Scalability and Adaptability Assessment of Classical Path Planners for Urban Air Mobility

Efficient path planning and replanning are essential for safe autonomous unmanned aerial vehicle (UAV) navigation in urban air mobility, where vehicles operate in a dynamic and shared airspace. In such environments, UAVs require both fast initial path generation in known conditions and efficient replanning capabilities when obstacles or airspace constraints change during execution. In addition, multi-UAV operations introduce further challenges related to scalability and inter-UAV conflict management. This paper addresses the scalability of the classical A* and D* Lite algorithms for UAV path planning in static and dynamic three-dimensional (3D) grid environments through a comparative performance evaluation. The comparison is conducted in both single-UAV and multi-UAV configurations in order to assess initial planning efficiency, dynamic replanning capability, scalability, and inter-UAV conflict occurrence. As expected from previous studies, A* shows significantly lower planning time in static environments, while D* Lite demonstrates superior replanning capabilities in dynamic settings. However, the scalability analysis demonstrates that, as the number of UAVs increases, inter-UAV conflicts and planning time grow significantly. These findings highlight the limitations of independent classical path planning in a shared airspace with multiple UAVs. This indicates that these algorithms alone cannot ensure efficient coordination in shared and scaled scenarios. These findings support a hybrid approach in which A* provides the efficient initial global path while a higher-level learning component enables adaptive decision-making for real-time conflict avoidance and response to environmental changes.