本文關(guān)鍵詞：七自由度乒乓球機(jī)器人的視覺檢測(cè)及擊球決策研究　出處：《哈爾濱工業(yè)大學(xué)》2015年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 乒乓球機(jī)器人 七自由度機(jī)械臂 解析逆運(yùn)動(dòng)學(xué) 雙目視覺 運(yùn)動(dòng)模糊 擊球決策 支持向量回歸

【摘要】：乒乓球運(yùn)動(dòng)要求參與者具備快速反應(yīng)和快速?zèng)Q策的能力�；谶@些特點(diǎn)，乒乓球機(jī)器人成為研究高速、智能機(jī)器人系統(tǒng)的理想實(shí)驗(yàn)平臺(tái)。本文以七自由度乒乓球機(jī)器人為背景，進(jìn)行了三方面的研究工作：七自由度機(jī)械臂的運(yùn)動(dòng)控制、高速運(yùn)動(dòng)球體的視覺跟蹤和基于學(xué)習(xí)的球擊決策。針對(duì)帶關(guān)節(jié)限位的起自由度機(jī)械臂，本文提出了一種改進(jìn)的基于“臂角”參數(shù)法的解析逆運(yùn)動(dòng)學(xué)求解方案。七自由度機(jī)械臂的冗余運(yùn)動(dòng)被參數(shù)化為“臂角”，即機(jī)械臂與參考平面之間的二面角�；谏鲜鰠�(shù)化方法，關(guān)節(jié)限位對(duì)于冗余參數(shù)的限制能夠表示為可以解析求解的三角不等式。而后，在每個(gè)自運(yùn)動(dòng)流型上“臂角”的取值范圍可以簡(jiǎn)明且易于實(shí)現(xiàn)的三角運(yùn)算得到；最后對(duì)于取值范圍內(nèi)的每個(gè)“臂角”值，即可求得符合關(guān)節(jié)限位的關(guān)節(jié)配置。本文采用數(shù)學(xué)證明和實(shí)驗(yàn)驗(yàn)證兩種方法證明了上述方法的正確性。另外，本文還采用MATLAB符號(hào)運(yùn)算實(shí)現(xiàn)了機(jī)械臂的正運(yùn)動(dòng)學(xué)；并通過對(duì)比常用微分逆運(yùn)動(dòng)學(xué)算法，選擇了加權(quán)最小范數(shù)法作為其自由度機(jī)械臂的微分逆運(yùn)動(dòng)學(xué)算法。針對(duì)高速運(yùn)動(dòng)球體在圖像中引入的“運(yùn)動(dòng)模糊”，本文提出了一種基于雙目視覺的圖像處理流程：首先給予背景去除法從圖像中提取球體的對(duì)應(yīng)區(qū)域；而后通過最小化圖像方向?qū)?shù)的L2范數(shù)來(lái)估計(jì)模糊參數(shù)并采用Tichardson-Lucy算法得到去模糊圖像；最后采用基于RANSAC的圓擬合算法得到乒乓球的中心位置并進(jìn)而計(jì)算出乒乓球在三維空間的位置和速度。上述方法可以有效減輕運(yùn)動(dòng)模糊現(xiàn)象對(duì)測(cè)量精度的影響，從而實(shí)現(xiàn)高速乒乓球的精確視覺跟蹤。針對(duì)擊球決策問題，，本文提出了一種基于支持向量回歸的擊球策略學(xué)習(xí)方法。機(jī)器人的擊球過程被形式化為擊球評(píng)價(jià)函數(shù)，該函數(shù)以來(lái)球狀態(tài)和擊球軌跡參數(shù)為輸入，以回報(bào)值為輸出。該函數(shù)由ε支持向量回歸算法對(duì)經(jīng)驗(yàn)數(shù)據(jù)集進(jìn)行泛化二得到。在在線決策過程中，采用多初值擬牛頓法最大化擊球評(píng)價(jià)函數(shù)以求解出最優(yōu)擊球軌跡。由于基于學(xué)習(xí)的擊球決策不依賴于物理模型，因此它可以有效避免非建模動(dòng)態(tài)和模型參數(shù)誤差等因素對(duì)擊球成功率的影響。本文提出的所以算法都在七自由度乒乓球機(jī)器人系統(tǒng)上實(shí)現(xiàn)，機(jī)械臂軌跡規(guī)劃實(shí)驗(yàn)、視覺跟蹤實(shí)驗(yàn)和擊球等驗(yàn)證了算法的有效性。
[Abstract]:Table tennis requires participants to have rapid response and rapid decision-making ability. Based on these characteristics, the table tennis robot is the research of high speed, an ideal experimental platform for intelligent robot system. Based on the background of table tennis robot with seven degrees of freedom, the study of three aspects: seven degrees of freedom manipulator motion control of high speed ball the visual tracking and learning based on decision. To strike the ball joint limit the degree of freedom manipulator, this paper presents an improved method based on arm angle parameter analytical inverse kinematics solution. Redundant motion of seven degree of freedom manipulator is parameterized as the "arm angle", namely machinery the arm and the reference plane. The dihedral angle based on the parametric method, the joint limit for redundant parameter constraints can be expressed as the triangle inequality can be solved analytically. Then, in each self moving The flow pattern on the "arm angle" range can be simple and easy to realize the triangle operation; each of the last "for the range of the arm angle value can be obtained with joint spacing joint configuration. In this paper, two methods to verify the validity of the method is proved by mathematical proof and experiments. In addition, this paper also the MATLAB symbol operation realizes kinematics of mechanical arm; and through the comparison of common differential inverse kinematics algorithm, the weighted minimum norm method as the differential degree of freedom manipulator inverse kinematics algorithm for high speed motion in the image into the sphere of" motion blur ", this paper proposes an image processing process of binocular vision based on the given background removal method: firstly extracting the corresponding areas of the spheres from the image; and then through the L2 norm minimization direction image derivative to estimate parameters and fuzzy Tichardson-Lucy algorithm is used to get the fuzzy image; finally the center position of RANSAC circle fitting method to get the table tennis and table tennis and then calculate the position and velocity in three-dimensional space. Based on the above method can effectively reduce motion blur on measurement accuracy, accurate visual tracking to realize the high speed for hitting of table tennis. The decision problem, this paper proposes a method of learning strategy based on support vector regression. Ball hitting process of the robot is formalized as a batting evaluation function, this function since the ball state and stroke trajectory parameters as input and return to value as output. The function by epsilon support vector regression algorithm of empirical data sets were obtained two generalization online. In the decision-making process, the initial value of the quasi Newton method to maximize the batting evaluation function to solve the optimal path based on learning. Due to stroke A decision does not depend on the physical model, so it can effectively avoid the influence of non modeling dynamics and parameter error and other factors on the success rate of stroke. The proposed algorithm so in the system of seven degrees of freedom table tennis robot, manipulator trajectory planning experiment, visual tracking experiments and hitting the availability of the algorithm is verified.

【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP242


本文編號(hào)：1357936