platform are described in detail. In particular, the article provides a deep technical description of the main components of the platform, namely the drone simulator and the navigation development framework. The results of the survey conducted after the challenge point to the suitability of the working platform deployed.

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.

This paper presents the simulation environment for the EIDOS (equipment destined for orientation and safety) architecture applied to wildfire fighting operations. The development of this environment involves a multidisciplinary study, in which we have to handle several different types of information (geographical data, vegetation models, fire models, etc.) and devices (sensors, aerial vehicles, and mobile devices). Therefore, the simulation tool is actually composed of several independent and interconnected modules.

Predictably, future urban airspaces will be crowded with autonomous unmanned aerial vehicles (UAVs) offering different services to the population. One of the main challenges in this new scenario is the design of collision-free navigation algorithms to avoid conflicts between flying UAVs. The most appropriate collision avoidance strategies for this scenario are non-centralized ones that are dynamically executed (in real time). Existing collision avoidance methods usually entail a high computational cost. In this work, we present Bounding Box Collision Avoidance (BBCA) algorithm, a simplified velocity obstacle-based technique that achieves a balance between efficiency and cost. The performance of the proposal is analyzed in detail in different airspace configurations. Simulation results show that the method is able to avoid all the conflicts in two UAV scenarios and most of them in multi-UAV ones. At the same time, we have found that the penalty of using the BBCA collision avoidance technique on the flying time and the distance covered by the UAVs involved in the conflict is reasonably acceptable. Therefore, we consider that BBCA may be an excellent candidate for the design of collision-free navigation algorithms for UAVs.