In the last decade, the popularity of UAVs, colloquially known as drones, has increased tremendously. Nowadays, drones are used in a wide range of use-cases, such as precision agriculture, surveillance, and photography. Many of these use-cases can be made more efficient if multiple UAVs are used cooperatively (i.e., in a swarm). To achieve this, communication between the UAVs is paramount. To ensure communication, many works rely on the existing infrastructure (e.g., 4G). However, in many rural areas, this infrastructure does not exist. In those cases, an ad hoc (Wi-Fi) network is the most adequate alternative. Yet, due to the limited communication range of Wi-Fi, it is not possible to let UAVs in a swarm to communicate over a long distance. To solve this issue a relay approach is necessary. Despite general solutions to relay messages between (mobile) nodes already exist, many UAV swarms rely on master–slave communication. Thus, a specific solution for this type of communication might be more efficient. Hence, in this work, we propose a strategy to efficiently relay messages for UAV swarms adopting the master–slave communication paradigm. Our approach seeks to introduce a very small message overhead to avoid congestion of the network, and to provide more bandwidth for the actual applications of the UAV swarm. We tested our approach using a realistic UAV simulator called ArduSim. Our results show that our approach is capable of detecting all the nodes in the network within a few seconds. Furthermore, we applied our message relay approach on an existing swarm application (where a swarm of UAVs had to follow a mission), and our results show that, now, the communication range of the UAVs can be much larger, without impacting other aspects of the mission (such as flight time).

Currently multicopters are facing a continuous growth in terms of recreational uses, and multiple companies focused on these aircrafts to facilitate certain tasks that were nearly inaccessible to humans, or otherwise involved a great cost. In this context, the drone swarm concept allows us to broaden and incorporate new, more refined applications in which various aircraft coordinate with each other to carry out large-scale tasks. When the number of UAVs involved becomes too high, guaranteeing that the take-off procedure is efficient and yet secure becomes quite complex. Hence, in this paper we propose and validate different algorithms to optimize the take-off time of drones belonging to a swarm, with the objective that there are no collisions between them. In particular, we propose algorithms for both trajectory analysis and batch generation for take-off. Based on a large number of experiments using the ArduSim simulator we prove that the proposed algorithms provide a robust solution within a reasonable time frame when testing with different aerial formations. In addition, we will assess how different UAV position assignment strategies impact our algorithm performance in terms of take-off time and number of batches required.

In the last decade, the popularity of UAVs has increased tremendously. Nowadays, many researchers are interested in UAV swarms. Coordinating a swarm of UAVs is a complicated task and many problems should be addressed before wide-spread adoption. In this work, we focus on the take-off for large-scale UAV swarms, with an extra focus on the assignment phase. The assignment phase is the first take-off stage whereby we decide which UAV on the ground goes to which place in the air. A good assignment algorithm, is quick, and at the same time reduce the total distance travelled as much as possible. We assess the performance of three different assignment algorithms: a heuristic, the original Kuhn-Munkres algorithm (KMA), and the KMA adapted for GPU use. Each algorithm was tested while varying the number of UAVs, as well as the type of flight formation. During the experiments, we measured the calculation time, total distance travelled, and number of flight paths crossing. In terms of total distance travelled, the KMA always outperforms the heuristic. However, the KMA takes longer (orders of magnitude) to calculate the assignment. Realistically, the KMA algorithm can only be used as long as the swarm does not contain more than 500 UAVs. From that point the GPU version of the KMA is faster. We can conclude that, in most cases, it is recommendable to use the KMA for the assignment as it will reduce the distance travelled to a minimum and, consequently, also reduce the number of flight paths crossing.

The field of Unmanned Aerial Vehicles (UAVs) has progressed greatly in the last years. UAVs are now used for many applications and are often flown automatically. One commonly implemented feature in an automatic flight is that of following a mission at a stable altitude. However, this altitude is almost always referenced from the take-off location and does not take terrain profile levels into account. This is a critical and dangerous issue because if the terrain level changes abruptly (e.g., mountain regions or buildings in a city), this can lead to crashes or an unintended (illegal) high altitude. Our aim for this work is to provide a solution such that a constant altitude above ground level is maintained. To this end, we make use of the readily available Digital Elevation Models (DEMs). These models, which contain the terrain elevation, help us in dynamically adjusting the VTOL UAV altitude so that it remains nearly constant in relation to the ground. Results have shown that with the use of our method, the altitude can be maintained sufficiently constant while introducing a limited increase in flight time and battery consumption that is proportional to the terrain’s irregularity. In a moderately changing terrain, the error could be reduced to just $\pm 5$ m.