田间喷雾器液体农药补充管自动对接系统的设计与试验

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Yuanyuan Gao , Kangyao Feng , Xinhua Wei , Jingkai Liu , Xin Han , Yongyue Hu , Shengwei Lu , Liping Chen
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引用次数: 0

摘要

田间喷雾器受到农药罐容量的限制。在连续作业过程中,作业过程需要频繁中断,以进行工位补给或人工辅助补给,大大减少了作业面积和效率。针对大规模养殖场人工补药液效率低、药液补药自动化程度不高的问题,本研究针对田间喷雾器开发了一种药液补药管(LPRT)自动对接系统。通过建立对接机械臂的运动学模型,进行了运动学工作空间分析和机器人结构设计。根据补料作业的要求,改进了农药罐灌装口的结构,提出了一种基于yolov5的农药罐灌装口识别定位方法。此外,设计了LPRT的灵活制导和自动收放机构,以及基于视觉伺服的精确对接算法。制定了基于CAN总线的各模块之间的通信协议,开发了现场喷雾器自动补料监控系统。通过实际实验验证了系统的性能。视觉识别试验表明,改进算法在不同光照条件下的识别置信度均超过0.95,能够适应野战复杂场景需求。农药罐灌装口识别性能测试结果表明,该农药罐灌装口识别算法在灌装作业范围内具有较好的适用性和稳定性,识别距离为30 - 60 cm,识别角度在30°以上时效果最佳。进一步的野外对接试验表明,DRA的平均工作时间为35.64 s,位置重复定位精度为4.12 cm,错误率为8%,满足了野外喷雾器与补给车之间的自动补给要求。本研究为提高田间喷雾器补药自动化程度,从而提高无人农业应用水平提供了切实可行的解决方案和技术参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and experiment of automatic docking system for liquid pesticide replenishment tube of field sprayer
Field sprayers are constrained by the capacity of their pesticide tanks. During continuous operation, the operation process needs to be frequently interrupted for station-based replenishment or manual-assisted replenishment, significantly reducing the operational area and efficiency. To address the low efficiency of artificial replenishment of liquid pesticide and insufficient automation of liquid medicine replenishment in large-scale farms, this study develops an automatic docking system for liquid pesticide replenishment tube (LPRT) targeting the field sprayer. The kinematic workspace analysis and robotic structure design were carried out by establishing the kinematic model of the docking robotic arm (DRA). Based on the requirements of replenishment operations, the structure of the pesticide tank filling opening is improved, and a YOLOv5s-based recognition and positioning method for the pesticide tank filling opening is proposed. Additionally, a flexible guidance and automatic retraction mechanism for the LPRT was designed, along with a visual servo-based precise docking algorithm. The communication protocol between each module based on the CAN bus is formulated, and the automatic replenishment monitoring system for the field sprayer is developed. The system’s performance is tested through actual experiments. The visual recognition tests showed that the improved algorithm achieved a recognition confidence level exceeding 0.95 under different lighting conditions, which can adapt to the complex scene requirements of field operations. The performance test results of the pesticide tank filling opening recognition show that the pesticide tank filling opening recognition algorithm has good applicability and stability within the replenishment operation range, the best effect is achieved when the recognition distance is 30–60 cm and the recognition angle is above 30°. Further field docking tests revealed that the average operation time of the DRA is 35.64 s, the position repeated positioning accuracy is 4.12 cm, and the error ratio is 8 %, meeting the requirements for automated replenishment between the field sprayer and the replenishment vehicle. This study provides a practical solution and technical reference for improving the automation of pesticide replenishment in field sprayers, thereby enhancing the level of unmanned agricultural applications.
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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