基于改进麻雀搜索算法和贝塞尔曲线的无人农场机器人路径规划方法  

Path Planning of Robot Based on Improved Sparrow Search Algorithm and Bessel Curve

作　　者：陆健强 陈祖城 兰玉彬 童海洋 鲍国庆 周正扬 郑佳祺 LU Jianqiang;CHEN Zucheng;LAN Yubin;TONG Haiyang;BAO Guoqing;ZHOU Zhengyang;ZHENG Jiaqi(State Key Laboratory of Agricultural Equipment Technology,Guangzhou 510642,China;College of Electronic Engineering(College of Artificial Inteligence),South China Agricultural University,Guangzhou 510642,China;National Center for International Research on Precision Agricultural Aviation Pesticide Spraying Technology,Guangzhou 510642,China)

机构地区：[1]农业装备技术全国重点实验室,广州510642 [2]华南农业大学电子工程学院(人工智能学院),广州510642 [3]国家精准农业航空施药技术国际联合研究中心,广州510642

出　　处：《农业机械学报》2025年第2期115-123,共9页Transactions of the Chinese Society for Agricultural Machinery

基　　金：广东省农业人工智能重点实验室开放课题(GDKL-AAL-2023007);广东省重点领域研发计划项目(2023B0202090001);广州市重点研发计划项目(2023B0311392);华南农业大学农业装备技术全国重点实验室开放基金项目(SKLAET-202412)。

摘　　要：优化无人农场作业路径用以提升农田管理效率和资源利用率是移动机器人导航领域的研究热点,针对传统麻雀搜索算法(Sparrow search algorithm,SSA)和强化学习算法搜索效率低、路径不够光滑容易陷入局部最优的问题,本文设计了一种融合改进IQL(Improved Q-learning)算法的改进麻雀搜索算法(Improved sparrow search algorithm,ISSA),结合贝塞尔曲线用于移动机器人的全局路径规划。首先,在算法初期采用多策略初始化种群,将IQL算法与Logistic混沌映射和拉丁超立方抽样(Latin hypercube sampling,LHS)方法相结合,为种群提供优良性和多样性的初始解;其次,将线性动态惯性权重调整方法引入到发现者位置更新中,平衡算法的全局搜索能力和局部开发能力、提升算法收敛速度;然后,在警戒者中引入反向学习策略进一步探索未开发区域,防止陷入局部最优解;最后,结合避障算法和贝塞尔曲线对路径进行平滑处理,消除行驶路径距离障碍物过近和路径不平滑问题。通过在Matlab平台上进行对比仿真试验,验证ISSA算法的有效性和优越性。试验结果表明,ISSA算法有效地结合IQL算法的自学习特性和SSA算法的强大搜索能力,在网格仿真环境和实地场景下均显著提高了全局路径优化效率,生成的路径更加平滑。在实地场景下,ISSA算法相较于SSA和ACO算法,路径规划时间分别减少64.43%、9.94%,平均最短路径长度分别减少8.3%、12%。研究可为无人农场机器人精准、高效作业提供优质的路径规划方案。Optimizing unmanned farm paths to improve farm management efficiency and resource utilization is a hot research topic in the field of mobile robot navigation.An improved sparrow search algorithm(ISSA)incorporating improved Q-learning(IQL)algorithm was designed to address the problems of low search efficiency and smooth paths that can easily fall into local optimization of traditional sparrow search algorithm(SSA)and reinforcement learning algorithm.ISSA incorporating the improved IQL algorithm was designed for global path planning of mobile robots in combination with Bessel curves.Firstly,a multi-strategy initialization of the population was used at the beginning of the algorithm,combining the IQL algorithm with Logistic chaos mapping and Latin hypercube sampling(LHS)methods to provide excellent and diverse initial solutions for the population;secondly,a linear dynamic inertia weight adjustment method was introduced into the finder position updating to balance the algorithm's global search capability and local exploitation capability,and improve the convergence speed of the algorithm;then,the reverse learning strategy was introduced into the vigilant to further explore the unexplored area and prevent falling into the local optimal solution;finally,the path was smoothed by combining obstacle avoidance algorithms and Bessel curves to eliminate the problems of traveling paths too close to obstacles and unsmooth paths.The effectiveness and superiority of ISSA algorithm was verified through comparative simulation tests on Matlab platform.The experimental results showed that the ISSA algorithm effectively combined the self-learning characteristics of the IQL algorithm and the powerful search capability of the SSA algorithm,which significantly improved the efficiency of global path optimization and generated smoother paths in both the grid simulation environment and the field scenario.In the field scenario,the ISSA algorithm reduced the path planning time by 64.43%and 9.94%,and the average value of the shortest path len

关 键 词：无人农场 路径规划 改进麻雀搜索算法 贝塞尔曲线 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]