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元启发式算法在植保无人机路径规划中的研究进展

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随着无人机系统技术、传感器技术和人工智能算法等相关技术的不断发展,植保无人机路径规划技术在农业生产中备受重视,并被广泛应用。作为航空植保的关键技术之一,植保无人机路径规划面临的是一个复杂且多约束的组合优化问题。传统算法往往难以得到理想的结果,而元启发式算法则因其高效率成为解决该类优化问题的有效手段。为此,首先介绍了农业航空中的路径规划,随后总结出了植保无人机路径规划的关键要素,并使用更为合理的分类方式进行归纳;其次,从算法层面对相关研究所采用的元启发式算法进行分类和梳理,并阐述了其在实际应用中的现状,且根据提出的分类方法和研究特点,全面地归纳了当前的研究成果;最后,针对目前植保无人机路径规划研究存在的问题提出了几条可行的发展思路。
Research Progress of Metaheuristic Algorithm in Path Planning of Plant Protection UAV
With the continuous development of unmanned system technology,sensor technology and artificial intelligence algorithms and other related technologies,the path planning technology of plant protection UAV is highly valued and widely used in agricultural production.As one of the key technologies of aerial plant protection,plant protection UAV path planning faces a complex and multi-constrained combinatorial optimization problem.Traditional algorithms are often difficult to obtain the desired results,while metaheuristic algorithms become an effective means to solve such optimization problems due to their high efficiency and speed.This paper first introduced path planning in agricultural aviation,and then summarized the key elements of path planning for plant protection UAV and generalized them using a more reasona-ble classification.Next,the metaheuristic algorithms used in related research were classified and sorted out at the algo-rithmic level,and their current status in practical applications was described.The current research results were compre-hensively summarized according to the proposed classification methods and research characteristics.Finally,several feasi-ble development ideas were proposed to address the current problems of plant protection UAV path planning research.

agricultural aviationplant protection UAVpath planningmetaheuristic algorithmrural revitalizationagricultural modernization

张旭东、于丽娅、李少波、张安思、张保

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贵州大学 机械工程学院,贵阳 550025

贵州大学 省部共建公共大数据国家重点实验室,贵阳 550025

农机航空 植保无人机 路径规划 元启发式算法 乡村振兴 农业现代化

2025

农机化研究
黑龙江省农业机械工程科学研究院 黑龙江省农业机械学会

农机化研究

北大核心
影响因子:0.668
ISSN:1003-188X
年,卷(期):2025.47(3)