Low-altitude wind fields significantly increase the complexity of multi-UAV cooperative path planning (MUAVCP) by affecting flight time, energy consumption, trajectory feasibility, and arrival coordination. To address this problem, this study establishes a three-dimensional MUAVCP model considering kinematic constraints, no-fly zones, temporal coordination, inter-UAV separation, and multiple mission-related costs. An improved hybrid Rime–Snow Ablation Optimizer (IHRSO) is proposed. IHRSO employs the previously developed IRIME as the primary search component and a normal-cloud-model-enhanced SAOHTC (NCM-SAOHTC) as the auxiliary component. The two components are coordinated through an adapted stagnation-triggered mechanism that activates auxiliary search after insufficient primary-search improvement and subsequently returns the shared elite to IRIME for further refinement. IHRSO is evaluated on the CEC2017 and CEC2022 benchmark suites and several multi-UAV path-planning scenarios under low-altitude wind conditions. Ablation experiments show that IHRSO achieves the best Friedman mean rank of 1.375 and reduces optimization error by an average of 37.30% compared with fixed-period switching. In multi-UAV simulations, IHRSO consistently generates high-quality coordinated trajectories with lower mission delays and planning costs. In the large-fleet experiments, IHRSO maintains favorable solution quality and mission-delay performance as the fleet size increases to 20 UAVs, although the computational cost rises substantially with problem dimensionality.

Road-transport electrification and driving automation increasingly connect vehicle actions with charging services and urban electricity demand. This review combines two Web of Science Core Collection result sets with abstract-level term analysis and a structured synthesis of technical studies to examine that connection. The broader searches yielded 60,043 and 16,458 records, respectively; recent exports contained 10,926 and 4502 records. Rule-based analysis of nonempty abstracts and the technical synthesis identify four intersections: energy-aware route-charging decisions, station selection and automated access, fleet scheduling, and coupled evaluation. Across the studies examined, vehicle execution, station-session events, and distribution constraints are often represented at different boundaries. The resulting evidence supports a common evaluation of mobility, charging service, and feeder operation, with the five-stage pathway serving as a framework for progressively stronger validation. In a two-hour extreme-condition illustration, on-time charging completion rises from 75.5% without guidance to 97.1% with authorized automated execution, while total vehicle travel time decreases. The review thus identifies measurable interfaces for testing cross-system coordination while keeping field deployment claims tied to their validation level.

Physics-informed neural networks (PINNs) approximate partial differential equations (PDEs) by enforcing governing equations and boundary conditions during training, but their accuracy depends on how collocation points are distributed and updated. We propose spatiotemporal compositional active sampling (STCAS), a reference-assisted offline configuration procedure that uses an analytic or high-accuracy numerical solution to rank complete three-stage sampling plans. It screens eight fixed rules, forms a task-specific shortlist, and evaluates bounded fixed, switched, and locally blended plans with independent selection sets and a composition guard. A safety-anchor decision retains the standard PINN unless the selected candidate is at least 5% better. Across five evaluations on 18 analytically specified two-dimensional Poisson tasks, this protocol improves 16 task means and ties two, reducing aggregate relative-$L_2$ error by 12.8% (hierarchical-bootstrap 95% interval $[6.78\%,19.32\%]$; one-sided paired Wilcoxon $p=2.19\times {10}^{-4}$). Against the confirmed fixed plan, aggregate error decreases by 8.2%. In comparison experiments designed for two transfer tasks and matched for main PINN training budgets, STCAS achieves the lowest aggregate mean reported error among the compared methods for both a steady convection–diffusion equation and a nonlinear time-dependent Burgers equation; its offline search cost is additional.

Variable-rate pesticide spraying and fertilizer application require coordinated operation of heterogeneous agricultural machines, particularly in irregular fields where task demands, vehicle mobility, payload capacity, energy consumption, and resupply requirements vary spatially. However, most existing studies optimize aerial spraying or ground fertilization separately and do not jointly consider prescription-map demands, air–ground synchronization, pesticide-drift risk, and agricultural vehicle constraints. This study formulates collaborative UAV spraying and UGV fertilization as a multi-objective mixed-integer nonlinear programming problem with three objectives: minimizing system makespan, weighted energy consumption, and pesticide-drift penalty. A prescription-map-guided bi-level planning framework is proposed. At the upper level, the problem-specific TNSAOO solver determines UAV and UGV task sequences and collaborative resupply-point activation. At the lower level, adaptive Theta* and row-constrained Hybrid A* generate UAV spraying and UGV fertilization trajectories, respectively, while prescription-dependent application commands are assigned along active operation segments and a time-window mechanism detects and corrects residual air–ground conflicts. The framework was evaluated using 30 real farmland boundaries and 90 randomized prescription scenarios. Mean geometric coverage rates reached 98.82% for UAV spraying and 98.95% for UGV fertilization, while the mean prescription-compliance errors were 6.21% and 2.13%, respectively. In addition, 96.7% of the batch runs contained no more than one detected air–ground conflict, with a mean corrective waiting time of 1.07 s. Compared with traditional independent operation, collaborative planning reduced mean system makespan by 9.32%, weighted energy consumption by 7.21%, modeled drift penalty by 3.62%, and total path length by 5.79%. In the multi-objective comparison, TNSAOO obtained a mean hypervolume of 0.597 and a mean inverted generational distance of 0.375, showing competitive Pareto-search performance relative to established comparison algorithms, particularly NSGA-II. Additional terrain and drift sensitivity analyses produced systematic changes in energy, completion time, and modeled drift risk under controlled parameter perturbations. These findings demonstrate the simulation-based feasibility of jointly planning heterogeneous variable-rate spraying and fertilization under a shared prescription map. Physical field experiments remain necessary to validate spray deposition, fertilizer-distribution uniformity, terrain effects, and model calibration under environmental uncertainty.