A Dataset of Distributed Operational States and Resource Allocation for Urban Low-Altitude Logistics Drone Swarms

Urban low-altitude logistics drone swarms offer a promising solution for intelligent delivery, but the development of their core algorithms—distributed cooperative control and dynamic resource allocation—has been hindered by a lack of realistic multi-drone operational data. To address this, we present the “Dataset of Distributed Operational States and Resource Allocation for Urban Low-Altitude Logistics Drone Swarms,”collected from real-flight experiments in a simulated urban low-altitude environment. The dataset includes over 100 cooperative delivery missions, yielding ~1.05 million high-frequency state records with centimeter-level 3D position, velocity, attitude, timestamps, task identifiers, and semantic flight phase labels. Data were acquired using drones equipped with distributed flight control and high-precision GNSS-IMU, and underwent synchronous recording, post-processing fusion (PPK + Kalman filtering), and quality control. Validation confirms high positioning accuracy, temporal synchronization, and data consistency through multidimensional analysis. Stored in structured CSV format with full metadata, this dataset provides a reliable benchmark for research in distributed control, resource allocation, trajectory prediction, digital twin simulation, and low-altitude traffic management.