Edge Computing for Drones: Managing Landings and Take-offs in Distribution Centers
Abstract
Existing drone anti-collision strategies mainly focus on cruise flight in scenarios with a limited number of drones, neglecting the management of landing and takeoff in large distribution centers where there is a high density of drones. This paper proposes and evaluates new policies of landing and takeoff sequencing in high-density areas, improving the Drone Edge Management System (DREMS). These policies are classified and tested in drone high-density scenarios, increasing the actual landing and takeoff rate without increasing the collision rate. The results highlight the need for an effective sequencing strategy in distribution centers to avoid collisions.
References
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Lu, S.-H., Kuo, R., Ho, Y.-T., and Nguyen, A.-T. (2022). Improving the efficiency of last-mile delivery with the flexible drones traveling salesman problem. Expert Systems with Applications, 209:118351.
McEnroe, P., Wang, S., and Liyanage, M. (2022). A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges. IEEE Internet of Things Journal, 9(17):15435–15459.
Moshref-Javadi, M. and Winkenbach, M. (2021). Applications and Research avenues for drone-based models in logistics: A classification and review. Expert Systems with Applications, 177:114854.
Pradeep, P. and Wei, P. (2018). Heuristic Approach for Arrival Sequencing and Scheduling for eVTOL Aircraft in On-Demand Urban Air Mobility. In 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC), pages 1–7.
Soares, L. D. P., Oliveira, F. C. D., Kamienski, C. A., and Bittencourt, L. F. (2023). Drone edge management system (drems): Sequencing drone takeoff and landing. In 2023 10th International Conference on Future Internet of Things and Cloud (FiCloud), pages 114–121, Los Alamitos, CA, USA. IEEE Computer Society.
Song, K. and Yeo, H. (2021). Development of optimal scheduling strategy and approach control model of multicopter VTOL aircraft for urban air mobility (UAM) operation. Transportation Research Part C: Emerging Technologies, 128:103181.
Sáez, R., Polishchuk, T., Schmidt, C., Hardell, H., Smetanová, L., Polishchuk, V., and Prats, X. (2021). Automated sequencing and merging with dynamic aircraft arrival routes and speed management for continuous descent operations. Transportation Research Part C: Emerging Technologies, 132:103402.
Bosson, C. and Lauderdale, T. A. (2018). Simulation Evaluations of an Autonomous Urban Air Mobility Network Management and Separation Service. In 2018 Aviation Technology, Integration, and Operations Conference.
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.
de 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.
de Oliveira, F. M. C., Bittencourt, L. F., 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.
Hayat, S., Jung, R., Hellwagner, H., Bettstetter, C., Emini, D., and Schnieders, D. (2021). Edge Computing in 5G for Drone Navigation: What to Offload? IEEE Robotics and Automation Letters, 6(2):2571–2578.
Itoh, E. and Erzberger, H. (2014). Design Principles and Algorithms for Air Traffic Arrival Scheduling.
Kleinbekman, I. C., Mitici, M. A., and Wei, P. (2018). eVTOL Arrival Sequencing and Scheduling for On-Demand Urban Air Mobility. In 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC), pages 1–7.
Labib, N. S., Brust, M. R., Danoy, G., and Bouvry, P. (2021). The Rise of Drones in Internet of Things: A Survey on the Evolution, Prospects and Challenges of Unmanned Aerial Vehicles. IEEE Access, 9:115466–115487.
Lu, S.-H., Kuo, R., Ho, Y.-T., and Nguyen, A.-T. (2022). Improving the efficiency of last-mile delivery with the flexible drones traveling salesman problem. Expert Systems with Applications, 209:118351.
McEnroe, P., Wang, S., and Liyanage, M. (2022). A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges. IEEE Internet of Things Journal, 9(17):15435–15459.
Moshref-Javadi, M. and Winkenbach, M. (2021). Applications and Research avenues for drone-based models in logistics: A classification and review. Expert Systems with Applications, 177:114854.
Pradeep, P. and Wei, P. (2018). Heuristic Approach for Arrival Sequencing and Scheduling for eVTOL Aircraft in On-Demand Urban Air Mobility. In 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC), pages 1–7.
Soares, L. D. P., Oliveira, F. C. D., Kamienski, C. A., and Bittencourt, L. F. (2023). Drone edge management system (drems): Sequencing drone takeoff and landing. In 2023 10th International Conference on Future Internet of Things and Cloud (FiCloud), pages 114–121, Los Alamitos, CA, USA. IEEE Computer Society.
Song, K. and Yeo, H. (2021). Development of optimal scheduling strategy and approach control model of multicopter VTOL aircraft for urban air mobility (UAM) operation. Transportation Research Part C: Emerging Technologies, 128:103181.
Sáez, R., Polishchuk, T., Schmidt, C., Hardell, H., Smetanová, L., Polishchuk, V., and Prats, X. (2021). Automated sequencing and merging with dynamic aircraft arrival routes and speed management for continuous descent operations. Transportation Research Part C: Emerging Technologies, 132:103402.
Published
2024-05-20
How to Cite
SOARES, Lucas de Paula; OLIVEIRA, Fabíola M. C. de; KAMIENSKI, Carlos A.; BITTENCOURT, Luiz F..
Edge Computing for Drones: Managing Landings and Take-offs in Distribution Centers. In: BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 42. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 407-420.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2024.1406.
