基于改进遗传算法的多无人机农业测绘协同路径建模OA

To address challenges in multiple unmanned aerial vehicle(UAVs)agricultural mapping such as poor path coordination,weak obstacle avoidance capability in complex farmland environments,and difficulty in balancing mapping accuracy and efficiency,a dual approach was proposed through mathematical modeling and intelligent algorithm optimization.A large-scale farmland was selected as research scenario,by integrating crop type zoning,field heterogeneous obstacles,and differentiated mapping requirements to con-struct a multi-constraint and multi-objective cooperative path planning model.Building upon conventional genetic algorithms,an im-proved genetic algorithm was developed by incorporating a crop zoning weight matrix to optimize fitness function and designing a region-continuity crossover operator.Simulations were conducted in a 10 km×10 km agricultural planting area,comparing improved genetic al-gorithm with standard genetic algorithm(SGA)and non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).Results demonstrated that improved genetic algorithm significantly achieved an average total path length of 31.8±1.2 km,with coverage completeness reaching 99.3％±0.2％,and obstacle avoidance success rate at 100％while maintaining convergence speed at an average of 105 generations.Res-ults significantly outperformed comparison algorithms,achieving a 100％resolution compliance rate in facility agriculture zones.An ef-ficient and reliable technical framework for multi-UAVs cooperative mapping in precision agriculture scenarios was provided.