Optimization of ultra-low volume spray for multi-drone based on wind sensitivity

Area of Science:

• Robotics and Automation
• Environmental Science
• Public Health

Background:

• Urban public health requires efficient aerial operations for vector-borne disease control.
• Existing methods for drone-based pesticide application face challenges in complex wind conditions and require optimization for coverage and safety.
• Ultra-low volume (ULV) spraying necessitates precise trajectory planning to maximize efficacy and minimize environmental impact.

Purpose of the Study:

• To develop a wind-sensitive trajectory optimization framework for multi-drone ULV spraying in urban environments.
• To integrate a Wind-Induced Stretched Deposition Footprint (WISDF) model with pesticide efficacy decay into a unified optimization objective.
• To enhance operational efficiency, coverage, deposition uniformity, and safety while minimizing overspray and energy consumption.

Main Methods:

• Development of a wind-sensitive trajectory optimization framework for multi-drone ULV spraying.
• Integration of the WISDF model and pesticide efficacy decay into a unified objective function.
• Implementation of an enhanced Collaborative Grey Wolf Optimization (C-GWO+) algorithm with Opposition-Based Learning, cooperative sub-swarms, and stagnation reset strategies.
• Experimental validation in complex wind fields comparing C-GWO+ against PSO, SSA, and a Lawnmower baseline.

Main Results:

• The C-GWO+ algorithm achieved superior solution quality, outperforming PSO by 5.7% and SSA by 16.3% in comprehensive fitness.
• Compared to the Lawnmower baseline, C-GWO+ reduced operational energy by approximately 43% while maintaining deposition consistency.
• Statistical analysis confirmed significant improvements (p < 0.001) in coverage rate, uniformity, and safety compliance (overspray control) compared to standard GWO.

Conclusions:

• The proposed wind-sensitive trajectory optimization framework and C-GWO+ algorithm provide a robust and low-carbon solution for precise urban epidemic prevention.
• The integrated approach effectively balances coverage, deposition uniformity, and safety compliance in challenging wind conditions.
• This study offers a significant advancement in drone-based public health interventions, particularly for vector-borne disease control in urban settings.