Multi-Aircraft Energy-Optimal Route Planning for Advanced Air Mobility

Advanced Air Mobility (AAM) will require scalable methods for routing large numbers of increasingly autonomous aircraft through dense four-dimensional (4D) urban airspace volumes. This paper presents a decentralized multi-aircraft path-planning framework that formulates motion planning as a discrete-time graph search problem built from feasible kinematic maneuver primitives. Each vehicle performs an A* search over a dynamically constructed hybrid discrete-continuous state space, while a two-tier geofencing architecture and 4D occupancy tensor provide lightweight, decoupled conflict avoidance. Monte Carlo simulations evaluate performance across a range of traffic densities, demonstrating computational efficiency, energy-aware routing, and strong scalability. Case study results indicate a sub-second planning time per vehicle and illustrate how geofence extent impacts maximum airspace occupancy. Due to defined cost and heuristic function weights, the planning algorithm has a consistent preference for delaying departure time rather than generating circuitous paths with altitude changes when resolving spatiotemporal conflicts. This approach contributes a practical, ConOps-aligned solution to large-scale AAM multi-agent path finding and provides a foundation for future integration with service-provider traffic management architectures.