Urban Multi-UAV Path Planning Simulation Dataset (2-D Dynamic Urban MEC Scenarios)

This dataset accompanies the manuscript “PAIR: A Hybrid A* with PPO Path Planner for Multi-UAV Navigation in 2-D Dynamic Urban MEC Environments” by Bahaa Hussien Taher et al., submitted to the MDPI journal Drones. It provides a reproducible benchmark for evaluating multi-UAV path planning algorithms in cluttered, dynamic urban airspace.The dataset consists of nine procedurally generated 2-D urban scenarios on a 100×100 grid. Each scenario includes (i) static obstacles with three density levels (approximately 15%, 25%, and 40%), (ii) two spatial regimes (“clustered” and “dispersed”), (iii) static or Markovian dynamic obstacles (Move / Pause / Detour states), and (iv) start–goal positions for nine UAVs. Dynamic scenarios contain trajectories for up to 30 moving obstacles over 1,000 time steps.Data are provided as CSV tables (scenarios.csv, static_obstacles.csv, uav_positions.csv, dynamic_obstacles.csv) and optionally as a single JSON file aggregating all scenarios. These files allow users to benchmark classical planners (A*, D* Lite, CBS–D*), metaheuristics (PSO), and learning-based planners (e.g., PPO-based PAIR) on metrics such as mission success rate, travel time, energy surrogate, and unified path-quality score.A plain-text README file in the repository documents the file structure, field definitions, recommended Python environment, and an example workflow for loading the data and running simulations.