O Impacto do Atraso de Comunicação nos Algoritmos Anticolisão de Drones
Resumo
Drones são alternativas plausíveis para o problemático e complexo fluxo de transporte em áreas urbanas. À medida em que as empresas adotam essa modalidade de entrega, o número de problemas operacionais aumenta, sendo necessários o desenvolvimento e a implementação de procedimentos para segurança, como estratégias para evitar colisões. O desenvolvimento de algoritmos para a operação de drones visa expandir o uso dessa tecnologia e trazer mais segurança e eficiência. O objetivo deste artigo é avaliar os efeitos do atraso de comunicação entre drones em estratégias anticolisão. Cinco estratégias são simuladas em cinco situações de atraso, variando desde atraso em sensores Lidar, em tecnologias de rede 5G e 6G até atrasos críticos da ordem de 750 ms.Referências
3rd Generation Partnership Project (3GPP) (2022). 3rd Generation Partnership Project (3GPP) Work Plan. [link]. Acessado em 22 de janeiro de 2023.
3rd Generation Partnership Project (3GPP) (2023). Resultados de busca para “6G”. [link]. Acessado em 22 de janeiro de 2023.
Al-Mousa, A., Sababha, B. H., Al-Madi, N., Barghouthi, A., and Younisse, R. (2019). UTSim: A framework and simulator for UAV air traffic integration, control, and communication. International Journal of Advanced Robotic Systems, 16(5):1–19.
Chen, K.-W., Xie, M.-R., Chen, Y.-M., Chu, T.-T., and Lin, Y.-B. (2022). DroneTalk: An Internet-of-Things-Based Drone System for Last-Mile Drone Delivery. IEEE Transactions on Intelligent Transportation Systems, 23(9):15204–15217.
Huang, S., Teo, R. S. H., and Tan, K. K. (2019). Collision avoidance of multi unmanned aerial vehicles: A review. Annual Reviews in Control, 48:147–164.
Kellner, J. R., Armston, J., Birrer, M., Cushman, K., Duncanson, L., Eck, C., Falleger, C., Imbach, B., Král, K., Krček, M., et al. (2019). New opportunities for forest remote sensing through ultra-high-density drone lidar. Surveys in Geophysics, 40:959–977.
Li, Y., Yonezawa, K., and Liu, H. (2021). Effect of ducted multi-propeller configuration on aerodynamic performance in quadrotor drone. Drones, 5(3).
Mansoor, N., Hossain, M. I., Rozario, A., Zareei, M., and Arreola, A. R. (2023). A Fresh Look at Routing Protocols in Unmanned Aerial Vehicular Networks: A Survey. IEEE Access, 11:66289–66308.
Masaracchia, A., Li, Y., Nguyen, K. K., Yin, C., Khosravirad, S. R., Costa, D. B. D., and Duong, T. Q. (2021). UAV-Enabled Ultra-Reliable Low-Latency Communications for 6G: A Comprehensive Survey. IEEE Access, 9:137338–137352.
Mehendale, N. and Neoge, S. (2020). Review on lidar technology. Available at SSRN.
Mishra, D., Vegni, A. M., Loscrí, V., and Natalizio, E. (2021). Drone networking in the 6g era: A technology overview. IEEE Communications Standards Magazine, 5(4):88–95.
Oliveira, F., Bittencourt, L., and Kamienski, C. (2021). Prevenção de Colisões em Serviços de Entregas por Drones em Cidades Inteligentes. In Anais do V Workshop de Computação Urbana, pages 182–195, Porto Alegre, RS, Brasil. SBC.
Oliveira, F. M. C., Bittencourt, L. F., Bianchi, R. A. C., and Kamienski, C. A. (2023). Drones in the Big City: Autonomous Collision Avoidance for Aerial Delivery Services. IEEE Transactions on Intelligent Transportation Systems.
Ouahouah, S., Bagaa, M., Prados-Garzon, J., and Taleb, T. (2022). Deep-Reinforcement-Learning-Based Collision Avoidance in UAV Environment. IEEE Internet of Things Journal, 9(6):4015–4030.
Rodrigues, T. A. and outros (2022). Drone flight data reveal energy and greenhouse gas emissions savings for very small package delivery. Patterns, 3(8):100569.
Seo, J., Kim, Y., Kim, S., and Tsourdos, A. (2017). Collision Avoidance Strategies for Unmanned Aerial Vehicles in Formation Flight. IEEE Transactions on Aerospace and Electronic Systems, 53(6):2718–2734.
Telli, K., Kraa, O., Himeur, Y., Ouamane, A., Boumehraz, M., Atalla, S., and Mansoor, W. (2023). A comprehensive review of recent research trends on unmanned aerial vehicles (uavs). Systems, 11(8).
Thumiger, N. and Deghat, M. (2022). A Multi-Agent Deep Reinforcement Learning Approach for Practical Decentralized UAV Collision Avoidance. IEEE Control Systems Letters, 6:2174–2179.
Wang, D., Fan, T., Han, T., and Pan, J. (2020). A Two-Stage Reinforcement Learning Approach for Multi-UAV Collision Avoidance Under Imperfect Sensing. IEEE Robotics and Automation Letters, 5(2):3098–3105.
Zhou, J. (2022). A Review of LiDAR sensor Technologies for Perception in Automated Driving. Academic Journal of Science and Technology, 3:255–261.
3rd Generation Partnership Project (3GPP) (2023). Resultados de busca para “6G”. [link]. Acessado em 22 de janeiro de 2023.
Al-Mousa, A., Sababha, B. H., Al-Madi, N., Barghouthi, A., and Younisse, R. (2019). UTSim: A framework and simulator for UAV air traffic integration, control, and communication. International Journal of Advanced Robotic Systems, 16(5):1–19.
Chen, K.-W., Xie, M.-R., Chen, Y.-M., Chu, T.-T., and Lin, Y.-B. (2022). DroneTalk: An Internet-of-Things-Based Drone System for Last-Mile Drone Delivery. IEEE Transactions on Intelligent Transportation Systems, 23(9):15204–15217.
Huang, S., Teo, R. S. H., and Tan, K. K. (2019). Collision avoidance of multi unmanned aerial vehicles: A review. Annual Reviews in Control, 48:147–164.
Kellner, J. R., Armston, J., Birrer, M., Cushman, K., Duncanson, L., Eck, C., Falleger, C., Imbach, B., Král, K., Krček, M., et al. (2019). New opportunities for forest remote sensing through ultra-high-density drone lidar. Surveys in Geophysics, 40:959–977.
Li, Y., Yonezawa, K., and Liu, H. (2021). Effect of ducted multi-propeller configuration on aerodynamic performance in quadrotor drone. Drones, 5(3).
Mansoor, N., Hossain, M. I., Rozario, A., Zareei, M., and Arreola, A. R. (2023). A Fresh Look at Routing Protocols in Unmanned Aerial Vehicular Networks: A Survey. IEEE Access, 11:66289–66308.
Masaracchia, A., Li, Y., Nguyen, K. K., Yin, C., Khosravirad, S. R., Costa, D. B. D., and Duong, T. Q. (2021). UAV-Enabled Ultra-Reliable Low-Latency Communications for 6G: A Comprehensive Survey. IEEE Access, 9:137338–137352.
Mehendale, N. and Neoge, S. (2020). Review on lidar technology. Available at SSRN.
Mishra, D., Vegni, A. M., Loscrí, V., and Natalizio, E. (2021). Drone networking in the 6g era: A technology overview. IEEE Communications Standards Magazine, 5(4):88–95.
Oliveira, F., Bittencourt, L., and Kamienski, C. (2021). Prevenção de Colisões em Serviços de Entregas por Drones em Cidades Inteligentes. In Anais do V Workshop de Computação Urbana, pages 182–195, Porto Alegre, RS, Brasil. SBC.
Oliveira, F. M. C., Bittencourt, L. F., Bianchi, R. A. C., and Kamienski, C. A. (2023). Drones in the Big City: Autonomous Collision Avoidance for Aerial Delivery Services. IEEE Transactions on Intelligent Transportation Systems.
Ouahouah, S., Bagaa, M., Prados-Garzon, J., and Taleb, T. (2022). Deep-Reinforcement-Learning-Based Collision Avoidance in UAV Environment. IEEE Internet of Things Journal, 9(6):4015–4030.
Rodrigues, T. A. and outros (2022). Drone flight data reveal energy and greenhouse gas emissions savings for very small package delivery. Patterns, 3(8):100569.
Seo, J., Kim, Y., Kim, S., and Tsourdos, A. (2017). Collision Avoidance Strategies for Unmanned Aerial Vehicles in Formation Flight. IEEE Transactions on Aerospace and Electronic Systems, 53(6):2718–2734.
Telli, K., Kraa, O., Himeur, Y., Ouamane, A., Boumehraz, M., Atalla, S., and Mansoor, W. (2023). A comprehensive review of recent research trends on unmanned aerial vehicles (uavs). Systems, 11(8).
Thumiger, N. and Deghat, M. (2022). A Multi-Agent Deep Reinforcement Learning Approach for Practical Decentralized UAV Collision Avoidance. IEEE Control Systems Letters, 6:2174–2179.
Wang, D., Fan, T., Han, T., and Pan, J. (2020). A Two-Stage Reinforcement Learning Approach for Multi-UAV Collision Avoidance Under Imperfect Sensing. IEEE Robotics and Automation Letters, 5(2):3098–3105.
Zhou, J. (2022). A Review of LiDAR sensor Technologies for Perception in Automated Driving. Academic Journal of Science and Technology, 3:255–261.
Publicado
20/05/2024
Como Citar
FERREIRA, Arthur A.; OLIVEIRA, Fabíola M. C. de; BITTENCOURT, Luiz F.; KAMIENSKI, Carlos.
O Impacto do Atraso de Comunicação nos Algoritmos Anticolisão de Drones. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 42. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 475-488.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2024.1426.