Distributed management and coordination of UAV swarms based on infrastructureless wireless networks

[ES] Los Vehículos Aéreos no Tripulados (o drones) ya han demostrado su utilidad en una gran variedad de aplicaciones. Hoy en día, se utilizan para fotografía, cinematografía, inspecciones y vigilancia, entre otros. Sin embargo, en la mayoría de los casos todavía son controlados por un piloto, que como máximo suele estar volando un solo dron cada vez. En esta tesis, tratamos de avanzar en paso más allá en esta tecnología al permitir que múltiples drones con capacidad para despegue y aterrizaje vertical trabajen de forma sincronizada, como una sola entidad. La principal ventaja de realizar vuelos en grupo, comúnmente denominado enjambre, es que se pueden realizar tareas más complejas que utilizando un solo dron. De hecho, un enjambre permite cubrir más área en el mismo tiempo, ser más resistente, tener una capacidad de carga más alta, etc. Esto puede habilitar el uso de nuevas aplicaciones, o una mejor eficiencia para las aplicaciones existentes. Sin embargo, una parte clave es que los miembros del enjambre deben organizarse correctamente, ya que, durante el vuelo, diferentes perturbaciones pueden provocar que sea complicado mantener el enjambre como una unidad coherente. Una vez que se pierde esta coherencia, todos los beneficios previamente mencionados de un enjambre se pierden también. Incluso, aumenta el riesgo de colisiones entre los elementos del enjambre. Por lo tanto, esta tesis se centra en resolver algunos de estos problemas, proporcionando un conjunto de algoritmos que permitan a otros desarrolladores crear aplicaciones de enjambres de drones. Para desarrollar los algoritmos propuestos hemos incorporado mejoras al llamado ArduSim. Este simulador nos permite simular tanto la física de un dron como la comunicación entre drones con un alto grado de precisión. ArduSim nos permite implementar protocolos y algoritmos (bien probados) en drones reales con facilidad. Durante toda la tesis, ArduSim ha sido utilizado ampliamente. Su utilización ha permitido que las pruebas fueran seguras, y al mismo tiempo nos permitió ahorrar mucho tiempo, dinero y esfuerzo de investigación. Comenzamos nuestra investigación sobre enjambres asignando posiciones aéreas para cada dron en el suelo. Suponiendo que los drones están ubicados aleatoriamente en el suelo, y que necesitan alcanzar una formación aérea deseada, buscamos una solución que minimice la distancia total recorrida por todos los drones. Para ello se empezó con un método de fuerza bruta, pero rápidamente nos dimos cuenta de que, dada su alta complejidad, este método funciona mal cuando el número de drones aumenta. Por lo tanto, propusimos una heurística. Como en todas las heurísticas, se realizó un compromiso entre complejidad y precisión. Al simplificar el problema, encontramos que nuestra heurística era capaz de calcular una solución muy rápidamente sin aumentar sustancialmente la distancia total recorrida. Además, implementamos el algoritmo de Kuhn-Munkres (KMA), un algoritmo que ha demostrado proporcionar la respuesta exacta (es decir, reducir la distancia total recorrida) en el menor tiempo posible. Después de muchos experimentos, llegamos a la conclusión de que nuestra heurística es más rápida, pero que la solución proporcionada por el KMA es ligeramente más eficiente. En particular, aunque la diferencia en la distancia total recorrida es pequeña, el uso de KMA reduce el número de trayectorias de vuelo que se cruzan entre sí, lo cual es una métrica importante para las siguientes propuestas.[...][CA] Els vehicles aeris no tripulats (o drons) ja han demostrat la seua utilitat en una gran varietat d'aplicacions. Avui dia, s'utilitzen per a fotografia, cinematografia, inspeccions i vigilància, entre altres. No obstant això, en la majoria dels casos encara són controlats per un pilot, que com a màxim sol controlar el vol d'un sol dron cada vegada. En aquesta tesi, tractem d'avançar un pas més enllà en aquesta tecnologia, en permetre que múltiples drons amb capacitat per a l'enlairament i l'aterratge vertical treballen de forma sincronitzada, com una sola entitat. El principal avantatge de realitzar vols en grup, comunament denominats eixam, és que es poden fer tasques més complexes que utilitzant un sol dron. De fet, un eixam permet cobrir més àrea en el mateix temps, ser més resistent, tenir una capacitat de càrrega més alta, etc. Això pot habilitar l'ús de noves aplicacions, o una millor eficiència per a les aplicacions existents. No obstant això, una punt clau és que els membres de l'eixam han d'organitzar-se correctament, ja que, durant el vol, diferents pertorbacions poden provocar que siga complicat mantenir l'eixam com una unitat coherent. Una vegada que es perd aquesta coherència, tots els beneficis prèviament esmentats d'un eixam es perden també. Fins i tot, augmenta el risc de col·lisions entre els elements de l'eixam. Per tant, aquesta tesi se centra a resoldre alguns d'aquests problemes, proporcionant un conjunt d'algorismes que permeten a altres desenvolupadors crear aplicacions d'eixams de drons. Per a desenvolupar els algorismes proposats hem incorporat millores a l'anomenat ArduSim. Aquest simulador ens permet simular tant la física d'un dron com la comunicació entre drons amb un alt grau de precisió. ArduSim ens permet implementar protocols i algorismes (ben provats) en drons reals amb facilitat. Durant tota la tesi, ArduSim s'ha utilitzat àmpliament. El seu ús ha permès que les proves foren segures, i al mateix temps ens va permetre estalviar molt de temps, diners i esforç d'investigació. Per tant, es va utilitzar ArduSim per a cada bloc de construcció que vam desenvolupar. Comencem la nostra recerca sobre eixams assignant posicions aèries per a cada dron en terra. Suposant que els drons estan situats aleatòriament en terra i que necessiten assolir la formació aèria desitjada, cerquem una solució que minimitze la distància total recorreguda per tots els drons. Per a això, es va començar amb un mètode de força bruta, però ràpidament ens vam adonar que, atesa l'alta complexitat, aquest mètode funciona malament quan el nombre de drons augmenta. Per tant, vam proposar una heurística. Com en totes les heurístiques, es va fer un compromís entre complexitat i precisió. En simplificar el problema, trobem que la nostra heurística era capaç de calcular una solució molt ràpidament sense augmentar substancialment la distància total recorreguda. A més, vam implementar l'algorisme de Kuhn-Munkres (KMA), un algorisme que ha demostrat proporcionar la resposta exacta (és a dir, reduir la distància total recorreguda) en el menor temps possible. Després de molts experiments, arribem a la conclusió que la nostra heurística és més ràpida, però que la solució proporcionada pel KMA és lleugerament més eficient. En particular, encara que la diferència en la distància total recorreguda és xicoteta, l'ús de KMA redueix el nombre de trajectòries de vol que s'encreuen entre si, la qual cosa és una mètrica important per a les propostes següents.[...][EN] Unmanned Aerial Vehicles (UAVs) have already proven to be useful in many different applications. Nowadays, they are used for photography, cinematography, inspections, and surveillance. However, in most cases they are still controlled by a pilot, who at most is flying one UAV at a time. In this thesis, we try to take this technology one step further by allowing multiple Vertical Take-off and Landing (VTOL) UAVs to work together as one entity. The main advantage of this group, commonly referred to as a swarm, is that it can perform more complex tasks than a single UAV. When organized correctly, a swarm allows for: more area to be covered in the same time, more resilience, higher load capability, etc. A swarm can lead to new applications, or a better efficiency for existing applications. A key part, however, is that they should be organized correctly. During the flight, different disturbances will make it complicated to keep the swarm as one coherent unit. Once this coherency is lost, all the previously mentioned benefits of a swarm are lost as well. Even worse, the chance of a hazard increases. Therefore, this thesis focuses on solving some of these issues by providing a baseline of building blocks that enable other developers to create UAV swarm applications. In order to develop these building blocks, we improve a multi-UAV simulator called ArduSim. This simulator allows us to simulate both the physics of a UAV, and the communication between UAVs with a high degree of accuracy. This is a crucial part because it allows us to deploy (well tested) protocols and algorithms on real UAVs with ease. During the entirety of this thesis, ArduSim has been used extensively. It made testing safe, and allowed us to save a lot of time, money and research effort. We started by assigning airborne positions for each UAV on the ground. Assuming that the UAVs, are placed randomly on the ground, and that they need to reach a desired aerial formation, we searched for a solution that minimizes the total distance travelled by all the UAVs. We started with a brute-force method, but quickly realized that, given its high complexity, this method performs badly when the number of UAVs grows. Hence, we created a heuristic. As for all heuristics, a trade-off was made between complexity and accuracy. By simplifying the problem, we found that our heuristic was able to calculate a solution very quickly without increasing the total distance travelled substantially. Furthermore, we implemented the \ac{KMA}, an algorithm that has been proven to provide the exact answer (i.e. minimal total distance travelled) in the shortest time possible. After many experiments, we came to the conclusion that our heuristic is faster, but that the solution provided by the \ac{KMA} is slightly better. In particular, although the difference in total distance travelled is small, the \ac{KMA} reduces the numbers of flight paths crossing each other, which is an important metric in our next building block. Once we developed algorithms to assign airborne positions to each UAV on the ground, we started developing algorithms to take off all those UAVs. The objective of these algorithms is to reduce the time it takes for all the UAVs to reach their aerial position, while ensuring that all UAVs maintain a safe distance. The easiest solution is a sequential take-off procedure, but this is also the slowest approach. Hence, we improved it by first proposing a semi-sequential and later a semi-simultaneous take-off procedure. With this semi-simultaneous take-off procedure, we are able to reduce the takeoff time drastically without introducing any risk to the aircraft. [..]Wubben, J. (2023). Distributed management and coordination of UAV swarms based on infrastructureless wireless networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19888

A Solution for the Efficient Takeoff and Flight Coordination of UAV Swarms

[ES] En la última década, hemos asistido a un gran aumento del uso de los VANTs, debido principalmente a los avances en tecnología y materiales. Hoy en día, los VANTs ya no son solo juguetes para el entretenimiento, sino también importantes activos para muchas empresas. Los VANTs son muy versátiles y, por ello, existen muchas y variadas aplicaciones: misiones de búsqueda y rescate, vigilancia de fronteras, inspección térmica de tuberías, cinematografía y agricultura de precisión, solo por nombrar algunas. En estos momentos en que las industrias están incorporando soluciones basadas en VANTs, es crucial que la investigación avance. El cambio más destacado (con respecto a los VANTs) que presenciaremos en esta década, es el despliegue de grupos de VANTs trabajando en colaboración para cumplir un objetivo superior. Estos grupos, también llamados enjambres de drones, permiten realizar tareas más complejas, de forma más eficiente, o con mayor redundancia. Sin embargo, existen retos inherentes al funcionamiento de un enjambre de VANTs. Debe existir una buena comunicación entre los VANTs, deben evitarse las colisiones y los VANTs individuales deben utilizarse de forma inteligente para aumentar la eficiencia global. En este trabajo fin de master se da solución a algunos de los principales problemas relativos a los enjambres de vehículos aéreos no tripulados. En primer lugar, diseñamos varios patrones de formación de enjambres ´útiles. A continuación, incorporamos esas formaciones en dos procedimientos de despegue - una heurística y un algoritmo ya existente (KMA) - los cuales se prueban ampliamente para decidir cual es el más adecuado para despegar un enjambre de VANTs de la manera más eficiente. Una vez que somos capaces de despegar de forma sincronizada y segura un enjambre completo, continuamos nuestra investigación proporcionando una solución para mantener ese enjambre organizado, y estable durante una misión pre-planificada. Nuestra solución incorpora mecanismos para proporcionar resiliencia al enjambre, de tal manera que todos y cada uno de los VANTs pueden abandonar el enjambre (en pleno vuelo), sin perturbar a los demás en su misión.[EN] In the last decade, we have seen a great increase in the use of Unmanned Aerial Vehicles (UAVs). This is mainly due to advances in technology and materials. Nowadays, UAVs are no longer only toys for entertainment, but also important assets for many enterprises. UAVs are versatile, and thus many diverse applications exist: search and rescue missions, border surveillance, thermal pipeline inspection, cinematography, and precision agriculture, just to name a few. Now that the industry is incorporating UAVs based solutions, it is crucial that research advances. The most prominent change (with respect to UAVs) that we will witness in this decade, is the deployment of groups of UAVs working collaboratively to fulfill a higher goal. Those groups, also called swarms, allow us to perform more complex tasks, more efficiently, or with more redundancy. However, there are inherent challenges while operating a swarm of UAVs: there must be a good communication channel between the UAVs, collisions must be avoided, and the individual UAVs should be used intelligently in order to increase the overall efficiency. In this master thesis, a solution is given for some of the main problems concerning Unmanned Aerial Vehicle (UAV) swarms. First, we lay out various useful swarm formation patterns. Then we incorporate those formations in two takeoff procedures - an heuristic and an existing algorithm (KuhnMunkres algorithm (KMA)) - which are extensively tested to decide which one is the most appropriate for the takeoff of a swarm of UAVs in the most efficient manner. Once we are able to take off an entire swarm, we continue our research by providing a solution to keep that swarm organized and stable during a pre-planned mission. Such solution incorporates mechanisms to provide resilience to the swarm in such a manner that any number of UAVs can be removed from the swarm (mid-flight) without disturbing the others in their mission.Wubben, J. (2021). A Solution for the Efficient Takeoff and Flight Coordination of UAV Swarms. Universitat Politècnica de València. http://hdl.handle.net/10251/172620TFG

Assessing the limits of centralized unmanned aerial vehicle conflict management in U-Space

There is an important growth of unmanned aerial vehicles (UAVs) performing planned missions in urban environments, which poses significant challenges to the research community. The possibility of collisions represents a critical challenge. UAVs can suffer collisions due to different causes external or internal to their flight plans. In this context, dynamic geo-fencing is a useful approach, whereby each UAV is able to provide a prediction of its future positions within a limited time. These predictions could be used to detect conflicts, allowing to dynamically modify the flight plans so as to avoid imminent collisions. In this work, a conflict detection algorithm/method is proposed, implemented and tested on a central server performing real-time conflict analysis for a large number of UAVs flying in the aerial space of a city (U-Space). The architecture assumes that UAVs send their future locations to a traffic controller. This controller compares the predicted positions of nearby vehicles to detect possible conflicts. The results of this work demonstrate the feasibility of the proposed conflict detection algorithm and its interest to improve the security and efficiency in U-Space environments. The server is able to track thousands of UAVs in real time with a conflict anticipation around 11 s.This work is derived from the following R&D projects: PID2021-122580NB-I00 and RTI2018-098156-B-C52, funded by MCIN/AEI/10.13039/501100011033, “ERDF A way of making Europe”, and SBPLY/19/180501/000159, funded by the Junta de Comunidades de Castilla-La Mancha (JCCM) and the EU through the European Regional Development Fund (ERDF-FEDER)

FFP: A Force Field Protocol for the tactical management of UAV conflicts

[EN] 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).This work is derived from R&D project PID2021-122580NB-I00, funded by MCIN/AEI/10.13039/501100011033 and ERDF A way of making Europe.Wubben, J.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P. (2023). FFP: A Force Field Protocol for the tactical management of UAV conflicts. Ad Hoc Networks. 140. https://doi.org/10.1016/j.adhoc.2022.10307814

Efficient and coordinated vertical takeoff of UAV swarms

[EN] 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.This work was partially supported by the Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018, Spain, under Grant RTI2018-096384-B-I00.Fabra Collado, FJ.; Wubben, J.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P. (2020). Efficient and coordinated vertical takeoff of UAV swarms. IEEE. 1-5. https://doi.org/10.1109/VTC2020-Spring48590.2020.9128488S1

Improving UAV Mission Quality and Safety through Topographic Awareness

[EN] 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 +/- 5 m.This work is derived from R&D projects RTI2018-096384-B-I00 and RTC2019-007159-5, funded by MCIN/AEI/10.13039/501100011033 and the European Regional Development Fund (ERDF) "A Way of Making Europe", and by the Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020, Spain, under the Grant AICO/2020/302.Wubben, J.; Morales, C.; Tavares De Araujo Cesariny Calafate, CM.; Hernández-Orallo, E.; Cano, J.; Manzoni, P. (2022). Improving UAV Mission Quality and Safety through Topographic Awareness. Drones. 6(3):1-16. https://doi.org/10.3390/drones60300741166

Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

[EN] Over the last few years, several researchers have been developing protocols and applications in order to autonomously land unmanned aerial vehicles (UAVs). 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 or the compact landing of UAV swarms. Therefore, in this work, a solution for high precision landing based on the use of ArUco markers is presented. In the proposed solution, a UAV equipped with a low-cost camera is able to detect ArUco markers sized 56×56 cm from an altitude of up to 30 m. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. The proposal was evaluated and validated using both the ArduSim simulation platform and real UAV flights. The results show an average offset of only 11 cm from the target position, which vastly improves the landing accuracy compared to the traditional GPS-based landing, which typically deviates from the intended target by 1 to 3 m.This work was funded by the Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018 , Spain, under Grant RTI2018-096384-B-I00.Wubben, J.; Fabra Collado, FJ.; Tavares De Araujo Cesariny Calafate, CM.; Krzeszowski, T.; Márquez Barja, JM.; Cano, J.; Manzoni, P. (2019). Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition. Electronics. 8(12):1-16. https://doi.org/10.3390/electronics8121532S116812Pan, X., Ma, D., Jin, L., & Jiang, Z. (2008). Vision-Based Approach Angle and Height Estimation for UAV Landing. 2008 Congress on Image and Signal Processing. doi:10.1109/cisp.2008.78Tang, D., Li, F., Shen, N., & Guo, S. (2011). UAV attitude and position estimation for vision-based landing. Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology. doi:10.1109/emeit.2011.6023131Gautam, A., Sujit, P. B., & Saripalli, S. (2014). A survey of autonomous landing techniques for UAVs. 2014 International Conference on Unmanned Aircraft Systems (ICUAS). doi:10.1109/icuas.2014.6842377Holybro Pixhawk 4 · PX4 v1.9.0 User Guidehttps://docs.px4.io/v1.9.0/en/flight_controller/pixhawk4.htmlGarrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F. J., & Medina-Carnicer, R. (2016). Generation of fiducial marker dictionaries using Mixed Integer Linear Programming. Pattern Recognition, 51, 481-491. doi:10.1016/j.patcog.2015.09.023Romero-Ramirez, F. J., Muñoz-Salinas, R., & Medina-Carnicer, R. (2018). Speeded up detection of squared fiducial markers. Image and Vision Computing, 76, 38-47. doi:10.1016/j.imavis.2018.05.004ArUco: Augmented reality library based on OpenCVhttps://sourceforge.net/projects/aruco/Jin, S., Zhang, J., Shen, L., & Li, T. (2016). On-board vision autonomous landing techniques for quadrotor: A survey. 2016 35th Chinese Control Conference (CCC). doi:10.1109/chicc.2016.7554984Chen, X., Phang, S. K., Shan, M., & Chen, B. M. (2016). System integration of a vision-guided UAV for autonomous landing on moving platform. 2016 12th IEEE International Conference on Control and Automation (ICCA). doi:10.1109/icca.2016.7505370Nowak, E., Gupta, K., & Najjaran, H. (2017). Development of a Plug-and-Play Infrared Landing System for Multirotor Unmanned Aerial Vehicles. 2017 14th Conference on Computer and Robot Vision (CRV). doi:10.1109/crv.2017.23Shaker, M., Smith, M. N. R., Yue, S., & Duckett, T. (2010). Vision-Based Landing of a Simulated Unmanned Aerial Vehicle with Fast Reinforcement Learning. 2010 International Conference on Emerging Security Technologies. doi:10.1109/est.2010.14Araar, O., Aouf, N., & Vitanov, I. (2016). Vision Based Autonomous Landing of Multirotor UAV on Moving Platform. Journal of Intelligent & Robotic Systems, 85(2), 369-384. doi:10.1007/s10846-016-0399-zPatruno, C., Nitti, M., Petitti, A., Stella, E., & D’Orazio, T. (2018). A Vision-Based Approach for Unmanned Aerial Vehicle Landing. Journal of Intelligent & Robotic Systems, 95(2), 645-664. doi:10.1007/s10846-018-0933-2Baca, T., Stepan, P., Spurny, V., Hert, D., Penicka, R., Saska, M., … Kumar, V. (2019). Autonomous landing on a moving vehicle with an unmanned aerial vehicle. Journal of Field Robotics, 36(5), 874-891. doi:10.1002/rob.21858De Souza, J. P. C., Marcato, A. L. M., de Aguiar, E. P., Jucá, M. A., & Teixeira, A. M. (2019). 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Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

Over the last few years, several researchers have been developing protocols and applications in order to autonomously land unmanned aerial vehicles (UAVs). 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 or the compact landing of UAV swarms. Therefore, in this work, a solution for high precision landing based on the use of ArUco markers is presented. In the proposed solution, a UAV equipped with a low-cost camera is able to detect ArUco markers sized 56 &times; 56 cm from an altitude of up to 30 m. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. The proposal was evaluated and validated using both the ArduSim simulation platform and real UAV flights. The results show an average offset of only 11 cm from the target position, which vastly improves the landing accuracy compared to the traditional GPS-based landing, which typically deviates from the intended target by 1 to 3 m

Safe and Efficient Take-Off of VTOL UAV Swarms

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

Improving UAV Mission Quality and Safety through Topographic Awareness

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 ±5 m