An Efficient Task Scheduling Framework for Large-Scale 3D Reconstruction in Multi-UAV Edge-Intelligence Systems

With the rapid development of edge-intelligence systems, multi-UAV platforms have become vital for large-scale 3D reconstruction. However, efficient task scheduling remains a critical challenge due to constraints on UAV energy, communication range, and the need for balanced workload distribution. To address these issues, this paper presents a novel, centralized two-stage task scheduling framework. In the first stage, the framework partitions the target area into communication-feasible subregions by applying cell decomposition that accounts for no-fly zones and workload. It then models the subregion allocation as a Capacitated Vehicle Routing Problem (CVRP) with an added balancing constraint to optimize the traversal sequence for each operational sortie. In the second stage, a time-efficient, scan-based heuristic algorithm allocates viewpoints among UAVs to ensure workload balance, minimizing the mission completion time. Extensive simulations demonstrate that our proposed approach achieves superior performance in workload balance, path efficiency, and reconstruction quality. Overall, this work provides a scalable and energy-aware solution for centralized multi-UAV 3D reconstruction, highlighting an effective approach to ensure cooperation and efficiency in complex multi-agent systems.