This paper proposes a FOV-aware, area-based coordination framework for UAV-UGV collaborative surveillance under real-world constraints such as limited battery capacity and road-constrained UGV mobility. Unlike traditional point-based reconnaissance approaches, our method discretizes the surveillance region into realistic grid cells based on UAV camera footprints and guides UAVs to maximize coverage using heading-constrained field-of-view (FOV) planning. A single UGV navigates a predefined fixed route extracted from GeoJSON road data and serves as a mobile charging station with two wireless pads. We formulate the task as a Team Orienteering Problem (TOP) and address it using a structured meta-heuristic algorithm. Key innovations include heading-aware path construction, dynamic reward reinitialization to prevent local stagnation, and tight synchronization with an ILP-based UAV scheduling algorithm that considers operational flight time and charging constraints. UAVs autonomously select their next positions within $\mathrm{a}\pm 5^{\circ}$ heading cone to optimize new area coverage while minimizing redundant overlap. Simulation results conducted over the Texas A&M campus demonstrate that our method achieves up to 19% higher area coverage, reduces redundancy by 11.3%, and lowers UAV charging delays compared to point-based and naive area-based baselines. These findings validate the effectiveness of integrating FOV-driven spatial planning with temporal scheduling and adaptive reward modeling, offering a scalable and robust framework for autonomous persistent surveillance missions.


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    Titel :

    Team Orienteering and Scheduling Algorithms for Collaborative UAV-UGV Area Coverage with Battery Constraints


    Beteiligte:


    Erscheinungsdatum :

    14.05.2025


    Format / Umfang :

    2326444 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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