Multi UAVs Preflight Planning in a Shared and Dynamic Airspace

Amath SOW, Mauricio Rodriguez Cesen, Fabíola Martins Campos de Oliveira, Mariusz Wzorek, Daniel de Leng, Mattias Tiger, Fredrik Heintz, Christian Esteve Rothenberg

Keywords: Preflight Planning in Dynamic Airspace, Unmanned Aircraft System Traffic Management (UTM), Multi-Agent Path Finding (MAPF), Heterogeneous Agents with Kinematic and Dynamic Constraints, Large Neihborhood Search

Abstract: Preflight planning for large Unmanned Aerial Vehicle (UAV) fleets in dynamic, shared airspace presents significant challenges, including temporal No-Fly Zones (NFZs), heterogeneous vehicle profiles, and strict delivery deadlines. While Multi-Agent Path Finding provides a formal framework, existing methods often lack the scalability and flexibility required for real-world Unmanned Traffic Management (UTM). We propose DTAPP-IICR, a Delivery-Time Aware Prioritized Planner with Incremental and Iterative Conflict Resolution. Our framework first generates an initial solution by prioritizing missions based on urgency and planning round-trip trajectories with Safe Flight Interval Path Planning with Soft and Temporal Constraints (SFIPP-ST), a novel 4D single-agent planner. SFIPP-ST handles heterogeneous UAVs, strictly enforces temporal NFZs, and models inter-agent conflicts as soft constraints. Subsequently, an iterative Large Neighborhood Search, guided by a geometric conflict graph, efficiently resolves any residual conflicts. A completeness-preserving directional pruning technique further accelerates the 3D search. On benchmarks with temporal NFZs, DTAPP-IICR achieves near-100% success with fleets of up to 1,000 UAVs, gaining up to 50% runtime reduction through pruning and outperforming batch Enhanced Conflict-Based Search in the UTM context. It also scales in realistic city-scale operations where other priority-based methods fail even at moderate deployments. These results position DTAPP-IICR as a practical and scalable solution for preflight planning in dense, dynamic urban airspace.

Area: Search, Optimization, Planning, and Scheduling (SOPS)

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Submission Number: 49