Due to the increasing popularity of UAVs, and UAV applications, the need for accurate simulation tools is now greater than ever. Simulations allow for a fast, cheap, and most importantly, safe way to test new applications and protocols. However, due to their complexity, most of the existing UAV simulators only allow simulating either the UAV physics or their communication with a high accuracy. Yet, real applications require a tool that can simulate both. Hence, in this paper, we present a real time framework where our multi-UAV simulator ArduSim, and the popular network simulator OMNeT++, are combined to achieve an advanced co-simulation tool. We show that this co-simulation approach allows for a high accuracy in terms of both UAV physics, and communication between the UAVs, factors that have a clear impact on the performance of different protocols and applications that rely on UAV-to-UAV communications. Validation experiments show that the proposed co-simulation framework is able to perform adequately in real time under moderate workloads, even in standard desktop PCs.

Climate change is one of the main problems that humanity is facing, and reducing air pollution is one of the actions that must be taken to fight it. Before we can reduce it, we must be able to accurately measure the current air pollution and identify the contamination sources. In this work, we propose the use of unmanned aerial vehicles (UAVs) to measure the air pollution and identify the highest contaminated areas (i.e. the pollution source). We developed an automatic multicopter guidance system that is capable of obtaining pollution data from an area in an efficient, autonomous, and precise way. Our guidance algorithms move a UAV based on the data that it measures at each location, in such a way that the UAV spends most of the time flying/measuring in areas where the pollution is higher. Hence, we are able to precisely map the contamination sources while reducing flight time by up to 85% compared to a full sweep of the scenario.

In recent years, we have seen a tremendous growth in the adoption of Unmanned Aerial Vehicles (UAVs). Nowadays, UAVs are used in many different industries such as agriculture, inspection (bridges, pipelines, etc.), parcel delivery, etc. In the near future, this will lead to a substantial increase of aircraft in our airspace, especially in urban areas. Many existing collision avoidance approaches rely on heavy and/or expensive sensors, which limits its use for real UAVs due to increased costs, weight and complexity. Hence, to address this problem, in this paper we present a solution for the tactical management (i.e. in-flight) of UAV conflicts outdoors that introduces minimal requirements: a wireless interface and a GPS module. Specifically, we provide a collision avoidance algorithm based on artificial potential fields to provide flight safety. Our solution, called Force Field Protocol (FFP), allows the UAVs to autonomously detect each other using wireless communications, and to maintain a safe distance between them without the intervention of any central service. Experiments performed in our multi-UAV simulator ArduSim show that, with our approach, collisions between two UAVs are completely avoided in a wide set of scenarios, while introducing low disturbances to the original flight plans. Specifically, in the scenarios that we tested, the additional flight time introduced will be only 7 s longer in the worst case; in addition, it is able to improve upon previous approaches by reducing flight time by up to 54 s. We have shown experimentally that our approach can be scaled easily up to 100 UAVs, and that the probability of a collision is very low (< 0.06) despite flying in a small area (2.5 km × 2.5 km).

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.

In recent years, the adoption of unmanned aerial vehicles (UAVs) has widely spread to different sectors worldwide. Technological advances in this field have made it possible to coordinate the flight of these aircraft so as to conform a swarm. A UAV swarm is defined as a group of UAVs working collaboratively to carry out more complex missions or perform tasks more efficiently. Common applications of these swarms include rescue missions, precision agriculture, and border control, among others. However, there are still certain problems that prevent us from ensuring the success of their mission, especially as the number of drones in a swarm increases. In this paper, we specifically address the problem of a swarm take-off by optimizing the total time involved, while guaranteeing the safety of the UAVs during the take-off stage. To this end, we propose a new approach that combines a collision detection algorithm based on trajectory analysis with a batch generation mechanism that we use in order to determine the take-off sequence. Experiments show that our algorithm offers an efficient solution, managing to improve the performance of existing take-off techniques.

The use of UAVs has been growing steadily over the last years. Now that even the industry is adopting them for a wide range of activities, it can be said with certainty that UAVs will become an important asset for many enterprises. We foresee that, due to affordable prices, applications with groups of UAVs, also called swarms, will become mainstream. Swarms of UAVs can perform tasks faster and/or with more redundancy, and other tasks are only possible by collaborative work of UAVs. However, there are still many challenges to be solved before swarms of UAVs can be used safely. One of the challenges is the takeoff; i.e., takeoff should be safe (no collisions) and fast at the same time. An important part of the takeoff is the assignment task; i.e., determining which UAV goes where. In this work we will compare the effectiveness of three assignment algorithms, in terms of total distance travelled, number of flight paths crossing, and calculation time. We specially focus on irregular patterns. Our results show that the Kuhn-Munkres Algorithm (KMA) is preferable in almost all cases. It ensures that the total distance travelled by all UAVs is minimal, and most importantly it reduces the number of flight paths crossing each other (i.e. potential collisions). This is a very important metric because it allows for fast (semi) simultaneous takeoff procedures, which are not possible if the chances of collision are high.

Unmanned Aerial vehicles (UAVs) have gained a lot of interest over the last years due to the many fields of potential application. Nowadays, researchers are becoming interested in groups of UAVs working together. The collaborations between UAVs open a wide field of opportunities, because they are typically able to do more sophisticated tasks than a single UAV. However, collaboration between multiple UAVs is still a complex task, and significant challenges need to be addressed before their mainstream adoption. For instance, the automatic reconfiguration of a swarm can be used to adapt the swarm to changing application demands to solve a task in a more efficient and effective manner. However, the chances of collision become high if reconfiguration is not carefully planned. In this work we propose an approach to allow changing the shape of a UAV formation during flight through a computational inexpensive method that is able to decrease collision chances significantly. During the experiments we tested different reconfiguration events that are prone to collisions. Results have shown that our approach maintains a safe distance (greater than 5 meters) between the UAVs, while keeping the time overhead limited to a few tenths of a second. Furthermore, scalability tests have proven that our approach can handle the reconfiguration of at least 25 UAVs simultaneously.

As we experience an unprecedented growth in the field of Unmanned Aerial Vehicles (UAVs), more and more applications keep arising due to the combination of low cost and flexibility provided by these flying devices, especially those of the multirrotor type. Within this field, solutions where several UAVs team-up to create a swarm are gaining momentum as they enable to perform more sophisticated tasks, or accelerate task execution compared to the single-UAV alternative. However, advanced solutions based on UAV swarms still lack significant advancements and validation in real environments to facilitate their adoption and deployment. In this paper we take a step ahead in this direction by proposing a solution that improves the resilience of swarm flights, focusing on handling the loss of the swarm leader, which is typically the most critical condition to be faced. Experiments using our UAV emulation tool (ArduSim) evidence the correctness of the protocol under adverse circumstances, and highlight that swarm members are able to seamlessly switch to an alternative leader when necessary, introducing a negligible delay in the process in most cases, while keeping this delay within a few seconds even in worst-case conditions.

As we witness the unrelenting growth of the UAV sector, novel and more sophisticated applications keep emerging every year, with many more in the horizon. Among these, applications that require the adoption of UAV swarms are among the most complex, as deploying swarms requires the interaction and cooperation of all the UAVs involved, which can become quite challenging. In this work we specifically focus on the swarm takeoff procedure for UAVs of the Vertical Take-Off and Landing (VTOL) type, proposing a heuristic that achieves reduced computing overhead while introducing near-optimal assignments of UAV positions in the swarm formation selected. Such heuristic is complemented by an efficient and collision-free takeoff approach that relies on adequate ordering and inter-UAV communications to achieve a sequential phased takeoff. A large number of experiments using our own ArduSim emulation platform, which is totally compatible with real drone code, evidence the improvements achieved in terms of time overhead and safety when compared to both ideal and agnostic approaches.

Over the last few years, different researchers have been developing protocols and applications in order to land unmanned aerial vehicles (UAVs) autonomously. However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications, such as package retrieval and delivery. Therefore, in this paper, we present a solution for high precision landing based on the use of ArUco markers. In our solution, a UAV equipped with a camera is able to detect ArUco markers from an altitude of 20 meters. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. We evaluated our proposal using our own UAV simulation platform (ArduSim), and validated it using real UAVs. The results show an average offset of only 11 centimeters, which vastly improves the landing accuracy compared to the traditional GPS-based landing, that typically deviates from the intended target by 1 to 3 meters.