Cooperative path modeling for multiple unmanned aerial vehicles in agricultural mapping based on improved genetic algorithm

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 construct a multi-constraint and multi-objective cooperative path planning model．Building upon conventional genetic algorithms, an improved 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 algorithm 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．Results significantly outperformed comparison algorithms, achieving a 100% resolution compliance rate in facility agriculture zones．An efficient and reliable technical framework for multi-UAVs cooperative mapping in precision agriculture scenarios was provided．