【摘要】：智能化是人工智能時(shí)代的機(jī)器人技術(shù)的發(fā)展目標(biāo)之一,機(jī)器人技術(shù)研究的高級(jí)目標(biāo)是使得機(jī)器人可以像人一樣思考、做事。如果要讓機(jī)器人能夠做出與人相似的智能行為,這需要機(jī)器人有很強(qiáng)的判斷能力,以及在變化的環(huán)境中學(xué)習(xí)以獲得經(jīng)驗(yàn)知識(shí)的能力。目前的技術(shù)還不能使機(jī)器人達(dá)到這方面的要求,所以針對(duì)特定的任務(wù),使用者需要對(duì)機(jī)器人進(jìn)行編程。機(jī)器人的編程工作不僅需要專(zhuān)業(yè)的技術(shù)知識(shí),而且過(guò)程復(fù)雜繁瑣,費(fèi)時(shí)費(fèi)力,這無(wú)法達(dá)到人類(lèi)對(duì)機(jī)器人給予的期望。為提高完成任務(wù)的能力,讓機(jī)器人在與人的交互過(guò)程中習(xí)得新技能,本課題將研究一種使機(jī)器人通過(guò)對(duì)人體手臂運(yùn)動(dòng)的模仿,自動(dòng)生成可執(zhí)行運(yùn)動(dòng)軌跡的方法。與傳統(tǒng)的通過(guò)手工編程為機(jī)器人規(guī)劃軌跡的方法相比,本課題的研究將大大降低編程的難度和周期,提高機(jī)器人編程的自動(dòng)化水平,增強(qiáng)人與機(jī)器人交互方式的智能化以及機(jī)器人的易用性,為機(jī)器人在更廣闊領(lǐng)域的應(yīng)用奠定基礎(chǔ)。首先,本文針對(duì)如何獲取人體手臂運(yùn)動(dòng)數(shù)據(jù)的問(wèn)題,對(duì)多相機(jī)測(cè)量三維空間點(diǎn)坐標(biāo)的原理作了介紹。通過(guò)對(duì)光學(xué)運(yùn)動(dòng)捕捉系統(tǒng)的學(xué)習(xí)和使用,實(shí)現(xiàn)了對(duì)運(yùn)動(dòng)數(shù)據(jù)的獲取。其次,針對(duì)如何對(duì)機(jī)器人進(jìn)行運(yùn)動(dòng)學(xué)建模的問(wèn)題,以一種六自由度的機(jī)械臂為對(duì)象,采用D-H法建立其運(yùn)動(dòng)學(xué)模型,確定了機(jī)器人的D-H參數(shù)及連桿間的位姿變換矩陣,求解了正、逆運(yùn)動(dòng)學(xué)方程,討論了逆解多解的取值問(wèn)題,并對(duì)所求得的解進(jìn)行了仿真驗(yàn)證。然后,針對(duì)如何描述人體手臂運(yùn)動(dòng)的問(wèn)題,對(duì)手臂的運(yùn)動(dòng)進(jìn)行了分析,并做相應(yīng)的簡(jiǎn)化,使用少量的關(guān)節(jié)轉(zhuǎn)動(dòng)角度值來(lái)描述手臂的運(yùn)動(dòng)。在模糊邏輯理論的基礎(chǔ)上引入了自適應(yīng)神經(jīng)模糊推理系統(tǒng),利用神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)能力進(jìn)行復(fù)雜的模糊推理,以建立復(fù)雜非線性系統(tǒng)的模型。最后,針對(duì)如何生成機(jī)器人可執(zhí)行軌跡的問(wèn)題,將獲得的手臂運(yùn)動(dòng)數(shù)據(jù)轉(zhuǎn)換為相應(yīng)的機(jī)器人關(guān)節(jié)路徑點(diǎn),并在關(guān)節(jié)空間中,使用過(guò)關(guān)節(jié)路徑點(diǎn)并帶拋物線擬合的線性插值方法來(lái)生成機(jī)器人的運(yùn)動(dòng)軌跡。在相關(guān)理論算法的基礎(chǔ)之上,編寫(xiě)了機(jī)器人軌跡自動(dòng)生成與仿真平臺(tái),實(shí)現(xiàn)了對(duì)示教者運(yùn)動(dòng)數(shù)據(jù)的實(shí)時(shí)采集、對(duì)機(jī)器人的正確控制以及對(duì)生成軌跡的三維仿真。
[Abstract]:Intelligence is one of the development goals of robot technology in the era of artificial intelligence. The advanced goal of robot technology research is to make robots think and do things like human beings. If robots are to be able to perform intelligent behaviors similar to human beings, they need to have a strong judgment ability and the ability to learn to gain experiential knowledge in a changing environment. The current technology can not meet the requirements of the robot, so for specific tasks, the user needs to program the robot. The programming of robots requires not only professional technical knowledge, but also complicated, time-consuming and laborious processes, which cannot meet the expectations of human beings for robots. In order to improve the ability of accomplishing tasks and to acquire new skills in the process of interaction with human beings, this paper will study a method that can automatically generate executable trajectory by imitating the movement of human arm. Compared with the traditional manual programming method for robot trajectory planning, the research of this topic will greatly reduce the difficulty and cycle of programming, and improve the automation level of robot programming. The intelligentization of the interaction between human and robot and the ease of use of the robot lay the foundation for the application of robot in a wider field. Firstly, this paper introduces the principle of measuring the coordinate of three dimensional point by multi-camera aiming at how to obtain the motion data of human arm. Through the study and use of optical motion capture system, the motion data acquisition is realized. Secondly, aiming at the problem of how to model the kinematics of the robot, taking a six-degree-of-freedom manipulator as an object, the kinematics model is established by using D-H method, the D-H parameters and the position and attitude transformation matrix between the connecting rod are determined, and the positive solution is obtained. In the inverse kinematics equation, the value problem of multiple solutions of inverse solutions is discussed, and the obtained solutions are verified by simulation. Then, aiming at the problem of how to describe the movement of human arm, the motion of arm is analyzed and simplified, and the motion of arm is described by a small amount of angle of joint rotation. On the basis of fuzzy logic theory, an adaptive neural fuzzy inference system is introduced. The complex fuzzy reasoning is carried out by using the learning ability of neural network, and the model of complex nonlinear system is established. Finally, aiming at the problem of how to generate the executable trajectory of the robot, the obtained arm motion data is converted into the corresponding robot joint path point, and in the joint space, A linear interpolation method with parabola fitting is used to generate the trajectory of the robot. Based on the related theories and algorithms, a robot trajectory automatic generation and simulation platform is developed, which realizes the real-time acquisition of the motion data of the teacher, the correct control of the robot and the 3D simulation of the generated trajectory.
【學(xué)位授予單位】：華僑大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP242

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本文編號(hào)：2244710