帮助 关于我们

返回检索结果

基于视觉伺服的联合收割机群协同导航从机定位方法
Slave positioning method for cooperative navigation of combine harvester group based on visual servo

查看参考文献30篇

文摘 针对收割机群协同导航从机实际定位需求,在分析主机与从机间相对位置关系的基础上,提出了一种采用视觉系统求解靶标投影矩形区域形心坐标,并以此作为导引信息,引导相对位姿测量装置测量从机相对主机位姿,实现从机精确定位的方法。重点对从机定位方法中需解决的运动靶标跟踪和对靶控制2个关键问题进行了研究,提出了一种基于双重波门的运动靶标跟踪方法和一种基于变尺度变论域模糊控制的对靶控制方法。试验结果表明,该文所提定位方法基本不受收割机前进速度的影响,不同速度最大偏差的标准差为0.003 5 m,平均偏差的标准差为0.002 3 m。
其他语种文摘 With the improvement of the farm mechanization level, the more corporate' style farming emerges. For example, more than one harvester collaborated with each other completes the task of harvesting operations. The new corporate style farming creates several new challenges for the agricultural machinery navigation, and the group navigation is the trend of agricultural machinery navigation technology. Group positioning and collaborative navigation control are the 2 critical technical problems to be resolved. In order to solve the slave positioning problem, a slave positioning method based on visual servo was proposed. Firstly, the combine group based on mater-slave structure was introduced, and the slave positioning scheme was proposed. In order to fulfill the automatic measurement of the slave position, the relative positioning device was designed. The structure and measurement process of the positioning device were introduced in detail. The positioning device was composed of laser ranging sensor, PTZ(Pan Tilt Zoom) controller and monocular camera. The monocular camera was used to guide the laser ranging sensor to aim at the target. The laser ranging sensor was used to measure the distance between the master and the slave. The laser ranging sensor guaranteed high precision of the measurement data. The position measurement process could be divided into 2 steps. The first step was to measure the relative position posture between the master and the slave. According to the deviation between the target's center and the image center, the system adjusted the platform's direction to change the posture of the camera, so that the 2 centers in the target and the image could coincide approximately. After aiming at the target, the laser sensor was triggered to measure the distance between the master and the slave. The second step was to calculate the slave positon. A global localization model was built up to show the relative location relationship between the master and the slave, and the slave's coordinate under global coordinate system could be calculated. Secondly, in order to solve the 2 key problems in the process of the position measurement, 2 methods were proposed. A method of automatic identification for motion target based on dual windows was proposed to reduce the visual feedback delay. This target identify method filtered out most of the identified area by setting dual windows, and searched the target point only in a small area of the image. A variable-scale variable-universe fuzzy control method was proposed to improve the target control accuracy. This method introduced the contraction-expansion factor to improve the granularity of fuzzy rules without increasing the number of control rules, and introduced the variable scale factor to improve the adaptability to the change in the distance between the master and the slave. Finally, in order to verify the effectiveness of the proposed model and method in this paper, the slave positioning experiments were carried out. When the slave ran at the speed of 0.8, 1.0 and 1.2 m/s, the average positioning error was 0.127 9, 0.128 6 and 0.132 1 m respectively. The experimental results show that the slave positioning accuracy is independent of the forward speed, and can meet the slave positioning requirement.
来源 农业工程学报 ,2016,32(24):59-68 【核心库】
DOI 10.11975/j.issn.1002-6819.2016.24.008
关键词 农业机械 ; 控制 ; 导航 ; 收割机群 ; 机群定位 ; 视觉伺服 ; 对靶控制
地址

中国科学院沈阳自动化研究所, 中国科学院网络化控制系统重点实验室, 沈阳, 110016

语种 中文
文献类型 研究性论文
ISSN 1002-6819
学科 农业工程
基金 国家863计划 ;  辽宁省科技攻关项目
文献收藏号 CSCD:5888607

参考文献 共 30 共2页

1.  胡静涛. 农业机械自动导航技术研究进展. 农业工程学报,2015,31(10):1-10 被引 106 次    
2.  Shearer S A. Trends in the automation of agricultural field machinery. In Proc. of the 21st Annual Meeting of the Club of Bologna,2010 被引 2 次    
3.  Bochtis D D. Conceptual model of fleet management in agriculture. Biosystems Engineering,2010,105(1):41-50 被引 7 次    
4.  Vougioukas S G. A distributed control framework for motion coordination of teams of autonomous agricultural vehicles. Biosystems Engineering,2012,11(3):284-297 被引 4 次    
5.  Johnson D A. Development and implementation of a team of robotic tractors for autonomous peat moss harvesting. Journal of Field Robotics,2009,26(6/7):549-571 被引 2 次    
6.  姬长英. 农业机械导航技术发展分析. 农业机械学报,2014,45(9):44-54 被引 87 次    
7.  罗锡文. 东方红X-804拖拉机的DGPS自动导航控制系统. 农业工程学报,2009,25(11):139-145 被引 98 次    
8.  李逃昌. 一种与行驶速度无关的农机路径跟踪方法. 农业机械学报,2014,45(2):59-65 被引 17 次    
9.  郭娜. 基于GPS导航的插秧机作业控制系统. 农业机械学报,2013,44(1):200-204 被引 21 次    
10.  周建军. 基于模糊控制的农用车辆路线跟踪. 农业机械学报,2009,40(4):151-156 被引 43 次    
11.  陈军. 基于神经网络的农用车辆自动跟踪控制. 农业机械学报,2007,38(5):131-133 被引 13 次    
12.  刘兆祥. 基于自适应模糊控制的拖拉机自动导航系统. 农业机械学报,2010,41(11):148-152,162 被引 36 次    
13.  刘军. 基于GPS/INS和线控转向的农业机械自动驾驶系统. 农业工程学报,2016,32(1):46-53 被引 16 次    
14.  刘柯楠. 基于GPS的太阳能平移式喷灌机自主导航系统设计与试验. 农业工程学报,2016,32(16):89-94 被引 9 次    
15.  张美娜. 农用车辆自主导航控制系统设计与试验. 农业机械学报,2016,47(7):42-47 被引 18 次    
16.  孟庆宽. 自然光照下基于粒子群算法的农业机械导航路径识别. 农业机械学报,2016,47(6):11-20 被引 24 次    
17.  汤一平. 基于机器视觉的茶陇识别与采茶机导航方法. 农业机械学报,2016,47(1):45-50 被引 13 次    
18.  李景彬. 棉花铺膜播种机导航路线图像检测方法. 农业机械学报,2014,45(1):40-45 被引 16 次    
19.  何洁. 基于边缘检测与扫描滤波的农机导航基准线提取方法. 农业机械学报,2014,45(11):265-270 被引 13 次    
20.  赵颖. 基于机器视觉的耕作机器人行走目标直线检测. 农业机械学报,2006,37(4):81-86 被引 7 次    
引证文献 3 篇

1 张万枝 线性时变模型预测控制器提高农业车辆导航路径自动跟踪精度 农业工程学报,2017,33(13):104-111
被引 11 次

2 刘慧 基于融合深度信息的自动喷雾车全局非线性轨迹优化方法 农业机械学报,2019,50(5):33-42
被引 1 次

显示所有3篇文献

论文科学数据集
PlumX Metrics
相关文献

 作者相关
 关键词相关
 参考文献相关

版权所有 ©2008 中国科学院文献情报中心 制作维护：中国科学院文献情报中心
地址：北京中关村北四环西路33号 邮政编码：100190 联系电话：(010)82627496 E-mail:cscd@mail.las.ac.cn 京ICP备05002861号-4 | 京公网安备11010802043238号