【正文】 和高度，10毫米的厚度。鏈接o3f和矩形空心梁與30mm的基礎(chǔ)和高度工型鋼，l0mm法蘭和6mm網(wǎng)。梁競，通用汽車與8mm的堅(jiān)實(shí)基礎(chǔ)和30mm高的矩形。圖8 梯形運(yùn)動姿態(tài)圖9中回應(yīng)的是機(jī)械手，相比之下，圖10中提高初始的反應(yīng)，在其中所有的鏈接和機(jī)械手的矩形截面梁的堅(jiān)實(shí)基礎(chǔ)，用30毫米，高度的差異是曲線，c和h的曲線積分，二是垂直位移的末端，177。% s177。，因?yàn)樵谙嗤牟椒ゲ粩嗉涌?，保持振動瓣膜差不多一樣，它對這整個(gè)系統(tǒng)中來說，仍然改善系統(tǒng)的剛度，幾乎相當(dāng)于初始制度，針對大規(guī)模的平面并聯(lián)機(jī)構(gòu)在該系統(tǒng)相比下降了30%，這樣的初始優(yōu)化是有效的。圖圖10 動態(tài)響應(yīng)結(jié)論本文設(shè)計(jì)了一種新型三自由度機(jī)械手變量的敏感性進(jìn)行了研究在adams環(huán)境中，可以得出以下結(jié)論：1）機(jī)器人具有較大的水平剛度，最終水平位移，效應(yīng)主要是由機(jī)械手垂直變形造成的，因此，更重要的是增加的幅度比剛度豎向剛度。2）參數(shù)ixx，iyy并鏈接39。截面剛度izz有不同的效應(yīng)，iyy已經(jīng)對垂直剛度的影響最大，ixx在第二位的是，ixx具有在垂直剛度的影響最小，他們都較少對水平比垂直剛度剛度。3）橫截面的不同環(huán)節(jié)都有不同的影響，連線豎向剛度ab和德應(yīng)該使用區(qū)扭轉(zhuǎn)常數(shù)和慣性力矩大，如變形、長方形、橫梁km，線 03f應(yīng)該使用區(qū)段形梁等重大時(shí)刻轉(zhuǎn)動慣量、橫梁gk，和gm 可以使用盡可能的一小部分，從而降低了質(zhì)量。4）最佳的線性驅(qū)動器的相對位置可以減少變形，最好的位置是垂直的平行結(jié)構(gòu)。5）改進(jìn)的機(jī)械手的動態(tài)分析表明該優(yōu)化設(shè)計(jì)方法研究的基礎(chǔ)上的效率。參考文獻(xiàn)[l]dasgupta b，mmthyunjayab t s。the stewart platform manipulator：a review。mechat~m and machine theory，200o。35(1)：15—40[ ] xi f，zhang d，xu z，et al。a parative study on tripod u ts for machine lo0ls。intemational journal of machine toolsamp。manufacture，2003，43(7)：721—730[ ]zhang d，gosselin c m。kinetostatic analysis and optimization of the tricept machine tool family。in：proceedings of year 2000 parallel kinematic machines international conference，ann arbor，michigan，2001，174—188 [ ]gosselin c m，angeles j。a globe preference index for the kinematic optimum of robotic manipulator。asme journal of mechanical，l991，113(3)：220—226 [ ]gao f，i，iux j，gruverw a。performance evaluation of twodegreeoffreedom planar parallel robots。mechanism and machine theory，l998，33(6)：661668[ ] huang t，li m，li z x，et al。optimal kinematic design of 2dof oaralel manipulator with well shahed workspace bounded by a specified conditioning index ieee transactions of robot and automation，2004，20，(3)：538—543 [ ] gosselin c m，wang j。singularity loci ofplanarparallel manipulator with revoluted actuators。robotics and autonomousm，1997，2l(4)：377 398 [ ] yiu y k，cheng h，xiong z h，et al。on the dvnamies of parallel mmfipulators proc。ofieee inemational conference on roboticsamp。 automation。20o1。3766 3771 [ ] chakarov d。study ofthe antagoni~ie stifness of parallel manipulators with actuation redundancy。mechanism and machinetheory，2004，39(6)：583—60l [ 10 ] shab~a a a。dynamics of multibody systems。cambridge：cambridge university press，l998。2703 l0六自由度機(jī)器人介紹篇四improved genetic algorithm and its performance analysisabstract: although genetic algorithm has bee very famous with its global searching, parallel puting, better robustness, and not needing differential information during r, it also has some demerits, such as slow convergence this paper, based on several general theorems, an improved genetic algorithm using variant chromosome length and probability of crossover and mutation is proposed, and its main idea is as follows : at the beginning of evolution, our solution with shorter length chromosome and higher probability of crossover and mutation。and at the vicinity of global optimum, with longer length chromosome and lower probability of crossover and y, testing with some critical functions shows that our solution can improve the convergence speed of genetic algorithm significantly , its prehensive performance is better than that of the genetic algorithm which only reserves the best c algorithm is an adaptive searching technique based on a selection and reproduction mechanism found in the natural evolution process, and it was pioneered by holland in the has bee very famous with its global searching, parallel puting, better robustness, and not needing differential information during r, it also has some demerits, such as poor local searching, premature converging, as well as slow convergence recent years, these problems have been this paper, an improved genetic algorithm with variant chromosome length and variant probability is g with some critical functions shows that it can improve the convergence speed significantly, and its prehensive performance is better than that of the genetic algorithm which only reserves the best section 1, our new approach is h optimization examples, in section 2, the efficiency of our algorithm is pared with the genetic algorithm which only reserves the best section 3 gives out the y, some proofs of relative theorems are collected and presented in ption of the algorithm some theorems before proposing our approach, we give out some general theorems(seeappendix)as follows: let us assume there is just one variable(multivariable can be spanided into many sections, one section for one variable)x ∈ [ a, b ] , x ∈ r, and chromosome length with binary encoding is m 1minimal resolution of chromosome is s = ba 2l1theorem 2weight value of the ith bit of chromosome iswi = bai1(i = 1,2,…l)2l1theorem 3mathematical expectation ec(x)of chromosome searching step with onepoint crossover is ec(x)= bapc 2lwhere pc is the probability of m 4mathematical expectation em(x)of chromosome searching step with bit mutation is em(x)=(ba)pm mechanism of algorithmduring evolutionary process, we presume that value domains of variable are fixed, and the probability of crossover is a constant, so from theorem 1 and 3, we know that the longer chromosome length is, the smaller searching step of chromosome, and the higher resolution。and vice ile, crossover probability is in direct proportion to searching theorem 4, changing the length of chromosome does not affect searching step of mutation, while mutation probability is also in direct proportion to searching the beginning of evolution, shorter length chromosome(can be too shorter, otherwise it is harmful to population spanersity)and higher probability of crossover and mutation increases searching step, which can carry out greater domain searching, and avoid falling into local at the vicinity of global optimum, longer length chromosome and lower probability of crossover and mutation will decrease searching step, and longer length chromosome also improves resolution of mutation, which avoid wandering near the global optimum, and speeds up algorithmy, it should be pointed ou