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農(nóng)用無(wú)人機(jī)移動(dòng)補(bǔ)給平臺(tái)自主降落算法與試驗(yàn)
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國(guó)家現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系特色蔬菜創(chuàng)新團(tuán)隊(duì)項(xiàng)目(CARS24D01)


Investigation and Experiment on Autonomous Landing Algorithm of Agricultural Unmanned Aerial Vehicle Movable Supply Platform
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    摘要:

    為實(shí)現(xiàn)根據(jù)無(wú)人機(jī)作業(yè)位置改變中途補(bǔ)充能源或噴灑物的起降地點(diǎn),增加無(wú)人機(jī)有效作業(yè)時(shí)間,提高無(wú)人機(jī)作業(yè)效率,,設(shè)計(jì)了無(wú)人機(jī)移動(dòng)補(bǔ)給平臺(tái),。通過(guò)研究農(nóng)用無(wú)人機(jī)自主降落過(guò)程,提出一種基于模糊邏輯和比例積分微分(Proportional-integral and derivative, PID)分段控制的農(nóng)用無(wú)人機(jī)跟蹤降落算法,,該算法既擁有PID算法的高精度,,又兼顧模糊控制算法響應(yīng)速度快、超調(diào)量小,、魯棒性強(qiáng)的優(yōu)點(diǎn),。目標(biāo)軌跡跟蹤預(yù)測(cè)由粒子濾波器跟蹤算法和軌跡擬合算法相結(jié)合進(jìn)行求解。仿真和現(xiàn)場(chǎng)試驗(yàn)表明,,與單一的PID算法和模糊邏輯算法相比,,分段控制算法能夠把農(nóng)用無(wú)人機(jī)對(duì)移動(dòng)補(bǔ)給平臺(tái)的跟蹤誤差縮小到6.7cm以內(nèi),在移動(dòng)補(bǔ)給平臺(tái)上的降落精度控制在7.2cm以內(nèi),。

    Abstract:

    The movable supply platform of agricultural unmanned aerial vehicles (UAVs) can change the landing site of midway supplying according to the variation of the UAVs location. It was mainly focused on the autonomous landing of agricultural UAVs on movable supply platform. The main novelty was that a piecewise control algorithm which combined the fuzzy logic control and PID control was proposed to control the landing process. When the distance error between the agricultural UAV and the movable supply platform was larger than the preset threshold, the fuzzy logic control was used to control the landing process due to its quick response character. When the distance error was smaller than the preset threshold, the PID control was used due to its high precision character. The detailed control process was as follows, firstly, in order to increase the recognition accuracy of UAV on movable platform, the upper part of the movable platform was designed to be a structure symmetry “H” pattern and the lower part was designed to be a contrasting QR code pattern. The UAV could use the optical flow algorithm to detect the black and white points, such that the relative speed between UAV and the platform could be calculated. Secondly, the dynamic model of UAV was constructed and then transformed its coordinates to the global coordinates to calculate the position error between agricultural UAV and the platform. Thirdly, the particle filter tracking algorithm and fitting function was used to fit the movement path of the UAV. At last, the proposed control method was used to control the UAV move along the fitted path. Using correlation coefficient index the Matlab simulation results proved the superiority of the proposed control method in tracking step signal and sinusoidal signal compared with individual fuzzy control method and PID control method. Furthermore, field test proved the control method could reduce the tracking error between UAV and movable supply platform to no more than 6.7cm and the landing accuracy on movable supply to no more than 7.2cm.

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祖林祿,侯加林,陳民,王紅,蘇斐.農(nóng)用無(wú)人機(jī)移動(dòng)補(bǔ)給平臺(tái)自主降落算法與試驗(yàn)[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2020,51(3):43-50. ZU Linlu, HOU Jialin, CHEN Min, WANG Hong, SU Fei. Investigation and Experiment on Autonomous Landing Algorithm of Agricultural Unmanned Aerial Vehicle Movable Supply Platform[J]. Transactions of the Chinese Society for Agricultural Machinery,2020,51(3):43-50.

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  • 收稿日期:2019-07-18
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  • 在線發(fā)布日期: 2020-03-10
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