REFERENCES
Aguilar, W., Casaliglla, V., and Pólit, J., Obstacle avoidance based-visual navigation for micro aerial vehicles, Electronics, 2017, vol. 6, no. 1, pp. 1–23.
Al-Kaff, A., Meng, Q., Martín, D., de la Escalera, A., and Armingol, J.M., Monocular vision-based obstacle detection/avoidance for unmanned aerial vehicles, Proc. Intelligent Vehicles Symp., Gotenburg, 2016, pp. 92–97.
Anis, H., Indra Fadhillah, A.H., Darma, S., and Soekirno, S., Automatic quadcopter control avoiding obstacle using camera with integrated ultrasonic sensor, J. Phys.: Conf. Ser., 2018, vol. 1011, pp. 1–6.
Balamurugan, G., Valarmathi, J., and Naidu, V., Survey on UAV navigation in GPS denied environments, Proc. Int. Conf. on Signal Processing, Communication, Power and Embedded System, Paralakhemundi, 2016, pp. 198–204.
Behak, S., Rondán, G., Zanetti, M., Iturriaga, S., and Nesmachnow, S., Distributed greedy approach for autonomous surveillance using unmanned aerial vehicles, in High Performance Computing, Nesmachnow, S., Castro, H., and Tchernykh, A., Eds., Springer Int. Publ., 2021, pp. 130–145.
Bloesch, M., Burri, M., Omari, S., Hutter, M., and Siegwart, R., Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback, Int. J. Rob. Res., 2017, vol. 36, pp. 1053–1072.
Bobkov, V.A., Borisov, Yu.S., Inzartsev, A.V., and Mel’man, S.V., Simulation program complex for studying motion control methods for autonomous underwater vehicles, Program. Comput. Software, 2008, vol. 34, no. 5, pp. 257–266.
Bristeau, P., Callou, F., Vissière, D., and Petit, N., The navigation and control technology inside the AR.Drone micro UAV, Int. Fed. Autom. Control, 2011, vol. 44, no. 1, pp. 1477–1484.
Cacace, J., Finzi, A., and Lippiello, V., Multimodal Interaction with multiple co-located drones in search and rescue missions, Proc. Italian Workshop on Artificial Intelligence, Ferrara, 2015, pp. 54–67.
Cesare, K., Skeele, R., Yoo, S.-H., Zhang, Y., and Hollinger, G., Multi-UAV exploration with limited communication and battery, Proc. Int. Conf. on Robotics and Automation, Seattle, 2015, pp. 2230–2235.
Cui, J., Lai, S., Dong, X., and Chen, B., Autonomous navigation of UAV in foliage environment, Intellig. Rob. Syst., 2016, vol. 84, pp. 259–276.
Dalal, N. and Triggs, B., Histograms of oriented gradients for human detection, Proc. Computer Vision and Pattern Recognition, San Diego, 2005, pp. 886–893.
Díaz, S., Garate, B., Nesmachnow, S., and Iturriaga, S., Autonomous navigation of unmanned aerial vehicles using markers, Proc. Iberoamerican Congress on Smart Cities, 2020, pp. 1–15.
Deakin, M. and Al Waer, H., From intelligent to smart cities, Intell. Build. Int., 2011, vol. 3, pp. 133–139.
Ester, M., Kriegel, H., Sander, J., and Xu, X., A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise, Proc. Int. Conf. Knowledge Discovery and Data Mining, Portland, 1996, pp. 226–231.
Fortuna, J.M., Ramírez, M.T., Martínez, J., Murguía, J.S., and Mejía, M., Object detection in aerial navigation using wavelet transform and convolutional neural networks: a first approach, Program. Comput. Software, 2020, vol. 46, no. 8, pp. 536–547.
Garrido, S., Munoz, R., Madrid, F., and Marín, M., Automatic generation and detection of highly reliable fiducial markers under occlusion, Pattern Recogn., 2014, vol. 47, no. 6, pp. 2280–2292.
Gaudín, A., Madruga, G., Rodríguez, C., Iturriaga, S., Nesmachnow, S., Paz, C., Danoy, G., and Bouvry, P., Autonomous flight of unmanned aerial vehicles using evolutionary algorithms, in High Performance Computing, vol. 1087: Communications in Computer and Information Science, Springer, 2019, pp. 337–352.
Hart, P., Nilsson, N., and Raphael, B., A formal basis for the heuristic determination of minimum cost paths, IEEE Trans. Syst. Sci. Cybern., 1968, vol. 4, no. 2, pp. 100–107.
Hosang, J., Benenson, R., and Schiele, B., Learning non-maximum suppression, Proc. Conf. on Computer Vision and Pattern Recognition, Honolulu, 2017, pp. 6469–6477.
Jin, S., Zhang, J., Shen, L., and Li, T., Onboard vision autonomous landing techniques for quadrotor: a survey, Proc. Chinese Control Conf., Chengdu, 2016, pp. 10284–10289.
Ju, C. and Son, H., Multiple UAV systems for agricultural applications: control, implementation, and evaluation, Electronics, 2018, vol. 7, no. 9, pp. 1–19.
Kopeikin, A., Ponda, S., and Inalhan, G., Control of communication networks for teams of UAVs, in Handbook of Unmanned Aerial Vehicles, Springer Netherlands, 2014, pp. 1619–1654.
Lamport, L., Paxos made simple, ACM SIGACT News, 2001, vol. 32, no. 4, pp. 51–58.
Laufs, J., Borrion, H., and Bradford, B., Security and the smart city: a systematic review, Sust. Cities Soc., 2020, vol. 55, pp. 1–18.
Lucas, B. and Kanade, T., An iterative image registration technique with an application to stereo vision, Proc. 7th Int. Joint Conf. on Artificial Intelligence, Vancouver, 1981, pp. 674–679.
Matsumoto, Y., Ikeda, K., Inaba, M., and Inoue, H., Visual navigation using omnidirectional view sequence, Proc. Int. Conf. on Intelligent Robots and Systems, Kyongju, 1999, pp. 317–322.
Mori, T. and Scherer, S., First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles, Proc. Int. Conf. on Robotics and Automation, Karlsruhe, 2013, pp. 1750–1757.
Mufalli, F., Batta, R., and Nagi, R., Simultaneous sensor selection and routing of unmanned aerial vehicles for complex mission plans, Comput. Oper. Res., 2012, vol. 39, no. 11, pp. 2787–2799.
Muñoz-Salinas, R., Marín-Jimenez, M., Yeguas-Bolivar, E., and Medina-Carnicer, R., Mapping and localization from planar markers, Pattern Recogn., 2018, vol. 73, pp. 158–171.
Siva Ram Murthy, C. and Manoj, B.S., Ad Hoc Wireless Networks: Architectures and Protocols, Prentice Hall, 2004.
Nesmachnow, S., An overview of metaheuristics: accurate and efficient methods for optimisation, Int. J. Metaheuristics, 2014, vol. 3, no. 4, pp. 320–347.
Nesmachnow, S. and Iturriaga, S., Cluster-UY: collaborative scientific high performance computing in Uruguay, in Proc. Int. Conf. on Supercomputing in Mexico, Springer, 2019, vol. 1151, pp. 188–202.
Nitschke, C., Marker-based tracking with unmanned aerial vehicles, Proc. Robotics and Biomimetics Conf., Bali, 2014, pp. 1331–1338.
Oubbati, O., Atiquzzaman, M., Lorenz, P., Tareque, H., and Hossain, S., Routing in flying ad hoc networks: survey, constraints, and future challenge perspectives, IEEE Access, 2019, vol. 7, pp. 81057–81105.
Reynolds, C., Steering behaviors for autonomous characters, Proc. Game Developers Conf., 1999, pp. 763–782.
Rohmer, E., Singh, S.P.N., and Freese, M., V-REP: a versatile and scalable robot simulation framework, Proc. Int. Conf. on Intelligent Robots and Systems, Tokyo, 2013, pp. 1321–1326.
Romero-Ramirez, F., Muñoz-Salinas, R., and Medina-Carnicer, R., Speeded up detection of squared fiducial markers, Image Vision Comput., 2018, vol. 76, pp. 38–47.
Sagar, J. and Visser, A., Obstacle avoidance by combining background subtraction, optical flow and proximity estimation, Proc. Int. Micro Air Vehicle Conf. and Competition, Delft, 2014, pp. 142–149.
Shang, K., Karungaru, S., Feng, Z., Ke, L., and Terada, K., A GA-ACO hybrid algorithm for the multi-UAV mission planning problem, Proc. Int. Symp. on Communications and Information Technologies, Incheon, 2014, pp. 243–248.
Shen, S., Michael, N., and Kumar, V., Obtaining liftoff indoors: autonomous navigation in confined indoor environments, Rob. Autom. Mag., 2013, vol. 20, no. 4, pp. 40–48.
Singh, A., Patil, D., and Omkar, S., Eye in the sky: real-time drone surveillance system (DSS) for volent individuals identification using ScatterNet hybrid deep learning network, Proc. IEEE Computer Vision and Pattern Recognition Workshops, Salt Lake City, 2018, pp. 1629–1637.
Slovokhotov, Yu.L. and Neretin, I.S., Toward constructing a modular model of distributed intelligence, Program. Comput. Software, 2018, vol. 44, no. 6, pp. 499–507.
Sun, K., Mohta, K., Pfrommer, B., Watterson, M., Liu, S., Mulgaonkar, Y., Taylor, C.J., and Kumar, V., Robust stereo visual inertial odometry for fast autonomous flight, IEEE Rob. Autom. Lett., 2018, vol. 3, no. 2, pp. 965–972.
Supreeth, H. and Patil, C., Efficient multiple moving object detection and tracking using combined background subtraction and clustering, Signal, Image Video Process, 2018, vol. 12, no. 6, pp. 1097–1105.
U.S. Government, Official information about the Global Positioning System (GPS), 2021. https://www.gps.gov/systems/gps. Accessed Feb. 9, 2021.
Villasenor, J., Drones and the future of domestic aviation, Proc. IEEE, 2014, vol. 102, no. 3, pp. 235–238.
Wu, Z., Han, P., Yao, R., Qiao, L., Zhang, W., Shen, T., Sun, M., Zhu, Y., Lin, M., and Fan, R., Autonomous UAV landing system based on visual navigation, Proc. Int. Conf. on Imaging Systems and Techniques, Abu Dhabi, 2019, pp. 1–6.
Zeng, Y., Zhang, R., and Lim, T., Wireless communications with unmanned aerial vehicles: opportunities and challenges, Commun. Mag., 2016, vol. 54, no. 5, pp. 36–42.
Zivkovic, Z., Improved adaptive Gaussian mixture model for background subtraction, Proc. Int. Conf. on Pattern Recognition, Cambridge, 2004, vol. 2, pp. 28–31.
ACKNOWLEDGMENTS
The work of S. Nesmachnow and S. Iturriaga is partly supported by ANII and PEDECIBA, Uruguay. The work of Andrei Tchernykh is partly supported by the Ministry of Education and Science of the Russian Federation (Project no. 075-15-2020-788). Authors acknowledge the support of GEOCOM S.A., who provided the equipment for developing the research.
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Garate, B., Díaz, S., Iturriaga, S. et al. Autonomous Swarm of Low-Cost Commercial Unmanned Aerial Vehicles for Surveillance. Program Comput Soft 47, 558–577 (2021). https://doi.org/10.1134/S0361768821080120
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DOI: https://doi.org/10.1134/S0361768821080120