Multi UAVs Preflight Planning in a Shared and Dynamic Airspace

2026-02-12

AI Summary

This work addresses the challenge of large-scale drone fleet coordination in dynamic shared airspace, where temporary no-fly zones, heterogeneous vehicle types, and strict delivery deadlines render existing approaches inadequate in balancing scalability and practicality. The authors propose the DTAPP-IICR framework, which integrates delivery-time-aware prioritized planning with an incremental iterative conflict resolution mechanism. It first generates an initial schedule based on task urgency and then employs a novel 4D single-agent planner, SFIPP-ST, to model spatiotemporal constraints and soft conflicts. Multi-agent conflicts are efficiently resolved via large neighborhood search guided by a geometric conflict graph, while directional pruning ensures completeness in 3D space and accelerates computation. Experiments in urban scenarios with temporary no-fly zones demonstrate that the method can effectively coordinate fleets of up to one thousand drones, achieving near-100% task success rates and reducing runtime by up to 50% compared to baseline methods, significantly outperforming existing batch conflict-resolution algorithms.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Preflight planning for large-scale 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 (MAPF) 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 Planning method with Incremental and Iterative Conflict Resolution. Our framework first generates an initial solution by prioritizing missions based on urgency. Secondly, it computes roundtrip trajectories using SFIPP-ST, a novel 4D single-agent planner (Safe Flight Interval Path Planning with Soft and Temporal Constraints). 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 and gains up to 50% runtime reduction from pruning, outperforming batch Enhanced Conflict-Based Search in the UTM context. Scaling successfully in realistic city-scale operations where other priority-based methods fail even at moderate deployments, DTAPP-IICR is positioned as a practical and scalable solution for preflight planning in dense, dynamic urban airspace.

Problem

• Multi-UAV
• Preflight Planning
• Dynamic Airspace
• Temporal No-Fly Zones
• Unmanned Traffic Management

Innovation

• Multi-Agent Path Finding
• Temporal No-Fly Zones
• Incremental Conflict Resolution
• 4D Trajectory Planning
• Large Neighborhood Search