摘要
针对植保机械在大田对靶施药过程中雾滴沉积偏移导致准确率低的问题,本文基于大田对靶施药机器人,开展了雾滴定向沉积控制方法研究。以对靶喷施雾滴群体为研究对象,阐述对靶施药工作原理和作业特点,剖析雾滴群体沉积偏移成因;根据机器人空间结构位置关系,建立基于GNSS/IMU卡尔曼滤波信号的喷头运动状态感知模型与沉积位置预估模型,确定喷头响应控制准则;在此基础上以机车作业速度、喷头控制方法为试验因素,以对靶喷施准确率和沉积偏移距离为试验指标,开展平整场地对靶喷施模拟试验;选取满足作业需求的组别进行大田对靶施药作业验证。试验表明:作业速度0.5、1.0、1.5、2.0 m/s下的平整地面对靶施药平均准确率分别为99.8%、98.4%、95.9%、76.5%,沉积偏移距离分别为3.8、5.4、7.5、10.0 cm;作业速度0.5、1.0、1.5 m/s下的田间对靶施药准确率分别为98.7%、96.7%、95.3%。结果表明,基于GNSS/IMU卡尔曼滤波的雾滴沉积位置预估喷头控制方法,满足大田对靶施药作业需求。
In order to solve the problem that the accuracy of plant protection machinery in the process of target application in large fields is reduced due to the drift of fog droplet deposition,a research on the method of fog droplet directional deposition control was carried out based on the robot of target application in large fields.Based on the target spraying droplet population,the working principle and operational characteristics of target application were explained,and the causes of droplet population deposition shift were analyzed.Combined with the prescription chart target position information to control the nozzle for fast action,the target spraying simulation test was carried out on a leveled field with target spraying accuracy and deposition offset distance as test factors,and the group meeting the operational requirements was selected for the verification of target application in a large field.The test results showed that the average accuracy of target application on level ground at 0.5 m/s,1.0 m/s,1.5 m/s and 2.0 m/s was 99.8%,98.4%,95.9%and 76.5%,respectively,and the deposition offset distance was 3.8 cm,5.4 cm,7.5 cm and 10.0 cm,respectively.The field-to-target application accuracies of the operating speeds were 98.7%,96.7%,and 95.3%at speeds of 0.5 m/s,1.0 m/s and 1.5 m/s,respectively.The results showed that the nozzle control method based on GNSS/IMU Kalman filter for droplet deposition position prediction can meet the demand of target application in large fields.This target application simulation method allowed accurate recording of target application accuracy and droplet deposition offset distances in a near natural environment.
作者
李海龙
权龙哲
皮鹏飞
郭英豪
马震
余涛
LI Hailong;QUAN Longzhe;PI Pengfei;GUO Yinghao;MA Zhen;YU Tao(College of Engineering,Northeast Agricultural University,Harbin 150030,China;Anhui Province Engineering Laboratory of Intelligent Agricultural Machinery and Equipment,Hefei 230036,China;School of Engineering,Anhui Agricultural University,Hefei 230036,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2022年第6期102-109,258,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(52075092)
黑龙江省博士后落地项目(LBHQ19007)。
关键词
对靶施药
雾滴粒群
沉积控制
信号融合
位置估计
targeted spraying
fog droplet particle population
deposition control
signal fusion
position estimation
