The Impact of Communication Delay on Drone Anti-Collision Algorithms
Abstract
Drones are viable alternatives to the problematic and complex transportation flow in urban areas. As companies adopt this delivery system, operational problems increase, making a case for developing and implementing safety procedures such as collision avoidance strategies. The development of algorithms for drone operation aims to expand the use of this technology and bring greater safety and efficiency. This paper aims to assess the effects of communication delay between drones in anticollision strategies. We simulate five strategies under five delay ranges, from Lidar sensors and 5G/6G wireless technologies to critical delays of around 750 ms.References
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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.
Published
2024-05-20
How to Cite
FERREIRA, Arthur A.; OLIVEIRA, Fabíola M. C. de; BITTENCOURT, Luiz F.; KAMIENSKI, Carlos.
The Impact of Communication Delay on Drone Anti-Collision Algorithms. In: BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (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.
