本文關(guān)鍵詞：6R工業(yè)機(jī)器人的運(yùn)動(dòng)軌跡規(guī)劃及仿真研究　出處：《中北大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 6R工業(yè)機(jī)器人 solidworks 運(yùn)動(dòng)學(xué)分析 軌跡規(guī)劃 RBF神經(jīng)網(wǎng)絡(luò)

【摘要】：隨著機(jī)器人技術(shù)越來越成熟,企業(yè)制造商越來越廣泛的使用機(jī)器人來完成工作任務(wù)。其中六自由度關(guān)節(jié)式機(jī)器人由于其操作空間大并且結(jié)構(gòu)緊湊在制造業(yè)領(lǐng)域中廣泛使用,它是生產(chǎn)制造中應(yīng)用最廣泛的機(jī)器人。由于關(guān)節(jié)式機(jī)器人的諸多優(yōu)點(diǎn),本文設(shè)計(jì)和分析了一個(gè)負(fù)載為6kg的關(guān)節(jié)式機(jī)器人。首先,對(duì)機(jī)器人常用的幾種類型進(jìn)行分析,最終選擇關(guān)節(jié)式機(jī)器人進(jìn)行設(shè)計(jì),然后對(duì)其每個(gè)關(guān)節(jié)的結(jié)構(gòu)進(jìn)行設(shè)計(jì),并建立solidworks三維模型圖,使得機(jī)器人的整體結(jié)構(gòu)更加直觀;機(jī)器人要實(shí)現(xiàn)運(yùn)動(dòng),就必須對(duì)其驅(qū)動(dòng)系統(tǒng)進(jìn)行設(shè)計(jì),因此,根據(jù)經(jīng)驗(yàn)及其他機(jī)器人樣機(jī),對(duì)工業(yè)機(jī)器人的電機(jī)和減速器進(jìn)行選型;建立對(duì)工業(yè)機(jī)器人的連桿坐標(biāo)系,得到其D-H參數(shù),根據(jù)這些參數(shù)建立機(jī)器人的運(yùn)動(dòng)學(xué)方程,并運(yùn)用Matlab Robotics toolbox工具箱對(duì)機(jī)器人的各個(gè)桿件進(jìn)行建模,組成一個(gè)完整的機(jī)器人模型,對(duì)其進(jìn)行正逆運(yùn)動(dòng)學(xué)和工作空間的仿真分析。其次,介紹了機(jī)器人的兩種軌跡規(guī)劃方法,這兩種方法分別是關(guān)節(jié)空間軌跡規(guī)劃和笛卡爾空間軌跡規(guī)劃,然后對(duì)兩者進(jìn)行分析比較,綜合兩者的優(yōu)缺點(diǎn),最終本文選擇關(guān)節(jié)空間進(jìn)行軌跡規(guī)劃;選取軌跡規(guī)劃方法后結(jié)合matlab的強(qiáng)大功能對(duì)機(jī)器人的運(yùn)動(dòng)軌跡進(jìn)行仿真,得到機(jī)器人末端的角位移、速度和加速度曲線,仿真得到的曲線光滑無突變,使得機(jī)器人可以按照預(yù)定的軌跡完成任務(wù)。最后,簡單介紹RBF神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu),對(duì)RBF神經(jīng)網(wǎng)絡(luò)的matlab設(shè)計(jì)進(jìn)行研究;由于RBF神經(jīng)網(wǎng)絡(luò)規(guī)模大、學(xué)習(xí)速度快、函數(shù)逼近擬合功能優(yōu)的特點(diǎn),將其運(yùn)用到關(guān)節(jié)式機(jī)器人的軌跡規(guī)劃中,結(jié)合matlab工具箱的強(qiáng)大功能,對(duì)機(jī)器人的軌跡進(jìn)行仿真,得到機(jī)器人光滑的角位移曲線,神經(jīng)網(wǎng)絡(luò)得到的角位移曲線光滑,且與軌跡規(guī)劃得到的曲線基本一致,說明了把RBF神經(jīng)網(wǎng)絡(luò)應(yīng)用到工業(yè)機(jī)器人軌跡規(guī)劃中是合理可行的。
[Abstract]:As robotics becomes more and more mature. Enterprise manufacturers are increasingly using robots to accomplish their tasks. Among them, six-degree-of-freedom joint robots are widely used in manufacturing due to their large operating space and compact structure. It is the most widely used robot in production and manufacture. Because of its many advantages, this paper designs and analyzes a robot with a load of 6kg. First of all. The common types of robot are analyzed. Finally, the joint robot is selected to design, and then the structure of each joint is designed, and the solidworks 3D model diagram is established. Make the whole structure of robot more intuitive; In order to realize the motion, the robot must design its drive system. Therefore, according to the experience and other robot prototype, the motor and reducer of the industrial robot are selected. The D-H parameters are obtained by establishing the coordinate system of the connecting rod of the industrial robot, and the kinematics equation of the robot is established according to these parameters. The Matlab Robotics toolbox toolbox is used to model each member of the robot to form a complete robot model. The forward and inverse kinematics and workspace simulation are analyzed. Secondly, two trajectory planning methods of robot are introduced, which are joint space trajectory planning and Descartes space trajectory planning. Then the two are analyzed and compared, and the advantages and disadvantages of the two are synthesised. Finally, the joint space is chosen for trajectory planning in this paper. After selecting the trajectory planning method and combining with the powerful function of matlab, the trajectory of the robot is simulated, and the angular displacement, velocity and acceleration curves at the end of the robot are obtained. Finally, the structure of RBF neural network is briefly introduced, and the matlab design of RBF neural network is studied. Due to the large scale, fast learning speed and excellent function of function approximation, RBF neural network is applied to the trajectory planning of joint robot and combined with the powerful function of matlab toolbox. The trajectory of the robot is simulated and the smooth angular displacement curve of the robot is obtained. The angular displacement curve obtained by the neural network is smooth and basically consistent with the curve obtained by the trajectory planning. It is reasonable and feasible to apply RBF neural network to industrial robot trajectory planning.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP242

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 張紅霞;;國內(nèi)外工業(yè)機(jī)器人發(fā)展現(xiàn)狀與趨勢研究[J];電子世界;2013年12期

2 陳德林;楊志幫;;可重構(gòu)機(jī)器人設(shè)計(jì)理論與研究[J];制造業(yè)自動(dòng)化;2013年10期

3 姚輝苗;寇子明;;基于Solidworks的換繩組件關(guān)鍵零件參數(shù)化設(shè)計(jì)[J];煤礦機(jī)械;2013年05期

4 孫志杰;王善軍;張雪鑫;;工業(yè)機(jī)器人發(fā)展現(xiàn)狀與趨勢[J];吉林工程技術(shù)師范學(xué)院學(xué)報(bào);2011年07期

5 周雪峰;江勵(lì);朱海飛;蔡傳武;管貽生;張憲民;;一個(gè)模塊化機(jī)器人平臺(tái)的設(shè)計(jì)[J];華南理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2011年04期

6 李烈;;我國工業(yè)機(jī)器人發(fā)展現(xiàn)狀淺談[J];科技致富向?qū)?2011年05期

相關(guān)碩士學(xué)位論文 前10條

1 鄭家旺;多傳感器融合的涂裝機(jī)器人研發(fā)[D];武漢理工大學(xué);2011年

2 張忠奎;關(guān)節(jié)型機(jī)器人設(shè)計(jì)平臺(tái)的開發(fā)及軌跡規(guī)劃算法研究[D];山東理工大學(xué);2010年

3 陶智量;基于MATLAB的6自由度工業(yè)機(jī)器人仿真研究[D];吉林大學(xué);2009年

4 王長潤;機(jī)器人柔性焊裝線整線控制技術(shù)研究[D];合肥工業(yè)大學(xué);2009年

5 楊梁玉;RBF神經(jīng)網(wǎng)絡(luò)在認(rèn)股權(quán)證定價(jià)中的應(yīng)用研究[D];廈門大學(xué);2009年

6 劉超;通用型機(jī)器人關(guān)節(jié)運(yùn)動(dòng)控制器的研制[D];中南大學(xué);2008年

7 孔德鵬;地面移動(dòng)機(jī)器人的瞬態(tài)特性分析與仿真[D];南京理工大學(xué);2008年

8 李翠明;工業(yè)機(jī)器人迭代學(xué)習(xí)控制策略研究[D];蘭州理工大學(xué);2008年

9 吳泰巍;基于RBF神經(jīng)網(wǎng)絡(luò)的網(wǎng)格性能預(yù)測[D];吉林大學(xué);2008年

10 胡鵬;開放式機(jī)器人控制系統(tǒng)研究與實(shí)現(xiàn)[D];武漢科技大學(xué);2006年

，

本文編號(hào)：1388111