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Multi UAVs Preflight Planning in a Shared and Dynamic Airspace

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

TL;DR

DTAPP-IICR tackles scalable preflight planning for dense, dynamic urban airspace with temporal NFZs by combining SFIPP-ST, a 4D single-agent planner that handles heterogeneity and NFZs with soft conflict modeling, and an iterative, geometry-guided conflict-resolution loop. The framework guarantees completeness and optimality for individual plans and leverages a geometric conflict graph to perform large-neighborhood repairs, enabling near-100% success for fleets up to 1,000 UAVs and substantial runtime reductions over batch CBS/ECBS. Experimental results across Monte Carlo simulations and city-scale use cases demonstrate strong scalability, robustness to dynamic constraints, and practical applicability in UTM contexts. These findings establish DTAPP-IICR as a practical, scalable solution for preflight UAV planning in dense urban environments, with potential extensions to adaptive heuristics and region-based decomposition.

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.

Multi UAVs Preflight Planning in a Shared and Dynamic Airspace

TL;DR

DTAPP-IICR tackles scalable preflight planning for dense, dynamic urban airspace with temporal NFZs by combining SFIPP-ST, a 4D single-agent planner that handles heterogeneity and NFZs with soft conflict modeling, and an iterative, geometry-guided conflict-resolution loop. The framework guarantees completeness and optimality for individual plans and leverages a geometric conflict graph to perform large-neighborhood repairs, enabling near-100% success for fleets up to 1,000 UAVs and substantial runtime reductions over batch CBS/ECBS. Experimental results across Monte Carlo simulations and city-scale use cases demonstrate strong scalability, robustness to dynamic constraints, and practical applicability in UTM contexts. These findings establish DTAPP-IICR as a practical, scalable solution for preflight UAV planning in dense urban environments, with potential extensions to adaptive heuristics and region-based decomposition.

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.
Paper Structure (24 sections, 2 theorems, 6 equations, 5 figures, 3 tables, 3 algorithms)

This paper contains 24 sections, 2 theorems, 6 equations, 5 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Definition
  4. Solution validity constraints:
  5. Objective
  6. DTAPP Framework
  7. Single Agent Planning with Soft and Temporal Constraints
  8. SFIPP-ST
  9. Main Algorithm:
  10. Directional Pruning for Neighbor Expansion:
  11. Theoretical Properties of SFIPP-ST
  12. DTAPP-IICR
  13. Geometric Conflict-Based Heuristic
  14. Case 1 (Small conflict region, $|V_c^0|\le N$):
  15. Case 2 (Large conflict region, $|V_c^0|>N$):
  16. ...and 9 more sections

Key Result

Theorem 1

SFIPP-ST is complete; it returns a valid path if one exists and FAILURE otherwise.

Figures (5)

  • Figure 2: Overview of Preflight Planning in UTM. We have three hubs that send periodic operation requests to the central controller. We use DTAPP-IICR as a prioritized planner, where operations are sorted based on delivery urgency, $t^{\mathrm{init}}$. We use SFIPP-ST as the single-agent planner, which receives the UAV profile $(t^{\mathrm{init}}, v_{u_i}, r_{u_i})$, precomputed hard and regulatory constraints, and computes a 4D roundtrip trajectory from the hub to the delivery location and back. We add pruning during neighbor expansion to reduce the search. The initial solution might have soft conflicts, and we iteratively replan the conflicting agents until the solution is free of conflict or the time limit is reached.
  • Figure 3: Scalability of DTAPP-IICR compared to PP, batch CBS/ECBS under varying obstacle densities. a) Using 5% of obstacles. b) Using 10% of obstacles.
  • Figure 4: Success Rate and Average Runtime for PP, PP (W/O), DTAPP-IICR, and DTAPP-IICR (W/O). (a) 100 agents with 5% obstacles, with a time limit of 500 seconds. (b) 1000 agents with 5% obstacles, with a time limit of 900 seconds.
  • Figure 5: 100 drone paths in an urban scene are shown in different colors when evaluating our DTAPP-IICR algorithm.
  • Figure 6: Different drones on the scene during the delivery of a package with mobility restrictions such as NFZs.

Theorems & Definitions (7)

  • Definition 1: UAV model
  • Definition 2: Roundtrip path
  • Definition 3: Temporal NFZs
  • Definition 4: Safe Flight Interval (SFI)
  • Definition 5: Search Node Definition
  • Theorem 1: Completeness
  • Theorem 2: Optimality