Learning enhanced scheduling and resource allocation for heterogeneous UAV swarms in edge assisted remote sensing

Large-scale 3D mapping and high-resolution remote sensing are essential for environmental monitoring, disaster assessment, and urban planning. Heterogeneous unmanned aerial vehicle (UAV) swarms, equipped with complementary sensing and onboard edge computing capabilities, offer efficient, adaptive, and resource-aware operations. However, achieving complete spatial coverage, ensuring sensing relevance, and optimizing both communication and computational resources remain challenging under dynamic and complex conditions. This paper proposes an energy- and resource-aware cooperative framework, DMMP-PR-TSA, which integrates remote sensing data-driven region partitioning, improved self-organizing map (SOM)-based intelligent pre-assignment, priority-aware dynamic task reallocation (PR), and reinforcement learning (RL)-based task sequence adjustment (TSA). The framework jointly optimizes spatial path planning for sensing tasks and computational resource allocation for edge processing and collaborative task execution, while embedding priority handling to meet deadlines for critical missions. Compared with baseline algorithms, DMMP-PR-TSA demonstrates [Formula: see text] higher completion rates in large-scale missions, [Formula: see text] improvement under dynamic fleet changes, and consistently higher success rates for high-priority tasks. Simulation results validate its scalability, robustness, and mission-critical applicability, highlighting its effectiveness in advancing the intelligence and operational efficiency of UAV-based large-scale remote sensing and edge-computing-assisted systems.