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

SwarmCity project: monitoring traffic, pedestrians, climate, and pollution with an aerial robotic swarm: Data collection and fusion in a smart city, and its representation using virtual reality

Smart cities have emerged as a strategy to solve problems that current cities face, such as traffic, security, resource management, waste, and pollution. Most of the current approaches are based on deploying large numbers of sensors throughout the city and have some limitations to get relevant and updated data. In this paper, as an extension of our previous investigations, we propose a robotic swarm to collect the data of traffic, pedestrians, climate, and pollution. This data is sent to a base station, where it is treated to generate maps and presented in an immersive interface. To validate these developments, we use a virtual city called SwarmCity with models of traffic, pedestrians, climate, and pollution based on real data. The whole system has been tested with several subjects to assess whether the information collected by the drones, processed in the base station, and represented in the virtual reality interface is appropriate. Results show that the complete solution, i.e., fleet control, data fusion, and operator interface, allows monitoring the relevant variables in the simulated city. © 2020, Springer-Verlag London Ltd., part of Springer Nature.This work is part of the “SwarmCity project: monitoring future cities with intelligent flying swarms,” developed by the Robotics and Cybernetics Research Group of the Centre for Automation and Robotics (UPM-CSIC).Peer reviewe