Optimizing the Layout of Run-of-River Powerplants Using Cubic Hermite Splines and Genetic Algorithms

Despite the clear advantages of mini hydropower technology to provide energy access in remote areas of developing countries, the lack of resources and technical training in these contexts usually lead to suboptimal installations that do not exploit the full potential of the environment. To address this drawback, the present work proposes a novel method to optimize the design of mini-hydropower plants with a robust and efficient formulation. The approach does not involve typical 2D simplifications of the terrain penstock layout. On the contrary, the problem is formulated considering arbitrary three-dimensional terrain profiles and realistic penstock layouts taking into account the bending effect. To this end, the plant layout is modeled on a continuous basis through the cubic Hermite interpolation of a set of key points, and the optimization problem is addressed using a genetic algorithm with tailored generation, mutation and crossover operators, especially designed to improve both the exploration and intensification. The approach is successfully applied to a real-case scenario with real topographic data, demonstrating its capability of providing optimal solutions while dealing with arbitrary terrain topography. Finally, a comparison with a previous discrete approach demonstrated that this algorithm can lead to a noticeable cost reduction for the problem studied

Mission-based mobility models for UAV networks

Las redes UAV han atraído la atención de los investigadores durante la última década. Las numerosas posibilidades que ofrecen los sistemas single-UAV aumentan considerablemente al usar múltiples UAV. Sin embargo, el gran potencial del sistema multi-UAV viene con un precio: la complejidad de controlar todos los aspectos necesarios para garantizar que los UAVs cumplen la misión que se les ha asignado. Ha habido numerosas investigaciones dedicadas a los sistemas multi-UAV en el campo de la robótica en las cuales se han utilizado grupos de UAVs para diferentes aplicaciones. Sin embargo, los aspectos relacionados con la red que forman estos sistemas han comenzado a reclamar un lugar entre la comunidad de investigación y han hecho que las redes de UAVs se consideren como un nuevo paradigma entre las redes multi-salto. La investigación de redes de UAVs, de manera similar a otras redes multi-salto, se divide principalmente en dos categorías: i) modelos de movilidad que capturan la movilidad de la red, y ii) algoritmos de enrutamiento. Ambas categorías han heredado muchos algoritmos que pertenecían a las redes MANET, que fueron el primer paradigma de redes multi-salto que atrajo la atención de los investigadores. Aunque hay esfuerzos de investigación en curso que proponen soluciones para ambas categorías, el número de modelos de movilidad y algoritmos de enrutamiento específicos para redes UAV es limitado. Además, en el caso de los modelos de movilidad, las soluciones existentes propuestas son simplistas y apenas representan la movilidad real de un equipo de UAVs, los cuales se utilizan principalmente en operaciones orientadas a misiones, en la que cada UAV tiene asignados movimientos específicos. Esta tesis propone dos modelos de movilidad basados en misiones para una red de UAVs que realiza dos operaciones diferentes. El escenario elegido en el que se desarrollan las misiones corresponde con una región en la que ha ocurrido, por ejemplo, un desastre natural. La elección de este tipo de escenario se debe a que en zonas de desastre, la infraestructura de comunicaciones comúnmente está dañada o totalmente destruida. En este tipo de situaciones, una red de UAVs ofrece la posibilidad de desplegar rápidamente una red de comunicaciones. El primer modelo de movilidad, llamado dPSO-U, ha sido diseñado para capturar la movilidad de una red UAV en una misión con dos objetivos principales: i) explorar el área del escenario para descubrir las ubicaciones de los nodos terrestres, y ii) hacer que los UAVs converjan de manera autónoma a los grupos en los que se organizan los nodos terrestres (también conocidos como clusters). El modelo de movilidad dPSO-U se basa en el conocido algoritmo particle swarm optimization (PSO), considerando los UAV como las partículas del algoritmo, y también utilizando el concepto de valores dinámicos para la inercia, el local best y el neighbour best de manera que el modelo de movilidad tenga ambas capacidades: la de exploración y la de convergencia. El segundo modelo, denominado modelo de movilidad Jaccard-based, captura la movilidad de una red UAV que tiene asignada la misión de proporcionar servicios de comunicación inalámbrica en un escenario de mediano tamaño. En este modelo de movilidad se ha utilizado una combinación del virtual forces algorithm (VFA), de la distancia Jaccard entre cada par de UAVs y metaheurísticas como hill climbing y simulated annealing, para cumplir los dos objetivos de la misión: i) maximizar el número de nodos terrestres (víctimas) que se encuentran bajo el área de cobertura inalámbrica de la red UAV, y ii) mantener la red UAV como una red conectada, es decir, evitando las desconexiones entre UAV. Se han realizado simulaciones exhaustivas con herramientas software específicamente desarrolladas para los modelos de movilidad propuestos. También se ha definido un conjunto de métricas para cada modelo de movilidad. Estas métricas se han utilizado para validar la capacidad de los modelos de movilidad propuestos de emular los movimientos de una red UAV en cada misión.UAV networks have attracted the attention of the research community in the last decade. The numerous capabilities of single-UAV systems increase considerably by using multiple UAVs. The great potential of a multi-UAV system comes with a price though: the complexity of controlling all the aspects required to guarantee that the UAV team accomplish the mission that it has been assigned. There have been numerous research works devoted to multi-UAV systems in the field of robotics using UAV teams for different applications. However, the networking aspects of multi-UAV systems started to claim a place among the research community and have made UAV networks to be considered as a new paradigm among the multihop ad hoc networks. UAV networks research, in a similar manner to other multihop ad hoc networks, is mainly divided into two categories: i) mobility models that capture the network mobility, and ii) routing algorithms. Both categories have inherited previous algorithms mechanisms that originally belong to MANETs, being these the first multihop networking paradigm attracting the attention of researchers. Although there are ongoing research efforts proposing solutions for the aforementioned categories, the number of UAV networks-specific mobility models and routing algorithms is limited. In addition, in the case of the mobility models, the existing solutions proposed are simplistic and barely represent the real mobility of a UAV team, which are mainly used in missions-oriented operations. This thesis proposes two mission-based mobility models for a UAV network carrying out two different operations over a disaster-like scenario. The reason for selecting a disaster scenario is because, usually, the common communication infrastructure is malfunctioning or completely destroyed. In these cases, a UAV network allows building a support communication network which is rapidly deployed. The first mobility model, called dPSO-U, has been designed for capturing the mobility of a UAV network in a mission with two main objectives: i) exploring the scenario area for discovering the location of ground nodes, and ii) making the UAVs to autonomously converge to the groups in which the nodes are organized (also referred to as clusters). The dPSO-U mobility model is based on the well-known particle swarm optimization algorithm (PSO), considering the UAVs as the particles of the algorithm, and also using the concept of dynamic inertia, local best and neighbour best weights so the mobility model can have both abilities: exploration and convergence. The second one, called Jaccard-based mobility model, captures the mobility of a UAV network that has been assigned with the mission of providing wireless communication services in a medium-scale scenario. A combination of the virtual forces algorithm (VFA), the Jaccard distance between each pair of UAVs and metaheuristics such as hill climbing or simulated annealing have been used in this mobility model in order to meet the two mission objectives: i) to maximize the number of ground nodes (i.e. victims) under the UAV network wireless coverage area, and ii) to maintain the UAV network as a connected network, i.e. avoiding UAV disconnections. Extensive simulations have been performed with software tools that have been specifically developed for the proposed mobility models. Also, a set of metrics have been defined and measured for each mobility model. These metrics have been used for validating the ability of the proposed mobility models to emulate the movements of a UAV network in each mission

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects

Practical applications using multi-UAV systems and aerial robotic swarms

[EN] Nowadays, there are a large number of unmanned aircraft on the market that can be commanded with high-level orders to perform complex tasks almost automatically, such as mapping crop fields. We can ask ourselves if it would be possible to coordinate a group of these robots to perform those same tasks more quickly, flexibly and robustly. In this work, we summarize the tasks that have been studied to be solved with systems composed by groups of unmanned aircraft and the algorithms used, as well as the methods and strategies on which they are based. Although the future of these systems is promising, there are certain legislative and technical obstacles that stop their implementation in a generalized way.[ES] A día de hoy, existen en el mercado una gran cantidad de aeronaves sin piloto que pueden ser comandadas con ordenes de alto nivel para realizar tareas complejas de forma casi automatica, como por ejemplo el mapeo de explotaciones agrícolas. De forma natural, nos podemos preguntar si sería posible coordinar a un grupo de estos robots para realizar esas mismas tareas de forma más rápida, flexible y robusta. En este trabajo se repasan las tareas que se han planteado resolver con sistemas compuestos por grupos de aeronaves no tripuladas y los algoritmos empleados, así como los metodos y estrategias en los que están basados. Aunque el futuro de estos sistemas es prometedor, existen ciertos obstaculos legislativos y técnicos que frenan su implantación de forma generalizada.Las investigaciones que han dado como resultado este trabajo han sido financiadas por RoboCity2030-DIH-CM, 426 Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, financiadas por los Programas de Actividades I+D en la Comunidad Madrid, y por el proyecto TASAR (Team of Advanced Search And Rescue Robots), PID2019-105808RB-I00, financiado por el Ministerio de Ciencia e Innovacion (Gobierno de España).García-Aunon, P.; Roldán, J.; De León, J.; Del Cerro, J.; Barrientos, A. (2021). Aplicaciones practicas de los sistemas multi-UAV y enjambres aéreos. Revista Iberoamericana de Automática e Informática industrial. 18(3):230-241. https://doi.org/10.4995/riai.2020.13560OJS230241183Acevedo, J. J., Arrue, B. C., Maza, I., Ollero, A., 2013. Cooperative large area surveillance with a team of aerial mobile robots for long endurance missions. Journal of Intelligent & Robotic Systems 70 (1-4), 329-345. https://doi.org/10.1007/s10846-012-9716-3Albani, D., IJsselmuiden, J., Haken, R., Trianni, V., 2017. Monitoring and mapping with robot swarms for agricultural applications. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, pp. 1-6. https://doi.org/10.1109/AVSS.2017.8078478Alvear, O., Zema, N. R., Natalizio, E., Calafate, C. T., 2017. Using uav-based systems to monitor air pollution in areas with poor accessibility. Journal of Advanced Transportation 2017. https://doi.org/10.1155/2017/8204353Augugliaro, F., Lupashin, S., Hamer, M., Male, C., Hehn, M., Mueller, M. W., Willmann, J. S., Gramazio, F., Kohler, M., D'Andrea, R., 2014. The flight assembled architecture installation: Cooperative construction with flying machines. IEEE Control Systems Magazine 34 (4), 46-64. https://doi.org/10.1109/MCS.2014.2320359Barrientos, A., Colorado, J., Cerro, J. d., Martinez, A., Rossi, C., Sanz, D., Valente, J., 2011. Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots. Journal of Field Robotics 28 (5), 667-689. https://doi.org/10.1002/rob.20403Beck, Z., Teacy, W. L., Rogers, A., Jennings, N. R., 2018. Collaborative online planning for automated victim search in disaster response. Robotics and Autonomous Systems 100, 251-266. https://doi.org/10.1016/j.robot.2017.09.014Bennet, D. J., MacInnes, C., Suzuki, M., Uchiyama, K., 2011. Autonomous three-dimensional formation flight for a swarm of unmanned aerial vehicles. Journal of guidance, control, and dynamics 34 (6), 1899-1908. https://doi.org/10.2514/1.53931Bernard, M., Kondak, K., Maza, I., Ollero, A., 2011. Autonomous transportation and deployment with aerial robots for search and rescue missions. Journal of Field Robotics 28 (6), 914-931. https://doi.org/10.1002/rob.20401Carrasco, Á. M., Novoa, S. C., Al-Kaff, A., Fernández, F. G., Gómez, D. M., de la Escalera Hueso, A., 2020. Vehículo aéreo no tripulado para vigilancia y monitorización de incendios. Revista Iberoamericana de Automática e Informática industrial.Chen, S., Li, C., Zhuo, S., 2017. A distributed coverage algorithm for multiuav with average voronoi partition. In: 2017 17th International Conference on Control, Automation and Systems (ICCAS). IEEE, pp. 1083-1086. https://doi.org/10.23919/ICCAS.2017.8204377Cieslewski, T., Choudhary, S., Scaramuzza, D., 2018. Data-efficient decentralized visual slam. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 2466-2473. https://doi.org/10.1109/ICRA.2018.8461155Cimino, M. G., Lazzeri, A., Vaglini, G., 2015. Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search. In: 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, pp. 1-6. https://doi.org/10.1109/IISA.2015.7387990Cledat, E., Cucci, D., 2017. Mapping gnss restricted environments with a drone tandem and indirect position control. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4, 1. https://doi.org/10.5194/isprs-annals-IV-2-W3-1-2017Cole, D. T., Thompson, P., Göktogan, A. H., Sukkarieh, S., 2010. System development and demonstration of a cooperative uav team for mapping and tracking. The International Journal of Robotics Research 29 (11), 1371-1399. https://doi.org/10.1177/0278364910364685Darrah, M., Trujillo, M. M., Speransky, K., Wathen, M., 2017. Optimized 3d mapping of a large area with structures using multiple multirotors. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, pp. 716-722. https://doi.org/10.1109/ICUAS.2017.7991414Erignac, C., 2007. An exhaustive swarming search strategy based on distributed pheromone maps. In: AIAA Infotech@ Aerospace 2007 Conference and Exhibit. p. 2822. https://doi.org/10.2514/6.2007-2822Fu, Z., Chen, Y., Ding, Y., He, D., 2019. Pollution source localization based on multi-uav cooperative communication. IEEE Access 7, 29304-29312. https://doi.org/10.1109/ACCESS.2019.2900475Fujisawa, R., Imamura, H., Hashimoto, T., Matsuno, F., 2008. Communication using pheromone field for multiple robots. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 1391-1396. https://doi.org/10.1109/IROS.2008.4650971Garcia-Aunon, P., Barrientos, A., 2018a. Comparison of heuristic algorithms in discrete search and surveillance tasks using aerial swarms. Applied Sciences 8 (5), 711. https://doi.org/10.3390/app8050711Garcia-Aunon, P., Barrientos, A., 2018b. Control optimization of an aerial robotic swarm in a search task and its adaptation to different scenarios. Journal of computational science 29, 107-118. https://doi.org/10.1016/j.jocs.2018.10.004Garcia-Aunon, P., del Cerro, J., Barrientos, A., 2019a. Behavior-based control for an aerial robotic swarm in surveillance missions. Sensors 19 (20), 4584. https://doi.org/10.3390/s19204584Garcia-Aunon, P., Roldan, J. J., Barrientos, A., 2019b. Monitoring traffic in future cities with aerial swarms: Developing and optimizing a behavior-based surveillance algorithm. Cognitive Systems Research 54, 273-286. https://doi.org/10.1016/j.cogsys.2018.10.031Garnier, S., Tache, F., Combe, M., Grimal, A., Theraulaz, G., 2007. Alice in pheromone land: An experimental setup for the study of ant-like robots. In: 2007 IEEE Swarm Intelligence Symposium. IEEE, pp. 37-44. https://doi.org/10.1109/SIS.2007.368024George, J., Sujit, P., Sousa, J. B., 2011. Search strategies for multiple uav search and destroy missions. Journal of Intelligent & Robotic Systems 61 (1-4), 355-367. https://doi.org/10.1007/s10846-010-9486-8Hadaegh, F. Y., Chung, S.-J., Manohara, H. M., 2014. On development of 100- gram-class spacecraft for swarm applications. IEEE Systems Journal 10 (2), 673-684. https://doi.org/10.1109/JSYST.2014.2327972Han, J., Xu, Y., Di, L., Chen, Y., 2013. Low-cost multi-uav technologies for contour mapping of nuclear radiation field. Journal of Intelligent & Robotic Systems 70 (1-4), 401-410. https://doi.org/10.1007/s10846-012-9722-5Hauert, S., Winkler, L., Zufferey, J.-C., Floreano, D., 2008. Ant-based swarming with positionless micro air vehicles for communication relay. Swarm Intelligence 2 (2-4), 167-188. https://doi.org/10.1007/s11721-008-0013-5Hinzmann, T., Stastny, T., Conte, G., Doherty, P., Rudol, P., Wzorek, M., Galceran, E., Siegwart, R., Gilitschenski, I., 2016. Collaborative 3d reconstruction using heterogeneous uavs: System and experiments. In: International Symposium on Experimental Robotics. Springer, pp. 43-56. https://doi.org/10.1007/978-3-319-50115-4_5Ju, C., Son, H., 2018. Multiple uav systems for agricultural applications: control, implementation, and evaluation. Electronics 7 (9), 162. https://doi.org/10.3390/electronics7090162Kim, J. H., Kwon, J.-W., Seo, J., 2014. Multi-uav-based stereo vision system without gps for ground obstacle mapping to assist path planning of ugv. Electronics Letters 50 (20), 1431-1432. https://doi.org/10.1049/el.2014.2227Lanillos, P., Gan, S. K., Besada-Portas, E., Pajares, G., Sukkarieh, S., 2014. Multi-uav target search using decentralized gradient-based negotiation with expected observation. Information Sciences 282, 92-110. https://doi.org/10.1016/j.ins.2014.05.054Li, W., 2015. Persistent surveillance for a swarm of micro aerial vehicles by flocking algorithm. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 229 (1), 185-194. https://doi.org/10.1177/0954410014529100Lyu, Y., Pan, Q., Zhang, Y., Zhao, C., Zhu, H., Tang, T., Liu, L., 2015. Simultaneously multi-uav mapping and control with visual servoing. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, pp. 125-131. https://doi.org/10.1109/ICUAS.2015.7152283Mahdoui, N., Frémont, V., Natalizio, E., 2017. Cooperative exploration strategy for micro-aerial vehicles fleet. In: 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, pp. 180-185. https://doi.org/10.1109/MFI.2017.8170426Maza, I., Ollero, A., 2007. Multiple uav cooperative searching operation using polygon area decomposition and efficient coverage algorithms. In: Distributed Autonomous Robotic Systems 6. Springer, pp. 221-230. https://doi.org/10.1007/978-4-431-35873-2_22Mirjan, A., Gramazio, F., Kohler, M., Augugliaro, F., D'Andrea, R., 2013. Architectural fabrication of tensile structures with flying machines. Green Design, Materials and Manufacturing Processes, 513-518. https://doi.org/10.1201/b15002-99Niedzielski, T., Jurecka, M., Mizinski, B., Remisz, J., Slopek, J., Spallek, W., Witek-Kasprzak, M., Kasprzak, Ł., Swierczynska-Chlasciak, M., 2018. A real-time field experiment on search and rescue operations assisted by unmanned aerial vehicles. Journal of Field Robotics 35 (6), 906-920. https://doi.org/10.1002/rob.21784Nigam, N., Bieniawski, S., Kroo, I., Vian, J., 2011. Control of multiple uavs for persistent surveillance: algorithm and flight test results. IEEE Transactions on Control Systems Technology 20 (5), 1236-1251. https://doi.org/10.1109/TCST.2011.2167331Odonkor, P., Ball, Z., Chowdhury, S., 2019. Distributed operation of collaborating unmanned aerial vehicles for time-sensitive oil spill mapping. Swarm and Evolutionary Computation 46, 52-68. https://doi.org/10.1016/j.swevo.2019.01.005Oh, S.-H., Suk, J., 2010. Evolutionary design of the controller for the search of area with obstacles using multiple uavs. In: ICCAS 2010. IEEE, pp. 2541- 2546. https://doi.org/10.1109/ICCAS.2010.5670230Perez-Carabaza, S., Besada-Portas, E., Lopez-Orozco, J. A., Jesus, M., 2018. Ant colony optimization for multi-uav minimum time search in uncertain domains. Applied Soft Computing 62, 789-806. https://doi.org/10.1016/j.asoc.2017.09.009Qu, Y., Zhang, Y., Zhang, Y., 2015. A uav solution of regional surveillance based on pheromones and artificial potential field theory. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, pp. 380-385. https://doi.org/10.1109/ICUAS.2015.7152313Rastgoftar, H., Atkins, E. M., 2018. Cooperative aerial lift and manipulation (calm). Aerospace Science and Technology 82, 105-118. https://doi.org/10.1016/j.ast.2018.09.005Reina, D., Tawfik, H., Toral, S., 2018. Multi-subpopulation evolutionary algorithms for coverage deployment of uav-networks. Ad Hoc Networks 68, 16-32. https://doi.org/10.1016/j.adhoc.2017.09.005Reuder, J., Jonassen, M. O., Olafsson, H., 2012. The small unmanned meteorological observer sumo: Recent developments and applications of a micro-uas for atmospheric boundary layer research. Acta Geophysica 60 (5), 1454- 1473. https://doi.org/10.2478/s11600-012-0042-8Reynolds, C. W., 1987. Flocks, herds and schools: A distributed behavioral model. Vol. 21. ACM. https://doi.org/10.1145/37402.37406Roldan, J. J., Garcia-Aunon, P., Peña-Tapia, E., Barrientos, A., 2019. Swarm-city project: Can an aerial swarm monitor traffic in a smart city? In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, pp. 862-867. https://doi.org/10.1109/PERCOMW.2019.8730677Santamaria, E., Segor, F., Tchouchenkov, I., 2013. Rapid aerial mapping with multiple heterogeneous unmanned vehicles. In: ISCRAM. Citeseer.Saska, M., Chudoba, J., Preucil, L., Thomas, J., Loianno, G., Tresnak, A., Vonasek, V., Kumar, V., 2014. Autonomous deployment of swarms of microaerial vehicles in cooperative surveillance. In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, pp. 584-595. https://doi.org/10.1109/ICUAS.2014.6842301Savkin, A. V., Huang, H., 2019. Asymptotically optimal deployment of drones for surveillance and monitoring. Sensors 19 (9), 2068. https://doi.org/10.3390/s19092068Schilling, F., Lecoeur, J., Schiano, F., Floreano, D., 2018. Learning visionbased cohesive flight in drone swarms. arXiv preprint arXiv:1809.00543.Schmuck, P., Chli, M., 2017. Multi-uav collaborative monocular slam. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 3863-3870. https://doi.org/10.1109/ICRA.2017.7989445Silic, M. B., Song, Z., Mohseni, K., 2018. Anisotropic flocking control of distributed multi-agent systems using fluid abstraction. In: 2018 AIAA Information Systems-AIAA Infotech@ Aerospace. p. 2262. https://doi.org/10.2514/6.2018-2262Sreenath, K., Kumar, V., 2013. Dynamics, control and planning for cooperative manipulation of payloads suspended by cables from multiple quadrotor robots. rn 1 (r2), r3. https://doi.org/10.15607/RSS.2013.IX.011St-Onge, D., Kaufmann, M., Panerati, J., Ramtoula, B., Cao, Y., Coffey, E. B., Beltrame, G., 2019. Planetary exploration with robot teams. IEEE Robotics & Automation Magazine.Stavros, E. N., Agha, A., Sirota, A., Quadrelli, M., Ebadi, K., Yun, K., 2019. Smoke sky-exploring new frontiers of unmanned aerial systems for wildland fire science and applications. arXiv preprint arXiv:1911.08288.Techy, L., Schmale III, D. G., Woolsey, C. A., 2010. Coordinated aerobiological sampling of a plant pathogen in the lower atmosphere using two autonomous unmanned aerial vehicles. Journal of Field Robotics 27 (3), 335-343. https://doi.org/10.1002/rob.20335Tuna, G., Nefzi, B., Conte, G., 2014. Unmanned aerial vehicle-aided communications system for disaster recovery. Journal of Network and Computer Applications 41, 27-36. https://doi.org/10.1016/j.jnca.2013.10.002Twidwell, D., Allen, C. R., Detweiler, C., Higgins, J., Laney, C., Elbaum, S., 2016. Smokey comes of age: unmanned aerial systems for fire management. Frontiers in Ecology and the Environment 14 (6), 333-339. https://doi.org/10.1002/fee.1299Vasarhelyi, G., Viragh, C., Somorjai, G., Tarcai, N., Szorenyi, T., Nepusz, T., Vicsek, T., 2014. Outdoor flocking and formation flight with autonomous aerial robots. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 3866-3873. https://doi.org/10.1109/IROS.2014.6943105Verdu, T., Hattenberger, G., Lacroix, S., 2019. Flight patterns for clouds exploration with a fleet of uavs. https://doi.org/10.1109/ICUAS.2019.8797953Waharte, S., Trigoni, N., 2010. Supporting search and rescue operations with uavs. In: 2010 International Conference on Emerging Security Technologies. IEEE, pp. 142-147. https://doi.org/10.1109/EST.2010.31Wang, Z., Singh, S., Pavone, M., Schwager, M., 2018. Cooperative object transport in 3d with multiple quadrotors using no peer communication. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1064-1071. https://doi.org/10.1109/ICRA.2018.8460742Zhao, N., Lu, W., Sheng, M., Chen, Y., Tang, J., Yu, F. R., Wong, K.-K., 2019. Uav-assisted emergency networks in disasters. IEEE Wireless Communications 26 (1), 45-51. https://doi.org/10.1109/MWC.2018.1800160Zheng, X., Wang, F., Li, Z., 2018. A multi-uav cooperative route planning methodology for 3d fine-resolution building model reconstruction. ISPRS journal of photogrammetry and remote sensing 146, 483-494. https://doi.org/10.1016/j.isprsjprs.2018.11.00