【摘要】：機械手臂是現(xiàn)代工業(yè)代替人做高危和流水作業(yè)的重要工具,隨著工業(yè)機械手臂的大量應(yīng)用,有效提高了勞動生產(chǎn)率,降低了成本,是一個國家工業(yè)化水平的標(biāo)志。因此,對高性能機械手臂運動控制系統(tǒng)的研究與應(yīng)用具有重要意義,受到專家學(xué)者們的青睞。本文主要是應(yīng)用群智能仿生算法對機械臂運動學(xué)逆解問題的研究。首先,將運動學(xué)逆解方程的問題轉(zhuǎn)換成n元函數(shù)求最值的問題,即將三維立體函數(shù)簡化成二維平面函數(shù),再利用所建立的目標(biāo)函數(shù)F與機械手臂運動路徑的對應(yīng)關(guān)系,把最優(yōu)路徑規(guī)劃問題轉(zhuǎn)換成了目標(biāo)函數(shù)F求最大值的問題。并在此基礎(chǔ)上,提出了基于混沌蟻群算法的路徑尋優(yōu)方法。在基本蟻群算法與混沌理論結(jié)合的具體應(yīng)用中,針對基本蟻群算法在搜索初期,各路徑上的信息素含量相同,因此收斂速度較慢的問題,將混沌理論的混沌初始化引入到基本蟻群算法中,使之對各條路徑投放不同量的信息素,從而加快了基本蟻群算法的收斂速度,同時通過引入混沌擾動因子,實時調(diào)整信息素的更新,有效地避免了基本蟻群算法在搜索過程中易陷入局部最優(yōu)的問題。因此,混沌蟻群算法不僅提高了收斂速度、準(zhǔn)確性和啟發(fā)性,同時降低了基本蟻群算法的時間復(fù)雜度,更好地解決了最優(yōu)的路徑選擇問題。最后,本文以SR165型機械手臂為例,建立基于MATLAB Robotics Toolbox的仿真實驗平臺,并通過仿真實驗以及與基本蟻群算法的仿真實驗結(jié)果對比,證明了混沌蟻群算法的可行性與優(yōu)越性。
[Abstract]:Mechanical arm is an important tool for modern industry to replace people to do high risk and flow work. With the extensive application of industrial mechanical arm, the labor productivity has been improved effectively, and the cost has been reduced. It is the symbol of a country's industrialization level. Therefore, the research and application of high performance manipulator motion control system is of great significance and is favored by experts and scholars. In this paper, the inverse kinematics problem of manipulator is studied by using swarm intelligence bionic algorithm. First of all, the inverse kinematics equation is transformed into the problem of finding the maximum value of the n-variable function, that is, the three-dimensional function is simplified into a two-dimensional plane function, and then the corresponding relation between the objective function F and the motion path of the manipulator is used. The optimal path planning problem is transformed into the problem of finding the maximum value of the objective function F. On this basis, a path optimization method based on chaotic ant colony algorithm is proposed. In the concrete application of the combination of basic ant colony algorithm and chaos theory, aiming at the problem that the pheromone content in every path is the same at the initial searching stage of the basic ant colony algorithm, so the convergence speed is slow. The chaos initialization of chaos theory is introduced into the basic ant colony algorithm, which puts different pheromones on different paths, thus speeding up the convergence speed of the basic ant colony algorithm, and at the same time introducing the chaos perturbation factor. The updating of pheromone is adjusted in real time to effectively avoid the problem that the basic ant colony algorithm is prone to local optimization in the search process. Therefore, chaotic ant colony algorithm not only improves the convergence speed, accuracy and enlightenment, but also reduces the time complexity of the basic ant colony algorithm and solves the optimal path selection problem better. Finally, taking the SR165 manipulator as an example, a simulation experiment platform based on MATLAB Robotics Toolbox is established, and the feasibility and superiority of chaotic ant colony algorithm are proved by comparing the simulation results with the basic ant colony algorithm.
【學(xué)位授予單位】：遼寧科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TP241

【參考文獻】

相關(guān)期刊論文 前10條

1 陶洪峰;董曉齊;楊慧中;;參考軌跡更新的點到點迭代學(xué)習(xí)控制算法優(yōu)化及應(yīng)用[J];控制理論與應(yīng)用;2016年09期

2 白云飛;叢明;楊小磊;劉冬;;基于6參數(shù)模型的6R串聯(lián)機器人運動學(xué)參數(shù)辨識[J];機器人;2015年04期

3 吳昊;;基于勒貝格測度與wigner變換的混沌判定方法[J];山東工業(yè)技術(shù);2013年10期

4 曲大鵬;王興偉;黃敏;;移動對等網(wǎng)絡(luò)中的感知蟻群路由算法[J];計算機學(xué)報;2013年07期

5 張文超;譚思超;高璞珍;;基于Lyapunov指數(shù)的搖擺條件下自然循環(huán)流動不穩(wěn)定性混沌預(yù)測[J];物理學(xué)報;2013年06期

6 林妍;吳瑾;樊鎖海;;圖著色和標(biāo)號問題的蟻群優(yōu)化算法[J];數(shù)學(xué)的實踐與認識;2012年17期

7 溫瑞;李大偉;欒孝豐;;基于蟻群算法的機器人路徑規(guī)劃[J];計算機與數(shù)字工程;2012年05期

8 劉洋;章衛(wèi)國;李廣文;;基于改進PRM算法的路徑規(guī)劃研究[J];計算機應(yīng)用研究;2012年01期

9 敬明;鄧衛(wèi);;結(jié)合概率搜索定界的入度統(tǒng)計最短路徑算法[J];交通運輸系統(tǒng)工程與信息;2011年06期

10 王雪松;潘杰;程玉虎;;基于知識遷移的Ant-Q算法[J];電子學(xué)報;2011年10期

相關(guān)博士學(xué)位論文 前1條

1 陳天石;演化算法的計算復(fù)雜性研究[D];中國科學(xué)技術(shù)大學(xué);2010年

相關(guān)碩士學(xué)位論文 前2條

1 吳強;基于工作空間離散化的重載多足步行機器人控制技術(shù)研究[D];吉林大學(xué);2015年

2 楊學(xué)峰;蟻群算法求解TSP問題的研究[D];吉林大學(xué);2010年

，

本文編號：2378186