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基于CPF-EKF算法的大載荷植保無(wú)人機(jī)姿態(tài)解算方法
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國(guó)家自然科學(xué)基金項(xiàng)目(11372309、61304017)、吉林省科技發(fā)展計(jì)劃重點(diǎn)項(xiàng)目(20150204074GX、20160204010NY)、省院合作科技專項(xiàng)資金項(xiàng)目(2017SYHZ0024)和中科院青促會(huì)項(xiàng)目(2014192)


Attitude Calculation Method Based on CPF-EKF for Large Load Plant Protection UAV
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    摘要:

    為了解決傳統(tǒng)人工噴灑農(nóng)藥的不足,更高效地進(jìn)行病蟲(chóng)害的防治,設(shè)計(jì)了基于八軸十六旋翼無(wú)人機(jī)的農(nóng)藥噴灑系統(tǒng),實(shí)現(xiàn)了農(nóng)藥的機(jī)載噴灑功能。使用共軸雙槳和旋翼模塊的傾斜配置,對(duì)八軸多旋翼無(wú)人機(jī)進(jìn)行結(jié)構(gòu)改進(jìn),提高了系統(tǒng)的安全性與可靠性。整個(gè)系統(tǒng)滿載10kg,噴灑飛行速度可到達(dá)5m/s,飛行時(shí)間超過(guò)10min。針對(duì)傳統(tǒng)擴(kuò)展卡爾曼濾波(Extended Kalman filter,EKF)姿態(tài)解算方法無(wú)法滿足大載荷無(wú)人機(jī)強(qiáng)振動(dòng)條件下的工作要求,導(dǎo)致姿態(tài)角解算精度不高,并且容易導(dǎo)致姿態(tài)角發(fā)散的問(wèn)題,提出了基于20維狀態(tài)量的CPF-EKF算法,額外引入了陀螺儀、加速度計(jì)和磁力計(jì)偏置誤差作為狀態(tài)量,使三軸姿態(tài)角的最優(yōu)估計(jì)值更加準(zhǔn)確,并且引入互補(bǔ)濾波(Complement filter,CPF)檢測(cè)模塊,當(dāng)檢測(cè)到EKF有發(fā)散趨勢(shì)時(shí),對(duì)EKF進(jìn)行復(fù)位,從而簡(jiǎn)單高效地避免了EKF發(fā)散。采用實(shí)際飛行數(shù)據(jù)對(duì)算法進(jìn)行驗(yàn)證,靜態(tài)試驗(yàn)表明,該算法滾轉(zhuǎn)角和俯仰角精度為±0.05°,偏航角精度為±0.2°。動(dòng)態(tài)試驗(yàn)中以MTi傳感器輸出為參考,CPF-EKF在姿態(tài)解算過(guò)程中出現(xiàn)復(fù)位,三軸姿態(tài)角準(zhǔn)確跟蹤并未發(fā)散,并且動(dòng)態(tài)精度與MTi相當(dāng),滾轉(zhuǎn)角、俯仰角精度為±0.1°,偏航角精度為±0.5°,并且算法具有良好的實(shí)時(shí)性,證明了該算法的有效性。

    Abstract:

    In order to solve the shortcomings of traditional artificial spraying pesticides and more efficient prevention and treatment of diseases and pests, a pesticide spraying system based on sixteenrotor unmanned aerial vehicle (UAV) was designed. The sixteen-rotor UAV’s basic structure and attitude calculation method were explained. The whole system was full of 10kg, cruising speed can reach 5m/s, and the flight time was more than 10min. The traditional extended Kalman filter (EKF) attitude calculation method cannot meet the work requirements under the strong vibration condition of the large load UAV. The attitude angle calculation accuracy was not high and the attitude angle divergence was easily caused. A CPF-EKF algorithm based on 20 dimensional state quantity was proposed. The bias error of gyroscopes, accelerometers and magnetometers were added as the state quantity, which made the optimal estimation of the attitude angle of the three axes more accurate. Complementary filtering (CPF) was treated as an EKF detection module. When the EKF had the divergence trend, the EKF was reset, thus the EKF divergence was avoided simply and efficiently. Using actual flight data to verify the algorithm, the static experiment showed that the precision of the roll angle and the pitch angle of the algorithm were ±0.05°, the precision of the yaw angle was±0.2°. The dynamic experiment showed that the precision of the roll angle and the pitch angle of the algorithm were±0.1°, the precision of the yaw angle was±0.5°, and the algorithm had good real-time performance.

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吳和龍,白越,裴信彪,馬萍,彭程,高慧斌.基于CPF-EKF算法的大載荷植保無(wú)人機(jī)姿態(tài)解算方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2018,49(6):24-31,77. WU Helong, BAI Yue, PEI Xinbiao, MA Ping, PENG Cheng, GAO Huibin. Attitude Calculation Method Based on CPF-EKF for Large Load Plant Protection UAV[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(6):24-31,77.

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  • 收稿日期:2017-12-23
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  • 在線發(fā)布日期: 2018-06-10
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