Driven by the critical demand for efficient and compliant aerial operations in urban public health-particularly for vector-borne disease control-this study proposes a wind-sensitive trajectory optimization framework for multi-drone ultra-low volume (ULV) spraying. By integrating a Wind-Induced Stretched Deposition Footprint (WISDF) model with pesticide efficacy decay, we construct a unified objective that maximizes coverage and deposition uniformity while minimizing overspray risk and energy consumption. To tackle the high-dimensional constrained optimization, we develop an enhanced Collaborative Grey Wolf Optimization (C-GWO+) algorithm incorporating Opposition-Based Learning initialization, a cooperative sub-swarm mechanism, and a stagnation reset strategy to strengthen global exploration and mitigate premature convergence. Comprehensive experiments in complex wind fields show that C-GWO+ achieves superior solution quality, outperforming state-of-the-art meta-heuristics such as PSO and SSA by 5.7% and 16.3% in comprehensive fitness, respectively. From an engineering perspective, compared to the Lawnmower baseline, C-GWO+ reduces the operational energy proxy by approximately 43% while ensuring superior deposition consistency. Wilcoxon signed-rank tests against standard GWO confirm statistically significant improvements (p < 0.001) in coverage rate, uniformity, and safety compliance (overspray control), providing a robust and low-carbon solution for precise urban epidemic prevention.
Zheng et al. (Tue,) studied this question.