Urban Multi\u2011UAV Path\u2011Planning Simulation Dataset with Static & Dynamic Obstacles Across Variable Densities

This dataset enables replication of the experiments in the paper \PAIR: A Hybrid A* with PPO Path Planning for Multi-UAVs in Dynamic Urban MEC Environments\ by Bahaa Hussien Taher et al., submitted to IEEE Transactions on Aerospace and Electronic Systems. It provides simulated urban scenarios for evaluating multi-UAV path planning algorithms, including A*, D* Lite, CBS-D* Lite, PSO, and the proposed PAIR framework. The dataset includes nine scenarios with varying obstacle density (15%, 25%, 40%), clustering (\u2018clustered\u2019 or \u2018dispersed\u2019), and obstacle dynamics (\u2018static\u2019 or \u2018dynamic\u2019). Each scenario contains static obstacle positions (3000\u20137000 obstacles), start-goal positions for 9 UAVs, and trajectories for dynamic obstacles (5, 10, 15, 20, 25, 30 per dynamic scenario). Users can use this dataset to assess planners on metrics such as mission success rate, energy consumption, travel time, normalized path length, optimality score, and replanning latency, as outlined in Section IV.F and Section VI of the paper. The dataset is provided in JSON and CSV formats for flexibility in simulation and analysis.