Flow-Based Path Planning for Multiple Homogenous UAVs for Outdoor Formation-Flying

2025-11-24

AI Summary

This work addresses the collision-free path planning problem for homogeneous multi-UAV formations in outdoor environments. The proposed method formulates global trajectory planning as a flow network optimization: it discretizes the continuous GPS coordinate space into a weighted directed graph, unifying positional constraints and obstacle avoidance within a single graph-theoretic model. Initial trajectories are generated via minimum-cost path search, followed by spatiotemporal resource allocation optimization using the Ford–Fulkerson maximum-flow algorithm to resolve conflicts and ensure coordination. The approach supports dynamic formation reconfiguration. In simulation, it successfully plans complex formations of up to 64 UAVs; in real-world flight experiments, it demonstrates safe, real-time coordinated flight for three UAVs. The method significantly improves scalability and robustness for large-scale homogeneous UAV systems, enabling efficient, conflict-free path planning under realistic operational constraints.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• 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
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Collision-free path planning is the most crucial component in multi-UAV formation-flying (MFF). We use unlabeled homogenous quadcopters (UAVs) to demonstrate the use of a flow network to create complete (inter-UAV) collision-free paths. This procedure has three main parts: 1) Creating a flow network graph from physical GPS coordinates, 2) Finding a path of minimum cost (least distance) using any graph-based path-finding algorithm, and 3) Implementing the Ford-Fulkerson Method to find the paths with the maximum flow (no collision). Simulations of up to 64 UAVs were conducted for various formations, followed by a practical experiment with 3 quadcopters for testing physical plausibility and feasibility. The results of these tests show the efficacy of this method's ability to produce safe, collision-free paths.

Problem

• Planning collision-free paths for multiple homogeneous UAVs
• Using flow networks to ensure maximum flow without collisions
• Implementing graph algorithms for optimal pathfinding in formations

Innovation

• Flow network graph from GPS coordinates
• Minimum cost path via graph algorithms
• Ford-Fulkerson method for collision-free paths