【摘要】：機(jī)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(jī)械手臂的大量應(yīng)用,有效提高了勞動(dòng)生產(chǎn)率,降低了成本,是一個(gè)國(guó)家工業(yè)化水平的標(biāo)志。因此,對(duì)高性能機(jī)械手臂運(yùn)動(dòng)控制系統(tǒng)的研究與應(yīng)用具有重要意義,受到專(zhuān)家學(xué)者們的青睞。本文主要是應(yīng)用群智能仿生算法對(duì)機(jī)械臂運(yùn)動(dòng)學(xué)逆解問(wèn)題的研究。首先,將運(yùn)動(dòng)學(xué)逆解方程的問(wèn)題轉(zhuǎn)換成n元函數(shù)求最值的問(wèn)題,即將三維立體函數(shù)簡(jiǎn)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(jī)械手臂運(yùn)動(dòng)路徑的對(duì)應(yīng)關(guān)系,把最優(yōu)路徑規(guī)劃問(wèn)題轉(zhuǎn)換成了目標(biāo)函數(shù)F求最大值的問(wèn)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(duì)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(wèn)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(duì)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(shí)通過(guò)引入混沌擾動(dòng)因子,實(shí)時(shí)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(guò)程中易陷入局部最優(yōu)的問(wèn)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(shí)降低了基本蟻群算法的時(shí)間復(fù)雜度,更好地解決了最優(yōu)的路徑選擇問(wèn)題。最后,本文以SR165型機(jī)械手臂為例,建立基于MATLAB Robotics Toolbox的仿真實(shí)驗(yàn)平臺(tái),并通過(guò)仿真實(shí)驗(yàn)以及與基本蟻群算法的仿真實(shí)驗(yàn)結(jié)果對(duì)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18;TP241

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本文編號(hào)：2378186