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Smart Agriculture ›› 2023, Vol. 5 ›› Issue (4): 33-44.doi: 10.12133/j.smartag.SA202310004

• 专题--面向智慧农业的人工智能和机器人技术 • 上一篇    下一篇

基于云-端高精度地图的油菜无人播种作业系统设计与试验

卢邦1,2(), 董万静1,2, 丁幼春1,2(), 孙阳1,2, 李浩鹏1,2, 张朝宇1,2   

  1. 1. 华中农业大学 工学院,湖北 武汉 430070,中国
    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,中国
  • 收稿日期:2023-10-07 出版日期:2023-12-30
  • 作者简介:
    卢 邦,研究方向为智能农业装备。E-mail:

    LU Bang, E-mail:

  • 通信作者:
    丁幼春,博士,教授,研究方向为智慧农业技术与装备。E-mail:

An Rapeseed Unmanned Seeding System Based on Cloud-Terminal High Precision Maps

LU Bang1,2(), DONG Wanjing1,2, DING Youchun1,2(), SUN Yang1,2, LI Haopeng1,2, ZHANG Chaoyu1,2   

  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:2023-10-07 Online:2023-12-30
  • corresponding author:
    DING Youchun, E-mail:
  • Supported by:
    National Key Research and Development Program of China(2021YFD2000402); Key Research and Development Project of Hubei Province(2021BBA255)

摘要:

[目的/意义] 针对人工采集南方中小田边界信息操作繁琐、无人拖拉机转场作业效率低且在转弯调头处留下较大漏作业区域的问题,本研究搭建了一种基于云-端高精度地图的油菜无人播种作业系统。 [方法] 系统采用“无人机构建高精度地图+远程网页端规划作业路径”方法,使用无人机采集影像数据构建高精度地图,基于高精度地图框选田块实现梭行外螺旋路径自动生成,完成直播机组远程路径规划及调度作业。系统采用梭行外螺旋全覆盖路径规划方法,利用两退三切鱼尾调头方法完成梭行路径换线。为完成外螺旋路径换线,在田块边角设计了一退两切转弯换线方法,减小了油菜无人播种作业系统在转弯或调头过程产生的漏作业区域面积,进一步提升了播种作业覆盖率,并开展梭行外螺旋全覆盖路径与梭行、套行路径作业面积和作业覆盖率对比仿真试验,以Case TM1404型拖拉机搭载智能播种施肥一体机为试验平台,开展了田间试验。 [结果和讨论] 试验结果表明,梭行外螺旋全覆盖路径较梭行与套行作业路径,漏作业率减小18.58%~26.01%。使用无人机构建的高精度地图平面误差最大为3.23 cm,导航作业过程中最大横向偏差为7.94 cm,最大平均绝对偏差为1.85 cm,作业覆盖率为93.16%。 [结论] 本研究所构建的油菜无人播种作业系统有效可行,可为南方中小田块油菜无人播种作业提供技术参考。未来将探索不规则田块情况下的油菜无人播种作业模式,进一步提高系统适用性。

关键词: 油菜, 无人播种作业, 全覆盖路径, 转弯模型, 高精度地图, 漏作业率

Abstract:

[Objective] Unmanned seeding of rapeseed is an important link to construct unmanned rapeseed farm. Aiming at solving the problems of cumbersome manual collection of small and medium-sized field boundary information in the south, the low efficiency of turnaround operation of autonomous tractor, and leaving a large leakage area at the turnaround point, this study proposes to build an unmanned rapeseed seeding operation system based on cloud-terminal high-precision maps, and to improve the efficiency of the turnaround operation and the coverage of the operation. [Methods] The system was mainly divided into two parts: the unmanned seeding control cloud platform for oilseed rape is mainly composed of a path planning module, an operation monitoring module and a real-time control module; the navigation and control platform for rapeseed live broadcasting units is mainly composed of a Case TM1404 tractor, an intelligent seeding and fertilizing machine, an angle sensor, a high-precision Beidou positioning system, an electric steering wheel, a navigation control terminal and an on-board controller terminal. The process of constructing the high-precision map was as follows: determining the operating field, laying the ground control points; collecting the positional data of the ground control points and the orthophoto data from the unmanned aerial vehicle (UAV); processing the image data and constructing the complete map; slicing the map, correcting the deviation and transmitting it to the webpage. The field boundary information was obtained through the high-precision map. The equal spacing reduction algorithm and scanning line filling algorithm was adopted, and the spiral seeding operation path outside the shuttle row was automatically generated. According to the tractor geometry and kinematics model and the size of the distance between the tractor position and the field boundary, the specific parameters of the one-back and two-cut turning model were calculated, and based on the agronomic requirements of rapeseed sowing operation, the one-back-two-cut turn operation control strategy was designed to realize the rapeseed direct seeding unit's sowing operation for the omitted operation area of the field edges and corners. The test included map accuracy test, operation area simulation test and unmanned seeding operation field test. For the map accuracy test, the test field at the edge of Lake Yezhi of Huazhong Agricultural Universit was selected as the test site, where high-precision maps were constructed, and the image and position (POS) data collected by the UAV were processed, synthesized, and sliced, and then corrected for leveling according to the actual coordinates of the correction point and the coordinates of the image. Three rectangular fields of different sizes were selected for the operation area simulation test to compare the operation area and coverage rate of the three operation modes: set row, shuttle row, and shuttle row outer spiral. The Case TM1404 tractor equipped with an intelligent seeding and fertilizer application integrated machine was used as the test platform for the unmanned seeding operation test, and data such as tracking error and operation speed were recorded in real time by software algorithms. The data such as tracking error and operation speed were recorded in real-time. After the flowering of rapeseed, a series of color images of the operation fields were obtained by aerial photography using a drone during the flowering period of rapeseed, and the color images of the operation fields were spliced together, and then the seedling and non-seedling areas were mapped using map surveying and mapping software. [Results and Discussions] The results of the map accuracy test showed that the maximum error of the high-precision map ground verification point was 3.23 cm, and the results of the operation area simulation test showed that the full-coverage path of the helix outside the shuttle row reduced the leakage rate by 18.58%-26.01% compared with that of the shuttle row and the set of row path. The results of unmanned seeding operation field test showed that the average speed of unmanned seeding operation was 1.46 m/s, the maximum lateral deviation was 7.94 cm, and the maximum average absolute deviation was 1.85 cm. The test results in field showed that, the measured field area was 1 018.61 m2, and the total area of the non-growing oilseed rape area was 69.63 m2, with an operating area of 948.98 m2, and an operating coverage rate of 93.16%. [Conclusions] The effectiveness and feasibility of the constructed unmanned seeding operation system for rapeseed were demonstrated. This study can provide technical reference for unmanned seeding operation of rapeseed in small and medium-sized fields in the south. In the future, the unmanned seeding operation mode of rapeseed will be explored in irregular field conditions to further improve the applicability of the system.

Key words: rapeseed, unmanned seeding operation, full coverage path, turn model, high-precision map, leakage rate