Abstract
This paper presents a comprehensive Unmanned Aircraft System (UAS) traffic generation tool designed to create realistic and complex drone traffic scenarios for various operational use cases. The tool’s primary features include the ability to simulate a diverse range of flight patterns, such as surveillance, cargo transportation, emergency response, and personal aerial mobility. The tool can accurately model UAS traffic with detailed flight plan information, including waypoints, polygonal areas, and temporal restrictions. Additionally, the paper demonstrates the potential for integrating the tool with UAS Traffic Management (UTM) systems, providing a testbed for assessing strategic and tactical conflict detection and resolution. By using the tool as a ground truth reference, the performance of the UTM system can be evaluated, ensuring its effectiveness in maintaining safe and efficient airspace operations. Overall, this UAS traffic generation tool offers valuable insights for researchers, industry stakeholders, and policymakers, contributing to the development of robust and efficient UTM systems and promoting the safe integration of drones into shared airspace.
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Acknowledgements
Authors acknowledge the funding received under grants PID2020-118249RB-C21 and PDC2021-121567-C21 funded by MCIN/AEI/10.13039/501100011033 and by EU Next GenerationEU/PRTR. Daniel Raposo acknowledges funding for his scholarship from UPM project RP180022025.
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Raposo, D., Carramiñana, D., Besada, J., Bernardos, A. (2023). A Realistic UAS Traffic Generation Tool to Evaluate and Optimize U-Space Airspace Capacity. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 750. Springer, Cham. https://doi.org/10.1007/978-3-031-42536-3_3
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