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基于逆運(yùn)動學(xué)降維求解與YOLO v4的果實(shí)采摘系統(tǒng)研究
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云南省科技廳生物資源數(shù)字化開發(fā)應(yīng)用項(xiàng)目(202002AA10007)、云南省教育廳科學(xué)研究基金項(xiàng)目(2020J0402,、2021J0153)和云南省農(nóng)業(yè)聯(lián)合項(xiàng)目(2018FG001-108)


Design of Fruit Picking System Based on Inverse Kinematics Dimension Reduction and YOLO v4
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    摘要:

    為提高采摘設(shè)備的執(zhí)行效率,采用六自由度機(jī)械臂,、樹莓派,、Android手機(jī)端和服務(wù)器設(shè)計(jì)了一種智能果實(shí)采摘系統(tǒng),,該系統(tǒng)可自動識別不同種類的水果,,并實(shí)現(xiàn)自動采摘,可通過手機(jī)端遠(yuǎn)程控制采摘設(shè)備的起始和停止,,并遠(yuǎn)程查看實(shí)時(shí)采摘視頻,。提出通過降低自由度和使用二維坐標(biāo)系來實(shí)現(xiàn)三維坐標(biāo)系中機(jī)械臂逆運(yùn)動學(xué)的求解過程,從而避免了大量的矩陣運(yùn)算,,使機(jī)械臂逆運(yùn)動學(xué)求解過程更加簡捷,。利用Matlab中的Robotic Toolbox進(jìn)行機(jī)械臂三維建模仿真,驗(yàn)證了降維求解的可行性,。在果實(shí)采摘流程中,,為了使機(jī)械臂運(yùn)動軌跡更加穩(wěn)定與協(xié)調(diào),采用五項(xiàng)式插值法對機(jī)械臂進(jìn)行運(yùn)動軌跡規(guī)劃控制,?;贒arknet深度學(xué)習(xí)框架的YOLO v4目標(biāo)檢測識別算法進(jìn)行果實(shí)目標(biāo)檢測和像素定位,在Ubuntu 19.10操作系統(tǒng)中使用2000幅圖像作為訓(xùn)練集,,分別對不同種類的果實(shí)進(jìn)行識別模型訓(xùn)練,,在GPU環(huán)境下進(jìn)行測試,結(jié)果表明,,每種果實(shí)識別的準(zhǔn)確率均在94%以上,,單次果實(shí)采摘的時(shí)間約為17s。經(jīng)過實(shí)際測試,,該系統(tǒng)具有良好的穩(wěn)定性,、實(shí)時(shí)性以及對果實(shí)采摘的準(zhǔn)確性。

    Abstract:

    In order to improve the execution efficiency of picking equipment, a six DOF robot arm, raspberry pie, single chip microcomputer, Android mobile terminal and server were used to build an intelligent fruit picking experimental system. It can automatically identify different kinds of fruits and realize automatic picking. Users can control the picking equipment and view the real-time picking video through the mobile terminal remotely. The solution process of inverse kinematics of manipulator in the three-dimensional coordinate system was realized by reducing the degree of freedom and using two-dimensional coordinate system, so as to avoid a lot of matrix operation and make the process of inverse kinematics solution of manipulator more simple. The Robotic Toolbox in Matlab was used to carry out 3D modeling and simulation of the manipulator, and the feasibility of dimension reduction was verified. In the fruit picking process, in order to make the trajectory of the manipulator more stable and coordinated, the pentanomic interpolation method was used to plan and control the trajectory of the manipulator. The object detection and pixel location of fruit was based on the YOLO v4 artificial intelligence recognition algorithm of Darknet deep learning framework. Using GPU training in the Ubuntu 19.10 operating system, totally 2000 images were used as a training set to train the recognition model of different kinds of fruits. The accuracy rate of each fruit recognition was more than 94%, the time for a single fruit picking about 17s. After the actual test, the system had good stability, real-time performance and the accuracy of fruit picking.

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張晴暉,孔德肖,李俊萩,鐘麗輝.基于逆運(yùn)動學(xué)降維求解與YOLO v4的果實(shí)采摘系統(tǒng)研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2021,52(9):15-23. ZHANG Qinghui, KONG Dexiao, LI Junqiu, ZHONG Lihui. Design of Fruit Picking System Based on Inverse Kinematics Dimension Reduction and YOLO v4[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(9):15-23.

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  • 收稿日期:2020-09-03
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  • 在線發(fā)布日期: 2021-09-10
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