| 基于改进纯追踪模型的农机路径跟踪算法研究 |
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| 引用本文： | 张华强,王国栋,吕云飞,秦昌礼,刘林,宫金良.基于改进纯追踪模型的农机路径跟踪算法研究[J].农业机械学报,2020,51(9):18-25. |
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| 作者姓名： | 张华强 王国栋 吕云飞 秦昌礼 刘林 宫金良 |
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| 作者单位： | 山东理工大学机械工程学院,淄博255049;山东理工大学生态无人农场研究院,淄博255049;山东理工大学机械工程学院,淄博255049 |
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| 基金项目： | 山东省引进顶尖人才“一事一议”专项经费项目和中央引导地方科技发展专项资金项目 |
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| 摘 要： | 为提高农机作业时直线行驶的精度,提出了一种基于改进纯追踪模型的农机路径跟踪算法。在建立了运动学模型和纯追踪模型的基础上,对农机直线跟踪方法进行研究;针对GPS导航精度易受噪声干扰的问题,通过卡尔曼滤波对航向误差以及横向误差进行了平滑处理,以获取更高精度的航向误差和横向误差;为提高纯追踪模型的自适应能力,以横向误差和航向误差的均方根误差为基础,构建适应度函数,并设计了权重函数,采用横向误差作为主要决策参数,通过粒子群优化(Particle swarm optimization,PSO)算法实时确定纯追踪模型中的前视距离;为使粒子群减少计算时间、尽快进行局部搜索,对PSO算法中惯性权重系数进行了改进。以东方红1104-C型拖拉机为试验平台,设计了农机自动导航控制系统,进行了农田播种试验。结果表明:当农机行驶速度为0.7 m/s时,采用基于改进纯追踪模型的农机路径跟踪算法,直线跟踪的最大横向误差为0.09 m;当行驶距离超过5 m后,最大横向误差为0.02 m,该算法能够有效地提高农机作业时的直线行驶精度。
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| 关 键 词： | 农业机械 自动导航控制 改进纯追踪模型 粒子群算法 卡尔曼滤波 前视距离 |
| 收稿时间： | 2019/12/12 0:00:00 |
Agricultural Machinery Automatic Navigation Control System Based on Improved Pure Tracking Model |
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| Institution: | Shangdong University of Technology |
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| Abstract: | In order to improve the accuracy of driving straight when agricultural machinery works, an agricultural machinery path tracking algorithm based on an improved pure tracking model was designed. The linear tracking method of agricultural machinery was studied, which was based on the establishment of agricultural machinery kinematics model and pure tracking model. Aiming at the problem that GPS navigation accuracy was susceptible to noise interference, the Kalman filter was used to smooth the heading error and the lateral error so that higher accuracy heading and lateral errors can be obtained. In order to improve the adaptive ability of the pure tracking model, a fitness function was constructed based on the root mean square error of the lateral error and the heading error, and a weight function was designed, and the lateral error was used as the main decision parameter to determine the forward view in the pure tracking model in real time distance. In order to reduce the calculation time of the particle swarm, which the local search of the particle swarm can be performed as soon as possible, the inertia weight coefficient in the particle swarm optimization (PSO) algorithm was improved. When conducting field planting trials, an automatic navigation control system for agricultural machinery was designed where Dongfanghong 1104-C was used as the experimental platform. Seeding experiment proved that: when the path tracking algorithm based on improved pure tracking model was adopted, the agricultural machinery travel speed was 0.7m/s, the maximum lateral error of the linear tracking was 0.09m;when the driving distance exceeded 5m, the maximum lateral error was 0.02m. The proposed improved pure tracking model had good applicability to the automatic navigation control of agricultural machinery, which can effectively improve the straight-line driving accuracy during agricultural machinery operation. |
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| Keywords: | agricultural machinery automatic navigation control improved pure tracking model particle swarm optimization Kalman filter forward-looking distance |
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