Multi-Agent Path Planning for Autonomous Drone Fleets in Urban Environments

The aim was to evaluate the efficiency of cooperative route planning for autonomous drones operating in densely built-up urban environments, with a particular focus on system resilience and positioning accuracy. Experiments were conducted between April and June 2025 at the Intelligent Unmanned Systems Laboratory of the School of Aeronautics at Beihang University (Shunyi District, Beijing, China). The methodology included 60 flights involving multidrone groups using Da Jiang Innovations Matrice 300 Real-Time Kinematics and Yuneec H520 platforms, both operating on multi-agent route cooperative planning (MARCP) and rapidly exploring random tree star (RRT*) algorithms. Data analysis was performed using Python. The results indicated that the MARCP algorithm significantly outperformed RRT* across all navigation metrics: the mean positional error was 0.83 m compared to 1.41 m, and maximum deviations were 2.03 m versus 3.56 m, respectively. MARCP showed superior route adherence at altitudes up to 60 m and in narrow corridors. It achieved 96.6% collision avoidance versus RRT*’s 82.8% through early conflict prediction and dynamic trajectory reallocation. Drone coordination was more reliable with consistent interdrone distances of 6.7–8.9 m, while RRT* showed significant inconsistencies. MARCP recorded only 19 failures (3 critical) compared to RRT*’s 47 failures (20 involving synchronization loss). Buffering and predictive models provided resilience under communication disruptions.