Enhanced Air–Ground Coordination with the HUCC-MAPPO Algorithm for Improved Multi-UAV Reconnaissance Efficiency

A novel air–ground coordination (AGC) system is developed, integrating multi-agent proximal policy optimization (MAPPO) with Voronoi diagram-based path planning to revolutionize task allocation and execution in complex environments. This advanced approach significantly enhances the efficiency and precision of coordination between unmanned aerial vehicles (UAVs) and ground operators, ensuring superior mission performance. Key improvements of the proposed system include adaptive task allocation and Voronoi diagram-based path planning, which effectively optimize task area coverage and mission execution. In simulations, the HUCC-MAPPO algorithm demonstrates exceptional improvements in simulation, surpassing conventional methods in task coverage, mission efficiency, and obstacle avoidance. Specific experimental results show significant enhancements in task area coverage by 15%, reduction in path lengths by 12% and an increase in task completion rates by 18% compared to baseline methods. These results highlight its strong potential for real-world applications, particularly in multi-UAV reconnaissance missions where precise and dynamic coordination is crucial.