基于混合蚁群算法的无人化农机路径寻优研究  

Research on path optimization of unmanned agricultural machinery based on hybrid ant colony algorithm

作　　者：杨会甲 张亚军 王鹏杰 王东 王亚平[1] YANG Hu-jia;ZHANG Ya-jun;WANG Peng-jie;WANG Dong;WANG Ya-ping(Xi’an Aerospace Automation Co.,Ltd.,Xi’an 710065,China;Shaanxi Union Research Center of University and Enterprise for Smart Agriculture Data Processing and Service,Xi’an 710065,China;College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100,Shaanxi,China)

机构地区：[1]西安航天自动化股份有限公司,西安710065 [2]陕西省“四主体一联合”智慧农业数据处理与服务校企联合研究中心,西安710065 [3]西北农林科技大学机械与电子工程学院,陕西杨凌712100

出　　处：《湖北农业科学》2024年第8期247-251,共5页Hubei Agricultural Sciences

基　　金：陕西省科技厅重点产业链提升计划项目(2020zdzx03-04-02)。

摘　　要：针对智慧农业中复杂环境下无人化农机路径规划寻优过程中存在的迭代速度慢、路径安全性较低等问题,融合人工势场、量子行为以及基于B样条的平滑策略提出了混合蚁群算法。该方法在迭代初期引入人工势场法,以解决迭代速度慢问题以及实现全局最优平衡;在路径寻优的中期加入量子行为优化信息密度阈值,改进算法状态选择概率,避免算法陷入局部最优,以提高获取优质解的能力;在迭代后期融合基于B样条的平滑策略,优化最优路径,提高无人化农机避障能力。仿真试验结果表明,基于混合蚁群算法的无人化农机在复杂环境作业时,路径寻优能力得到有效提升,路径优化响应速度提升了73倍,路径优化后距离缩短超过11.8%。In addressing the challenges of slow iteration speed and low path safety in the optimization process of unmanned agricultural machinery path planning under complex environments in smart agriculture,a hybrid ant colony algorithm was proposed,integrating ar⁃tificial potential fields,quantum behavior and a B-spline-based smoothing strategy.This method introduced artificial potential fields in the early iterations to address the issues of slow iteration speed and balance global optimality.In the mid-term of path optimization,quantum behavior was incorporated to enhance the algorithm’s capability to obtain high-quality solutions by adjusting the information density threshold,improving algorithm state selection probabilities,and avoiding local optima.In the later stages of iteration,the Bspline-based smoothing strategy was integrated to optimize the optimal path and enhance the obstacle avoidance capability of un⁃manned agricultural machinery.Simulation experiment results demonstrated that the unmanned agricultural machinery based on the hy⁃brid ant colony algorithm showed significantly improved path optimization ability in complex environments.The response speed of path optimization was increased by 73 times,and the distance was reduced by over 11.8%after path optimization.

关 键 词：智慧农业 无人化农机 路径寻优 混合蚁群算法 避障 人工势场 

分 类 号：TP249[自动化与计算机技术—检测技术与自动化装置] S252[自动化与计算机技术—控制科学与工程]