With the increasing adoption of cooperative multi-UAV systems in applications such as cargo delivery and ground reconnaissance, the demand for scalable and efficient path planning methods has grown substantially. However, traditional heuristic algorithms are frequently trapped in local optima, require task-specific manual tuning, and exhibit limited generalization capabilities. Furthermore, their dependence on iterative optimization renders them unsuitable for large-scale real-time applications. To address these challenges, this paper introduces an end-to-end deep reinforcement learning framework that bypasses the reliance on handcrafted heuristic rules. The proposed method leverages an encoder–decoder architecture with multi-head attention (MHA), where the encoder generates embeddings for UAVs and task parameters, while the decoder dynamically selects actions based on contextual embeddings and enforces feasibility through a masking mechanism. The MHA module effectively models global spatial-task dependencies among nodes, enhancing solution quality. Additionally, we integrate a Multi-Start Greedy Rollout Baseline to evaluate diverse trajectories via parallelized greedy searches, thereby reducing policy gradient variance and improving training stability. Experiments demonstrated significant improvements in scalability, particularly in 100-node scenarios, where our method drastically reduced inference time compared to conventional methods, while maintaining a competitive path cost efficiency. A further validation on simulated mission environments and real-world geospatial data (sourced from Google Earth) underscored the robust generalization of the framework. This work advances large-scale UAV mission planning by offering a scalable, adaptive, and computationally efficient solution.


    Access

    Download


    Export, share and cite



    Title :

    An End-to-End Solution for Large-Scale Multi-UAV Mission Path Planning


    Contributors:
    Jiazhan Gao (author) / Liruizhi Jia (author) / Minchi Kuang (author) / Heng Shi (author) / Jihong Zhu (author)


    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Large-scale UAV mission planning method and system

    LI YUMENG / SUN RUOFEI / GUO TONG et al. | European Patent Office | 2024

    Free access

    UAV Mission and Path Planning: Introduction

    Valavanis, Kimon P. / Vachtsevanos, George J. | Springer Verlag | 2014



    Multi-UAS Path-Planning for a Large-scale Disjoint Disaster Management

    Choi, Younghoon / Choi, Youngjun / Briceno, Simon et al. | IEEE | 2019


    Online Path Planning for Aircraft Landing Mission

    Li, Yan / Sun, Xiaoli | IEEE | 2018