Desenvolvendo Simulações de Uso de Drones em Cidades Inteligentes
Resumo
Dada a dificuldade de planejamento de missões para voos de drones nas cidades, devido ao ambiente e aos recursos limitados da aeronave, torna-se necessária a utilização de tecnologias de ambiente de simulação para treinamento e preparo para missões de campo. Com isso, este trabalho tem como objetivo descrever e analisar uma metodologia de desenvolvimento de simulação de voo de drones para o planejamento de missões de campo com um cenário verossímil comparado à realidade em termos de tempo e distância. Ao utilizar os simuladores, foi possível obter um tempo médio de 97,62% de precisão nos testes do ambiente virtual em relação aos testes de campo, apresentando uma verossimilhança alta obtida através dos ajustes realizados entre o Modelo Digital de Superfície e calibração dos sistemas de coordenadas utilizado.
Palavras-chave:
Simuladores de voo de drones, Gazebo, SITL, ArduPilot, Cidades Inteligentes
Referências
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C. H. Choi, H. J. Jang, S. G. Lim, H. C. Lim, S. H. Cho, I. Gaponov, Automatic wireless drone charging station creating essential environment for continuous drone operation, in: 2016 International Conference on Control, Automation and Information Sciences (ICCAIS), 2016, pp. 132-136. doi:10.1109/ICCAIS.2016.7822448.
G. Wild, J. Murray, G. Baxter, Exploring civil drone accidents and incidents to help prevent potential air disasters, Aerospace 3 (3) (2016) 22.
L. Rodrigues, A. Riker, M. Ribeiro, C. Both, F. Sousa, W. Moreira, K. Cardoso, A. Oliveira-Jr, Flight planning optimization of multiple uavs for internet of things, Sensors 21 (22). doi:10.3390/s21227735.
H. Bian, Q. Tan, S. Zhong, X. Zhang, Assessment of uam and drone noise impact on the environment based on virtual flights, Aerospace Science and Technology 118 (2021) 106996. doi: https://doi.org/10.1016/j.ast.2021.106996.
B. Siemiatkowska, W. Stecz, A framework for planning and execution of drone swarm missions in a hostile environment, Sensors 21 (12). doi:10.3390/s21124150.
Z. Shaikhanov, A. Boubrima, E. W. Knightly, Falcon: A networked drone system for sensing, localizing, and approaching rf targets, IEEE Internet of Things Journal 9 (12) (2022) 9843-9857. doi:10.1109/JIOT.2022.3152380.
J. Sandino, F. Maire, P. Caccetta, C. Sanderson, F. Gonzalez, Drone-based autonomous motion planning system for outdoor environments under object detection uncertainty, Remote Sensing 13 (21). doi:10.3390/rs13214481.
C. Bernardeschi, A. Fagiolini, M. Palmieri, G. Scrima, F. Sofia, ROS/Gazebo based simulation of co-operative UAVs, in: International Conference on Modelling and Simulation for Autonomous Systems, Springer, 2018, pp. 321-334.
B. Abbyasov, R. Lavrenov, A. Zakiev, K. Yakovlev, M. Svinin, E. Magid, Automatic tool for Gazebo world construction: from a grayscale image to a 3D solid model, in: 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 7226-7232. doi:10.1109/ICRA40945.2020.9196621.
M. Sánchez, J. Morales, J. L. Martínez, J. J. Fernández-Lozano, A. Garcê-Cerezo, Automatically annotated dataset of a ground mobile robot in natural environments via gazebo simulations, Sensors 22 (15). doi:10.3390/s22155599.
A. Farley, J. Wang, J. A. Marshall, How to pick a mobile robot simulator: A quantitative comparison of CoppeliaSim, Gazebo, MORSE and Webots with a focus on accuracy of motion, Simulation Modelling Practice and Theory 120 (2022) 102629. doi: https://doi.org/10.1016/j.simpat.2022.102629.
A. Mairaj, A. I. Baba, A. Y. Javaid, Application specific drone simulators: Recent advances and challenges, Simulation Modelling Practice and Theory 94 (2019) 100-117.
ArduPilot Team, ArduPilot - versatile, trusted, open, https://www.ardupilot.org/, acessado em 03 de agosto de 2022.
S. Baldi, D. Sun, X. Xia, G. Zhou, D. Liu, ArduPilot-based adaptive autopilot: architecture and software-in-the-loop experiments, IEEE Transactions on Aerospace and Electronic Systems.
Gazebo Development Team, Gazebo, https://gazebosim.org/, acessado em 04 de agosto de 2022.
N. Koenig, A. Howard, Design and use paradigms for gazebo, an open-source multi-robot simulator, in: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566), Vol. 3, IEEE, 2004, pp. 2149-2154.
S.-M. Tang, C.-H. Hsu, Z. Tian, X. Su, An aerodynamic, computer vision, and network simulator for networked drone applications, in: MobiCom '21: Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, ACM, 2021, pp. 831-833.
AirSim Development Team, Airsim project, https://github.com/microsoft/AirSim, acessado em 03 de agosto de 2022. (2022).
S. Shah, D. Dey, C. Lovett, A. Kapoor, Airsim: High-fidelity visual and physical simulation for autonomous vehicles, in: Field and service robotics, Springer, 2018, pp. 621-635.
RealFlight Development Team, Realflight, https://www.realflight.com/, acessado em 04 de agosto de 2022.
QGIS Development Team, QGIS geographic information system, https://www.qgis.org, acessado em 15 de junho de 2022. (2022).
Prefeitura de Niterói, SIGeo - sistema de gestão da geoinformação, https://www.sigeo.niteroi.rj.gov.br/, acessado em 01 de julho de 2022.
Google Development Team, Google earth, https://www.google.com.br/earth/, acessado em 13 de agosto de 2022.
Litchi Development Team, Litchi for dji drones, https://flylitchi.com/, acessado em 03 de agosto de 2022.
S. H. Alsamhi, O. Ma, M. S. Ansari, F. A. Almalki, Survey on collaborative smart drones and Internet of Things for improving smartness of smart cities, IEEE Access 7 (2019) 128125-128152. doi:10.1109/ACCESS.2019.2934998.
C. H. Choi, H. J. Jang, S. G. Lim, H. C. Lim, S. H. Cho, I. Gaponov, Automatic wireless drone charging station creating essential environment for continuous drone operation, in: 2016 International Conference on Control, Automation and Information Sciences (ICCAIS), 2016, pp. 132-136. doi:10.1109/ICCAIS.2016.7822448.
G. Wild, J. Murray, G. Baxter, Exploring civil drone accidents and incidents to help prevent potential air disasters, Aerospace 3 (3) (2016) 22.
L. Rodrigues, A. Riker, M. Ribeiro, C. Both, F. Sousa, W. Moreira, K. Cardoso, A. Oliveira-Jr, Flight planning optimization of multiple uavs for internet of things, Sensors 21 (22). doi:10.3390/s21227735.
H. Bian, Q. Tan, S. Zhong, X. Zhang, Assessment of uam and drone noise impact on the environment based on virtual flights, Aerospace Science and Technology 118 (2021) 106996. doi: https://doi.org/10.1016/j.ast.2021.106996.
B. Siemiatkowska, W. Stecz, A framework for planning and execution of drone swarm missions in a hostile environment, Sensors 21 (12). doi:10.3390/s21124150.
Z. Shaikhanov, A. Boubrima, E. W. Knightly, Falcon: A networked drone system for sensing, localizing, and approaching rf targets, IEEE Internet of Things Journal 9 (12) (2022) 9843-9857. doi:10.1109/JIOT.2022.3152380.
J. Sandino, F. Maire, P. Caccetta, C. Sanderson, F. Gonzalez, Drone-based autonomous motion planning system for outdoor environments under object detection uncertainty, Remote Sensing 13 (21). doi:10.3390/rs13214481.
C. Bernardeschi, A. Fagiolini, M. Palmieri, G. Scrima, F. Sofia, ROS/Gazebo based simulation of co-operative UAVs, in: International Conference on Modelling and Simulation for Autonomous Systems, Springer, 2018, pp. 321-334.
B. Abbyasov, R. Lavrenov, A. Zakiev, K. Yakovlev, M. Svinin, E. Magid, Automatic tool for Gazebo world construction: from a grayscale image to a 3D solid model, in: 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 7226-7232. doi:10.1109/ICRA40945.2020.9196621.
M. Sánchez, J. Morales, J. L. Martínez, J. J. Fernández-Lozano, A. Garcê-Cerezo, Automatically annotated dataset of a ground mobile robot in natural environments via gazebo simulations, Sensors 22 (15). doi:10.3390/s22155599.
A. Farley, J. Wang, J. A. Marshall, How to pick a mobile robot simulator: A quantitative comparison of CoppeliaSim, Gazebo, MORSE and Webots with a focus on accuracy of motion, Simulation Modelling Practice and Theory 120 (2022) 102629. doi: https://doi.org/10.1016/j.simpat.2022.102629.
A. Mairaj, A. I. Baba, A. Y. Javaid, Application specific drone simulators: Recent advances and challenges, Simulation Modelling Practice and Theory 94 (2019) 100-117.
ArduPilot Team, ArduPilot - versatile, trusted, open, https://www.ardupilot.org/, acessado em 03 de agosto de 2022.
S. Baldi, D. Sun, X. Xia, G. Zhou, D. Liu, ArduPilot-based adaptive autopilot: architecture and software-in-the-loop experiments, IEEE Transactions on Aerospace and Electronic Systems.
Gazebo Development Team, Gazebo, https://gazebosim.org/, acessado em 04 de agosto de 2022.
N. Koenig, A. Howard, Design and use paradigms for gazebo, an open-source multi-robot simulator, in: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566), Vol. 3, IEEE, 2004, pp. 2149-2154.
S.-M. Tang, C.-H. Hsu, Z. Tian, X. Su, An aerodynamic, computer vision, and network simulator for networked drone applications, in: MobiCom '21: Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, ACM, 2021, pp. 831-833.
AirSim Development Team, Airsim project, https://github.com/microsoft/AirSim, acessado em 03 de agosto de 2022. (2022).
S. Shah, D. Dey, C. Lovett, A. Kapoor, Airsim: High-fidelity visual and physical simulation for autonomous vehicles, in: Field and service robotics, Springer, 2018, pp. 621-635.
RealFlight Development Team, Realflight, https://www.realflight.com/, acessado em 04 de agosto de 2022.
QGIS Development Team, QGIS geographic information system, https://www.qgis.org, acessado em 15 de junho de 2022. (2022).
Prefeitura de Niterói, SIGeo - sistema de gestão da geoinformação, https://www.sigeo.niteroi.rj.gov.br/, acessado em 01 de julho de 2022.
Google Development Team, Google earth, https://www.google.com.br/earth/, acessado em 13 de agosto de 2022.
Litchi Development Team, Litchi for dji drones, https://flylitchi.com/, acessado em 03 de agosto de 2022.
Publicado
21/11/2022
Como Citar
MORAES, Lucas Baptista de; SABINO, Hullysses; HILARIO, Bruno; FERNANDES, Natalia Castro; PASSOS, Fernanda G. O..
Desenvolvendo Simulações de Uso de Drones em Cidades Inteligentes. In: ARTIGOS COMPLETOS - SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 12. , 2022, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 40-47.
ISSN 2763-9002.
DOI: https://doi.org/10.5753/sbesc_estendido.2022.227271.
