欢迎您访问《智慧农业(中英文)》官方网站! English

Smart Agriculture ›› 2020, Vol. 2 ›› Issue (4): 89-102.doi: 10.12133/j.smartag.2020.2.4.202010-SA002

• 专刊--农业机器人与智能装备 • 上一篇    下一篇

轮式谷物联合收获机视觉导航系统设计与试验

丁幼春1,2(), 王绪坪1,2, 彭靖叶1,2, 夏中州1,2   

  1. 1.华中农业大学 工学院,湖北 武汉 430070
    2.农业农村部长江中下游农业装备重点实验室,湖北 武汉 430070
  • 收稿日期:2020-10-14 修回日期:2020-12-08 出版日期:2020-12-30
  • 基金资助:
    国家重点研发计划项目(2017YFD0700400);湖北省重点研发计划项目(2020BAB097)
  • 通信作者:

Visual Navigation System for Wheel-Type Grain Combine Harvester

DING Youchun1,2(), WANG Xuping1,2, PENG Jingye1,2, XIA Zhongzhou1,2   

  1. 1.College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
    2.Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
  • Received:2020-10-14 Revised:2020-12-08 Online:2020-12-30

摘要:

为提高联合收获机收获质量与效率,构建了轮式谷物联合收获机视觉导航控制系统,结合OpenCV设计了谷物收获边界直线检测算法识别水稻田间已收获区域与未收获区域边界,经预处理、二次边缘分割和直线检测等得到联合收获机视觉导航作业前视目标路径,并根据前视路径相对位置信息进行田间动态标定获得联合收获机满幅收获作业状态;提出了一种基于前视点的直线路径跟踪控制方法,通过预纠偏控制实现维持满割幅的同时防止作物漏割,以相对位置偏差值和实时转向后轮转角作为视觉导航控制器的输入,并根据纠偏策略对应输出转向轮控制电压大小。稻田试验结果表明,该导航系统实现了轮式联合收获机田间相对位置姿态的可靠采集及目标直线路径跟踪控制的稳定执行,在田间照度符合人眼正常工作的情况下,收获边界识别算法检测准确率不低于96.28%,单帧检测时间50 ms以内;以不产生漏割为前提的视觉导航平均割幅率为94.16%,随作业行数增多,割幅一致性呈提高趋势。本研究可为联合收获机自动导航满割幅作业提供技术支撑。

关键词: 视觉导航, 联合收获机, 跟踪控制, 图像处理, 割幅率, 前视点

Abstract:

Due to the short harvest period after grain ripening, the heavy of harvest tasks, the complicated operation of the combined harvester and the high driver's labor intensity, it is difficult to maintain the consistency of high cutting rate and cutting width of combined harvester. Based on computer vision, the combined harvester navigation method is being used to realize real-time control of the cut width of the combine harvester. To improve the working quality and efficiency of the wheel grain combine harvest, visual navigation control system was built in this research, it was composed of industrial camera, rear wheel angle measurement device, electro-hydraulic steering valve set, control box and notebook computer, etc. The industrial camera was mounted on the light frame of the front left turn of the combine harvester to capture the image information of the front field view of the harvester. The rear wheel angle measurement device was installed on the steering wheel bridge of the harvester, and the change and position of the rear wheel angle were fed back in real time. The steering cylinder was directly driven bypass connection way outside the electro-hydraulic steering valve set to make the rear wheel of the harvester turning. The control box integrated data acquisition card, power supply module, power amplifier, etc., for steering wheel control and steering wheel angle information measurement. As the main controller of the system, the notebook computer run the visual navigation software for image processing and deviation correction control, realized the automatic navigation process control of the combined harvester. Combined with OpenCV, a target path detection algorithm was designed to identify the boundary of harvested and non harvested paddy fields. After preprocessing, secondary edge segmentation and line detection, the forward-looking target path of visual navigation operation of combine harvester was obtained, and field dynamic calibration was carried out according to the relative position information of forward-looking path to obtain the full range harvesting status of combine harvester. A visual navigation tracking control method based on the front viewpoints was proposed in this research, through rectifying control implemented to maintain full cutting length at the same time prevent the leakage cut of crops, with full cut deviation, the front viewpoints pixel deviation and real-time steering rear corner as visual navigation controller input. The output steering wheel voltage was controlled according to the rectifying strategy. The results of paddy field experiments showed that the navigation system could basically realize the reliable acquisition of the position and the stable implementation of the target linear path tracking control. Under the condition that the field illumination conforming to the normal operation of human eyes, the detection accuracies of the image processing algorithm were higher than 96.28%, and the detection times of a single frame were less than 50 ms. Under the conditions that the nominal cutting width of the harvester was 2.56 m, the average full cutting width rate of visual navigation was 94.16%. Compared with the boundary of the first row, the straightness accuracy of the second row was increased by 44.92%. The research could provide technical support for the combine harvester to automatically navigate the operation of full cutting width.

Key words: visual navigation, combine harvester, tracking control, image processing, cutting length rate, front viewpoint

中图分类号: