吳才聰，王東旭，陳智博，宋兵兵，楊麗麗，楊衛(wèi)中

SF2104拖拉機自主行駛與作業(yè)控制方法

吳才聰1,2，王東旭1，陳智博1，宋兵兵1，楊麗麗1，楊衛(wèi)中1※

（1. 中國農(nóng)業(yè)大學信息與電氣工程學院，北京 100083；2. 農(nóng)業(yè)農(nóng)村部農(nóng)業(yè)信息獲取技術(shù)重點實驗室，北京 100083）

針對農(nóng)業(yè)機械無人化作業(yè)的應用需求，該研究基于SF2104動力換向線控底盤拖拉機和全球衛(wèi)星導航系統(tǒng)（Global Navigation Satellite System，GNSS），研發(fā)了拖拉機自主行駛與作業(yè)控制系統(tǒng)。該系統(tǒng)針對田內(nèi)直線作業(yè)與地頭轉(zhuǎn)彎，采用分層控制思想，將控制系統(tǒng)劃分為規(guī)劃層、控制層和執(zhí)行層。規(guī)劃層生成U形轉(zhuǎn)彎所需的路網(wǎng)數(shù)據(jù)，控制層進行拖拉機橫向控制、速度控制、轉(zhuǎn)彎控制、機具升降控制、當前路徑更新及終止作業(yè)等行為決策；執(zhí)行層負責以上行為的配置執(zhí)行。拖拉機掛載深松機進行深松作業(yè)，并與有人駕駛深松作業(yè)進行對照。結(jié)果表明，拖拉機自主行駛與作業(yè)控制系統(tǒng)橫向偏差的平均標準差為4 cm，平均作業(yè)速度及其平均標準差分別為1.66和0.09 m/s，穩(wěn)定作業(yè)時發(fā)動機轉(zhuǎn)速的平均標準差為7.9 r/min，平均機具位置的極差為23.8，均優(yōu)于有人駕駛。該研究初步實現(xiàn)了拖拉機的自主行駛與作業(yè)，有助于解決農(nóng)村勞動力緊缺問題。

農(nóng)業(yè)機械；試驗；自動駕駛；自主作業(yè)；控制系統(tǒng)

0 引 言

中國農(nóng)業(yè)勞動力數(shù)量不斷減少，用工成本日益增長?！耙蝗硕鄼C”作業(yè)模式可有效減少駕駛員數(shù)量，具有良好的經(jīng)濟效益[1-4]，而線控底盤和自動導航技術(shù)為該模式提供了基礎(chǔ)支撐[5-8]?！耙蝗硕鄼C”首先要求實現(xiàn)單機的無人駕駛[9]，但由于感知與避障等技術(shù)尚未成熟[10-12]，研發(fā)基于近距離人工遙控的單機自主作業(yè)控制技術(shù)是當前的重點。在該領(lǐng)域，國內(nèi)外學者以無人駕駛與自主作業(yè)為目標開展了系列研究，取得了一定的進展。

Zhang等[13-16]基于傳統(tǒng)拖拉機，利用GNSS（Global Navigation Satellite System）、慣性導航、激光雷達等研發(fā)的自動化拖拉機，可初步實現(xiàn)道路行駛和田內(nèi)作業(yè)的無人操作。凱斯紐荷蘭研發(fā)的無駕駛室Magnum和有駕駛室NHDriveTM等無人駕駛概念車輛配備了感應和探測裝置，能夠感知并避開障礙物[17-18]。近年來，國內(nèi)有關(guān)機構(gòu)基于PZ-60型水稻插秧機[19-20]，利用工況狀態(tài)邏輯控制等方法進行行駛機構(gòu)和插植機構(gòu)的聯(lián)合控制，實現(xiàn)了準無人駕駛作業(yè)，插秧機未配置感知系統(tǒng)，由操作員監(jiān)視作業(yè)環(huán)境和緊急制動；這種作業(yè)模式將單機所需的勞動力從3人減至1人，有效節(jié)約了用工成本，在黑龍江等地得到了應用推廣。為減少施藥過程中對人的危害，劉兆朋等[21]基于ZP9500高地隙噴霧機，利用查詢表方法進行直線跟蹤、地頭轉(zhuǎn)彎和噴霧作業(yè)的自動控制，初步實現(xiàn)了自主噴霧作業(yè)。陳黎卿等[22]基于純電動型噴霧機，設(shè)計了信息采集與通信系統(tǒng)，實現(xiàn)了噴霧機的自主行駛與作業(yè)控制。李云伍等[23]基于丘陵山地電動轉(zhuǎn)運車，基于GNSS、視覺傳感器及毫米波雷達，實現(xiàn)了轉(zhuǎn)運車的自主行駛。

農(nóng)機自主作業(yè)還需做好地頭轉(zhuǎn)彎的路徑規(guī)劃和跟蹤，其核心在于選擇轉(zhuǎn)彎模式和平滑轉(zhuǎn)彎路徑。Sabelhaus等[24-25]基于Dubins曲線和Reeds-Shepp曲線，設(shè)計了連續(xù)曲率掉頭路徑生成算法，并分析了Ω式、自相交式和魚尾式轉(zhuǎn)彎的特點及其時間特性。Paraforos等[26]為了找出最佳的跳過路徑數(shù)，針對歷史作業(yè)數(shù)據(jù)，設(shè)計了轉(zhuǎn)彎方式自動判別方法及轉(zhuǎn)彎時間自動分析方法，通過對800 hm2地塊連續(xù)4 a的數(shù)據(jù)分析，得出了最佳跳過路徑數(shù)為3條的結(jié)論。Yin等[27]針對SPV-6C插秧機作業(yè)路徑規(guī)劃與跟蹤控制系統(tǒng)，基于平滑最小轉(zhuǎn)向圓完成了小幅寬相鄰路徑地頭轉(zhuǎn)彎，實現(xiàn)了插秧機轉(zhuǎn)彎的自動化。Cariou等[28-29]以移動機器人小車為平臺，針對相鄰路徑掉頭問題，通過基于基本圖元的軌跡規(guī)劃和基于輪胎側(cè)偏角監(jiān)督估計的模型預測，優(yōu)化了掉頭時間和掉頭區(qū)面積。

綜上可知，農(nóng)機無人駕駛與自主作業(yè)的研究尚處于起步階段。本文擬基于SF2104動力換向線控底盤拖拉機和GNSS，開發(fā)拖拉機自主行駛與作業(yè)控制系統(tǒng)，并通過深松作業(yè)驗證其性能。

1 材料與方法

1.1 作業(yè)機組

拖拉機自主行駛與作業(yè)機組的組成如圖1，主要包括拖拉機、深松機、導航系統(tǒng)、車載控制器和監(jiān)控終端。

A.WAS-3106角度傳感器 B.電動方向盤 C.ZC30基準站 D.SF9507車載控制器 E.ZC200天線控制器一體機 F.深松機

拖拉機型號為SF2104，后輪驅(qū)動，阿克曼轉(zhuǎn)向，支持SAE J1939協(xié)議。軸距為2 894 mm，輪距為1 750 mm，轉(zhuǎn)彎半徑為7 150 mm，標定轉(zhuǎn)速為2 200 r/min，標定功率為154 kW。深松機型號為1SZ-230，幅寬為2.5 m，深松鏟數(shù)量為4鏟。導航系統(tǒng)型號為FARMSTAR F2BD-2.5RD，包括電動方向盤（MDU180）、角度傳感器（WAS-3106）、天線控制器一體機（ZC200）等。車載控制器型號為SF9507，輸入/輸出通道總計24路，可通過控制局域網(wǎng)（Controller Area Network，CAN）控制發(fā)動機、變速箱及液壓提升系統(tǒng)。監(jiān)控終端采用手機或電腦，4G通信，可實現(xiàn)拖拉機的遠程啟停及數(shù)據(jù)可視化。

1.2 控制系統(tǒng)

1.2.1 系統(tǒng)組成

控制系統(tǒng)的結(jié)構(gòu)見圖2，包括數(shù)據(jù)獲取單元、規(guī)劃控制單元及動作執(zhí)行單元。

圖2 控制系統(tǒng)結(jié)構(gòu)

數(shù)據(jù)獲取單元通過ZC200內(nèi)置的GNSS天線和陀螺儀獲取拖拉機的實時坐標與航向；通過角度傳感器獲取拖拉機前輪（轉(zhuǎn)向輪）的實時角度。GNSS基準站為ZC200播發(fā)差分改正數(shù)，實現(xiàn)厘米級定位。

規(guī)劃控制單元為ZC200內(nèi)置的導航控制器，是實現(xiàn)導航與控制的核心部件。該單元通過標準串口與電動方向盤進行通信，通過CAN與車載控制器進行通信。

動作執(zhí)行單元接收控制單元指令并執(zhí)行相應動作。電動方向盤負責控制前輪轉(zhuǎn)動，車載控制器通過CAN控制發(fā)動機轉(zhuǎn)速、變速箱擋位和懸掛裝置位置。

1.2.2 導航與控制方法

導航與控制的數(shù)據(jù)流圖如圖3，數(shù)據(jù)來自用戶輸入和實時獲取。導航控制器按分層思想設(shè)計，包括導航規(guī)劃層、行為控制層和行為執(zhí)行層。

至于灌區(qū)內(nèi)部、城鎮(zhèn)內(nèi)部的水權(quán)分配采用何種方式，可以交由各地自行探索、自主選擇?；蛟S可以采用灌區(qū)用水協(xié)會集體所有的形式，也可能在灌區(qū)內(nèi)部采用進一步分解到農(nóng)戶的形式。在城鎮(zhèn)內(nèi)部，水權(quán)或許可以屬于城鎮(zhèn)政府，而委托給供水公司和自供水單位使用。

圖3 導航與控制數(shù)據(jù)流圖

導航規(guī)劃層：有研究表明[25]，一般情況下，單弧轉(zhuǎn)彎時間最短。為此，本文的路徑規(guī)劃算法采用FSP（First Turn Skip Pattern）[30]，該模式將農(nóng)田劃分為多個標準區(qū)塊和1個剩余區(qū)塊，可進行單弧轉(zhuǎn)彎及套行作業(yè)。該算法通過迭代生成路徑編號，見式（1）。

式中q表示第個區(qū)塊內(nèi)的第個順序號的路徑編號；為跳過路徑數(shù)。

考慮到拖拉機最小轉(zhuǎn)彎半徑，單弧轉(zhuǎn)彎跳過的路徑數(shù)按式（2）計算。

式中為作業(yè)幅寬，m；為最小轉(zhuǎn)彎半徑，m。

導航規(guī)劃的最終輸出為路網(wǎng)數(shù)據(jù)S，形式如式（3）。

式中A、B為序號為的作業(yè)路徑的起點和終點坐標（坐標系為WGS84），包含經(jīng)度和緯度2個參數(shù)；為該區(qū)塊最后一條作業(yè)路徑的序號。

式中d為相對于11的延展方向，向右側(cè)延展時d=1，向左側(cè)延展時d=-1。

考慮到序號有奇偶之分，A和B的計算見式（5）。

然后，將生成的A和B的UTM坐標轉(zhuǎn)換為WGS84坐標。經(jīng)以上步驟，便可獲得最終的導航路徑，即路網(wǎng)數(shù)據(jù)，規(guī)劃結(jié)果如圖4。

注：No.表示該路徑的路徑編號為，=1,2,3，…；i表示該路徑的作業(yè)順序號為，=1,2,3，…；A和B表示作業(yè)順序號為的路徑的起點和終點坐標。下同。

Note: No.indicates that the No. of the path is=1,2,3,…;iindicates that the operation sequence No. of the path is,=1,2,3，…;AandBindicate the start and end coordinates of the path with operation sequence No.. The same as below.

圖4 路徑規(guī)劃結(jié)果

Fig.4 Result of route planning

行為控制層：行為控制層輸入為S和拖拉機實時位置now，輸出為執(zhí)行層的目標行為，即target，該行為的分類見表1。

表 1 目標行為分類

target的選擇與now有關(guān)，見式（6）。

式中為拖拉機實時位置與作業(yè)路徑起終點間距的判斷閾值，綜合考慮定位精度與跟蹤精度，本文取0.1 m。

行為執(zhí)行層：橫向控制由農(nóng)機自動導航系統(tǒng)實現(xiàn)，其算法業(yè)已成熟。當OP∈target時，直線跟蹤行為線程啟動，并向該模塊傳遞now和now的坐標，該模塊將基于軸距ab和拖拉機位置now動態(tài)調(diào)整目標前輪轉(zhuǎn)角，最終將該值通過標準串口傳遞給電動方向盤執(zhí)行。

速度控制通過調(diào)節(jié)發(fā)動機轉(zhuǎn)速target實現(xiàn)，由車載控制器通過CAN傳遞給發(fā)動機。為滿足深松作業(yè)的農(nóng)藝和轉(zhuǎn)彎要求，設(shè)置高轉(zhuǎn)速狀態(tài)up和低轉(zhuǎn)速狀態(tài)down，分別對應升速與降速行為，target滿足式（7）。

機具升降包括機具提升和機具降落，定義開關(guān)量target，滿足式（8），即當target為1時機具提升，當target為2時機具降落。

對于深松作業(yè)，機具降落點down和機具上升點up需要在作業(yè)前完成配置，均由無量綱的機具位置表示，范圍為[0, 1 000]，其中，0表示最低位置，1 000表示最高位置。導航控制器將以上參數(shù)通過CAN總線發(fā)送至車載控制器，由車載控制器控制電液提升系統(tǒng)。

地頭轉(zhuǎn)彎采用單弧轉(zhuǎn)彎（圖5），此時的目標前輪轉(zhuǎn)角為定值?？紤]對行與轉(zhuǎn)向要求，應滿足式（9）。

式中ab為軸距，m；為轉(zhuǎn)彎距離，m。

注：為目標前輪轉(zhuǎn)角，（°）；ab為軸距，m；為轉(zhuǎn)彎距離，m。

Note:is the target front wheel turning angle, (°);abis the wheelbase, m;is the turning distance, m.

圖5 轉(zhuǎn)向示意圖

Fig.5 Diagram of turning

1.3 試驗設(shè)計

為驗證自主行駛與作業(yè)系統(tǒng)的精度與穩(wěn)定性，本文設(shè)計自動駕駛組和有人駕駛組開展對比試驗。試驗地塊位于北京市順義區(qū)（40°12'48.32"N，116°33'13.43"E），南北長約90 m，東西寬約35 m。

對于自動駕駛組，將=7.15及=2.5代入式（2）可得為6；由于地塊限制，設(shè)置作業(yè)路徑數(shù)為10條，速度控制和升降控制參數(shù)見表2。有人駕駛組由機手自行操作，實際作業(yè)路徑數(shù)為11條。

表2 速度和升降控制參數(shù)

2 結(jié)果與討論

2.1 行駛軌跡

自動駕駛和有人駕駛的行駛軌跡如圖6。直觀來看，在直線作業(yè)段，自動駕駛的行駛軌跡更平直；在地頭轉(zhuǎn)彎段，自動駕駛只存在前進軌跡，且軌跡均為U形，而有人駕駛存在部分倒車軌跡。

圖6 行駛軌跡對比

2.2 橫向偏差

在作業(yè)階段，自動駕駛和有人駕駛的拖拉機橫向偏差的平均標準差分別為4和8 cm。其中，圖7所示為第1和第5條作業(yè)路徑的橫向控制偏差。顯然，自動駕駛的拖拉機橫向偏差標準差降低了50%，作業(yè)性能更穩(wěn)定。

圖7 第1和第5條作業(yè)路徑的橫向控制偏差

2.3 作業(yè)速度

自動駕駛和有人駕駛的平均作業(yè)速度分別為1.66和2.98 m/s，平均標準差分別為0.09和0.27 m/s。自動駕駛的控制誤差降低了約67%，表明自動駕駛的作業(yè)速度更為平穩(wěn)（圖8）。

圖8 拖拉機作業(yè)速度對比

2.4 發(fā)動機轉(zhuǎn)速

圖9為拖拉機的發(fā)動機轉(zhuǎn)速對比。自動駕駛的發(fā)動機轉(zhuǎn)速有2個穩(wěn)定點，作業(yè)階段穩(wěn)定在1 500 r/min附近，轉(zhuǎn)彎階段穩(wěn)定在1 000 r/min附近，與設(shè)定值一致，僅在直線作業(yè)開始或結(jié)束時出現(xiàn)超調(diào)現(xiàn)象，而有人駕駛的發(fā)動機轉(zhuǎn)速沒有出現(xiàn)穩(wěn)定點，自動駕駛在作業(yè)階段（以130～300 s為例）和掉頭階段（以335～425 s為例）的發(fā)動機轉(zhuǎn)速的標準差分別為7.9和9.1 r/min，拖拉機動力控制性能更優(yōu)。

圖9 拖拉機發(fā)動機轉(zhuǎn)速對比

2.5 機具升降

圖10為機具位置對比。對前4個穩(wěn)定作業(yè)階段（自動駕駛：130～300 s、450～500s、675～725 s、690～740 s；有人駕駛：20～130 s、205～300 s、405～535 s、720～800 s）的機具升降情況進行分析，自動駕駛平均機具位置的極差為23.8，有人駕駛平均機具位置的極差為113.3。較小的極差反映了自動駕駛的機具升降控制更為精確。

圖10 機具位置對比

3 結(jié) 論

1）基于SF2104動力換向拖拉機、GNSS農(nóng)機自動導航系統(tǒng)和深松機，設(shè)計了自主行駛與作業(yè)控制系統(tǒng)。采用分層控制思想，將控制系統(tǒng)劃分為規(guī)劃層、控制層和執(zhí)行層。規(guī)劃層生成路網(wǎng)數(shù)據(jù)，控制層進行橫向控制、速度控制、轉(zhuǎn)彎控制和機具升降控制等行為決策，執(zhí)行層負責配置執(zhí)行。

2）田間對照試驗表明，自動駕駛和有人駕駛的橫向偏差的平均標準差分別為4和8 cm，作業(yè)速度的平均標準差分別為0.09和0.27 m/s。自動駕駛穩(wěn)定作業(yè)時發(fā)動機轉(zhuǎn)速的平均標準差為7.9 r/min，平均機具位置極差23.8，均優(yōu)于有人駕駛作業(yè)的對應指標，說明自主作業(yè)控制技術(shù)具有較高的作業(yè)精度和穩(wěn)定性。

[1]Lowenberg-DeBoer James, Huang Iona Yuelu, Grigoriadis Vasileios, et al. Economics of robots and automation in field crop production[J]. Precision Agriculture, 2020, 21(2): 278-299.

[2]Pedersen S?ren Marcus, Fountas Spyros, S?rensen Claus G, et al. Robotic seeding: Economic perspectives[M]. Precision Agriculture: Technology and Economic Perspectives. Springer, 2017: 167-179.

[3]Marinoudi Vasso, S?rensen Claus, Pearson Simon, et al. Robotics and labour in agriculture: A context consideration[J]. Biosystems Engineering, 2019, 184: 111-121.

[4]Duckett Tom, Pearson Simon, Blackmore Simon, et al. Agricultural robotics: The future of robotic agriculture[EB/OL]. 2018-08-02 https://arxiv.org/ftp/arxiv/ papers/1806/1806.06762.pdf.

[5]韓樹豐，何勇，方慧. 農(nóng)機自動導航及無人駕駛車輛的發(fā)展綜述[J]. 浙江大學學報：農(nóng)業(yè)與生命科學版，2018，44(4)：381-391. Han Shufeng, He Yong, Fang Hui. Recent development in automatic guidance and autonomous vehicle for agriculture: A review[J]. Journal of Zhejiang University: Agriculture & Life Sciences, 2018, 44(4): 381-391. (in Chinese with English abstract)

[6]董勝，袁朝輝，谷超，等. 基于多學科技術(shù)融合的智能農(nóng)機控制平臺研究綜述[J]. 農(nóng)業(yè)工程學報，2017，33(8)：1-11. Dong Sheng, Yuan Chaohui, Gu Chao, et al. Research on intelligent agricultural machinery control platform based on multi-discipline technology integration[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(8): 1-11. (in Chinese with English abstract)

[7]張漫，季宇寒，李世超，等. 農(nóng)業(yè)機械導航技術(shù)研究進展[J]. 農(nóng)業(yè)機械學報，2020，51(4)：1-18. Zhang Man, Ji Yuhan, Li Shichao, et al. Research progress of agricultural machinery navigation technology[J]. Transactions of Chinese Society for Agricultural Machinery, 2020, 51(4): 1-18. (in Chinese with English abstract)

[8]Kelc Damijan, Stajnko Denis, Berk Peter, et al. Reduction of environmental pollution by using RTK-navigation in soil cultivation[J]. International Journal of Agricultural and Biological Engineering, 2019, 12(5): 173-178.

[9]劉小偉，吳才聰，車宇. 無人化農(nóng)機技術(shù)與裝備發(fā)展趨勢[J]. 農(nóng)機科技推廣，2019(10)：26-27. Liu Xiaowei, Wu Caicong, Che Yu. Development trend of unmanned agricultural machinery technology and equipment[J]. Agriculture Machinery Technology Extension, 2019(10): 26-27. (in Chinese with English abstract)

[10]何勇，蔣浩，方慧，等. 車輛智能障礙物檢測方法及其農(nóng)業(yè)應用研究進展[J]. 農(nóng)業(yè)工程學報，2018，34(9)：21-32. He Yong, Jiang Hao, Fang Hui, et al. Research progress of intelligent obstacle detection methods of vehicles and their application on agriculture[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(9): 21-32. (in Chinese with English abstract)

[11]薛金林，閆嘉，范博文. 多類農(nóng)田障礙物卷積神經(jīng)網(wǎng)絡(luò)分類識別方法[J]. 農(nóng)業(yè)機械學報，2018，49(S1)：35-41. Xue Jinlin, Yan Jia, Fan Bowen. Classification and identification method of multiple kinds of farm obstacles based on convolutional neural network[J]. Transactions of Chinese Society for Agricultural Machinery, 2018, 49(S1): 35-41. (in Chinese with English abstract)

[12]薛金林，董淑嫻，范博文. 基于信息融合的農(nóng)業(yè)自主車輛障礙物檢測方法[J]. 農(nóng)業(yè)機械學報，2018，49(S1)：29-34. Xue Jinlin, Dong Shuxian, Fan Bowen. Detection of obstacles based on information fusion for autonomous agricultural vehicles[J]. Transactions of Chinese Society for Agricultural Machinery, 2018, 49(S1): 29-34. (in Chinese with English abstract)

[13]Zhang Chi, Noguchi Noboru, Yang Liangliang. Leader–follower system using two robot tractors to improve work efficiency[J]. Computers and Electronics in Agriculture, 2016, 121: 269-281.

[14]Zhang Chi, Noguchi Noboru. Development of a multi-robot tractor system for agriculture field work[J]. Computers and Electronics in Agriculture, 2017, 142: 79-90.

[15]Wang Hao, Noguchi Noboru. Autonomous maneuvers of a robotic tractor for farming[C]// 2016 IEEE/SICE International Symposium on System Integration (SII). Sapporo, Japan: IEEE, 2016. 592-597.

[16]Wang Hao, Noguchi Noboru. Adaptive turning control for an agricultural robot tractor[J]. International Journal of Agricultural and Biological Engineering, 2018, 11(6): 113-119.

[17]關(guān)群. 凱斯紐荷蘭工業(yè)集團推出無人駕駛概念拖拉機[J]. 農(nóng)業(yè)機械，2016(9)：40-43. Guan Qun. Case New Holland Industries launches driverless concept tractor[J]. Farm Machinery, 2016(9): 40-43. (in Chinese with English abstract)

[18]關(guān)群. 凱斯無人駕駛概念拖拉機榮獲“最佳設(shè)計獎”[J]. 農(nóng)業(yè)機械，2018(1)：64. Guan Qun. Case driverless concept tractor won the “Best Design Award”[J]. Farm Machinery, 2018(1): 64. (in Chinese with English abstract)

[19]何杰，朱金光，羅錫文，等. 電動方向盤插秧機轉(zhuǎn)向控制系統(tǒng)設(shè)計[J]. 農(nóng)業(yè)工程學報，2019，35(6)：10-17. He Jie, Zhu Jinguang, Luo Xiwen, et al. Design of steering control system for rice transplanter equipped with steering wheel-like motor[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(6): 10-17. (in Chinese with English abstract)

[20]何杰，朱金光，張智剛，等. 水稻插秧機自動作業(yè)系統(tǒng)設(shè)計與試驗[J]. 農(nóng)業(yè)機械學報，2019，50(3)：17-24. He Jie, Zhu Jinguang, Zhang Zhigang, et al. Design and experiment of automatic operation system for rice transplanter[J]. Transactions of Chinese Society for Agricultural Machinery, 2019, 50(3): 17-24. (in Chinese with English abstract)

[21]劉兆朋，張智剛，羅錫文，等. 雷沃ZP9500高地隙噴霧機的GNSS自動導航作業(yè)系統(tǒng)設(shè)計[J]. 農(nóng)業(yè)工程學報，2018，34(1)：15-21. Liu Zhaopeng, Zhang Zhigang, Luo Xiwen, et al. Design of automatic navigation operation system for Lovol ZP9500 high clearance boom sprayer based on GNSS[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(1): 15-21. (in Chinese with English abstract)

[22]陳黎卿，許澤鎮(zhèn)，解彬彬，等. 無人駕駛噴霧機電控系統(tǒng)設(shè)計與試驗[J]. 農(nóng)業(yè)機械學報，2019，50(1)：122-128. Chen Liqing, Xu Zezhen, Xie Binbin, et al. Design and test of electronic control system for unmanned drive sprayer[J]. Transactions of Chinese Society for Agricultural Machinery, 2019, 50(1): 122-128. (in Chinese with English abstract)

[23]李云伍，徐俊杰，王銘楓，等. 丘陵山區(qū)田間道路自主行駛轉(zhuǎn)運車及其視覺導航系統(tǒng)研制[J]. 農(nóng)業(yè)工程學報，2019，35(1)：52-61. Li Yunwu, Xu Junjie, Wang Mingfeng, et al. Development of autonomous driving transfer trolley on field roads and its visual navigation system for hilly areas[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(1): 52-61. (in Chinese with English abstract)

[24]Sabelhaus Dennis, Frank Roben, Lars Peter Meyer zu Helligen, et al. Using continuous-curvature paths to generate feasible headland turn manoeuvres[J]. Biosystems Engineering, 2013, 116(4): 399-409.

[25]Backman Juha, Piirainen Pyry, Oksanen Timo. Smooth turning path generation for agricultural vehicles in headlands[J]. Biosystems Engineering, 2015, 139: 76-86.

[26]Paraforos Dimitrios S, Hübner Robert, Griepentrog Hans W. Automatic determination of headland turning from auto-steering position data for minimising the infield non-working time[J]. Computers and Electronics in Agriculture, 2018, 152: 393-400.

[27]Yin Xiang, Du Juan, Noguchi Noboru, et al. Development of autonomous navigation system for rice transplanter[J]. International Journal of Agricultural and Biological Engineering, 2018, 11(6): 89-94.

[28]Cariou Christophe, Lenain Roland, Thuilot Benoit, et al. Motion planner and lateral-longitudinal controllers for autonomous maneuvers of a farm vehicle in headland[C]// 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis, MO, USA: IEEE, 2009. 5782-5787

[29]Cariou Christophe, Lenain Roland, Berducat Michel, et al. Autonomous maneuver of a farm vehicle with a trailed implement: motion planner and lateral-longitudinal controllers[C]// 2010 IEEE International Conference on Robotics and Automation. Anchorage, AK, USA: IEEE, 2010. 3819-3824

[30]Zhou Kun. Simulation Modelling for In-field Planning of Sequential Machinery Operations in Cropping Systems[D]. Aarhus: Aarhus University, 2015.

Autonomous driving and operation control method for SF2104 tractors

Wu Caicong1,2, Wang Dongxu1, Chen Zhibo1, Song Bingbing1, Yang Lili1, Yang Weizhong1※

(1,,100083,; 2,,100083,)

To solve the critical shortage and the increasing cost of rural labor, the concept of “one person, multiple machines” were proposed and an autonomous driving and operating system for SF2104 was developed. The hardware of the system included SF2104 tractor with a power reverser transmission and wire-controlled chassis, WAS-3106 angle sensor, 1SZ-230 subsoiler, GNSS (Global Navigation Satellite System) based auto-steering system for agricultural machinery (FARMSTARF2BD-2.5RD), SF9507 vehicle controller, and mobile monitor such as smartphone and PC (personal computer). The control system mainly included three function units, i.e., data acquisition unit, planning and control unit, and movement unit. The navigation and control method was deployed in the planning and control unit according to the hierarchical control method. The entire method constituted of the layer of navigation planning, the layer of behavior control, and the layer of behavior execution. The operation width, the turning radius and the first operation path (straight line) from user inputs were transferred to the layer of navigation planning, and it also used to calculate the path network data. The path network data, wheelbase from user inputs and the real-time data (i.e.，location, heading and front wheel angle), were transferred to the layer of behavior control involving the target behavior decision. The decision of the target behavior wouldl be transferred to the layer of behavior execution, which derived the target front wheel angle, the target engine rotation speed and the target implement position. The layer of navigation planning generated the path network data to meet the requirement of operating in the field and turning in the headland through the FSP (First Turn Skip Pattern). The layer of behavior control made the decisions of target behavior, including lateral control, speed control, turning control, lifting control, current path update and operation ending. When the tractor entered the operating strip, the system identified the starting point of the operation, and sequentially executed the behavior of implement lowering, the behavior of speed increase, and the behavior of tracking thestraight line. When the tractor finished the operation of the current path, the behaviors of implement lifting, speed reduction, and turning were executed sequentially. The behavior of speed control was executed by controlling the tractor’s engine rotation speed at a high value or a low value through the vehicle controller. The behavior of lifting control was executed by transmitting an implement status value to the controller of the hydraulic lifting system. The behavior of turning control was executed by transmitting a fixed front wheel angle which was calculated by tractor kinematics turning distance. The subsoil operation experiments were carried out in the Shunyi District of Beijing. The experiments included the manual driving group and the autonomous driving group. For the autonomous driving group, the operating trajectories were straight and smooth, the average standard deviation of lateral deviation was 4 cm, the average operating speed was 1.66 m/s, and the standard deviation of operating speed was 0.09 m/s. During the stable operating stage in the field, the standard deviation of engine rotation speed was 7.9 r/min, and the range of the average implement position was 23.8. For themanual driving group, the operating trajectories were not smoother than the trajectories of the autonomous driving group, and the average standard deviation of lateral deviation was 8 cm, the average operating speed was 2.98 m/s, and the standard deviation of operating speed was 0.27 m/s. The stability of engine rotation speed and the range of implement position were also poor in manual driving group. The results showed that the autonomous driving group outperformed the manual driving group in terms of operating accuracy and working stability, which can effectively reduce labor costs. This research provides a platform foundation and theoretical basis for the future research of multi-vehicle and multi-operation collaboration with less human operations.

agricultural machinery; experiments; automatic driving; autonomous operation; control system

吳才聰，王東旭，陳智博，等. SF2104拖拉機自主行駛與作業(yè)控制方法[J]. 農(nóng)業(yè)工程學報，2020，36(18)：42-48.doi：10.11975/j.issn.1002-6819.2020.18.006 http://www.tcsae.org

Wu Caicong, Wang Dongxu, Chen Zhibo, et al. Autonomous driving and operation control method for SF2104 tractors[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(18): 42-48. (in Chinese with English abstract) doi：10.11975/j.issn.1002-6819.2020.18.006 http://www.tcsae.org

2020-06-03

2020-08-01

國家重點研發(fā)計劃項目（2016YFB0501805）

吳才聰，博士，副教授，博士生導師，主要從事農(nóng)機導航與位置服務等研究。Email：wucc@cau.edu.cn

楊衛(wèi)中，博士，副教授，主要從事農(nóng)機導航與位置服務等研究。Email：ywz@cau.edu.cn

10.11975/j.issn.1002-6819.2020.18.006

S24

A

1002-6819(2020)-18-0042-07