Collaborative multi-UAV swarms are central to many missions. This review covers the most recent two years. It organizes the literature with a scenario-aligned taxonomy. The taxonomy has 12 cells (Path/Distribution/Coverage × offline/online × static/dynamic). Nine cells are well populated and analyzed. For each, representative techniques, reported limitations, and scenario-appropriate use are summarized. Cross-scenario trade-offs are made explicit. Key examples include scalability vs. energy efficiency and centralized vs. decentralized (hybrid) architectures. The review also links offline pre-planning to online execution through architecture choices, digital-twin validation, and safety-aware collision avoidance in cluttered airspace. Unlike prior algorithm-centric or bibliometric surveys, this work applies a scenario-conditioned taxonomy, ties best-suited method families to each populated cell, and surfaces reported limitations alongside trade-offs. The result is deployment-oriented guidance that maps methods to mission context. Finally, five near-term priorities are highlighted: (i) compute-aware real-time adaptivity on resource-constrained platforms; (ii) scalable multi-objective scheduling with coupled motion and cooperative control; (iii) bandwidth-aware, conflict-resilient intra-swarm communication with reliability guarantees; (iv) certifiable planning for dense urban low-altitude corridors; and (v) energy-aware, hierarchical planners that couple offline pre-planning with online replanning.

In recent years, the number of electric vehicles in China has continued to rise, and the charging network for electric vehicles has developed rapidly. This paper studies the development characteristics of the electric vehicle charging network business market and forms a charging network policy environment that includes national and local policies. Then, use policy tools and divide policy tools into supply, environment and demand. Finally, based on the policy influence from the perspective of policy tools, the corresponding development suggestions are put forward.

During the overall design phase of solar-powered unmanned aerial vehicles (UAVs), a large amount of high-fidelity (HF) propeller aerodynamic performance data is required to enhance design performance, but the acquisition cost is prohibitively expensive. To improve model accuracy and reduce modeling costs, this paper constructs a multi-fidelity aerodynamic data fusion model by associating data with different fidelity. This model utilizes a low-fidelity computational method to quickly determine the design space. The constrained Latin hypercube sampling based on the successive local enumeration (SLE-CLHS) method and the expected improvement (EI) criterion were adopted to achieve the efficient initialization and fastest convergence of the Co-Kriging surrogate model within the design space. This modeling framework was applied to acquire the aerodynamic performance of high-altitude propellers, and the model was evaluated using various performance indicators. The results demonstrate that the proposed model has excellent predictive performance. Specifically, when the surrogate model was constructed using 350 high-fidelity samples, there were improvements of 13.727%, 12.241%, and 5.484% for thrust, torque, and efficiency compared with the surrogate model constructed from low-fidelity samples.