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JX0467 步行 机器人 头部 身体 结构 设计

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JX0467-双足步行机器人头部及身体结构的设计,JX0467,步行,机器人,头部,身体,结构,设计

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南京理工大学泰州科技学院毕业设计（论文）任务书系部：机械工程系专 业：机械工程及自动化学 生 姓 名：徐昕晏学 号：05010244设计(论文)题目：两足行走机器人身体及头部结构部分设计起 迄 日 期:2009年 3月09 日 6月14日设计(论文)地点:南京理工大学泰州科技学院指 导 教 师:路建萍 刘艳 专业负责人:龚光容发任务书日期: 2009年 2月 26 日毕 业 设 计（论 文）任 务 书1本毕业设计（论文）课题应达到的目的： 通过本设计，使学生熟悉机械产品开发性设计的一般过程，培养学生综合运用所学基础理论、专业知识和各项技能，着重培养设计、计算、分析和解决问题的能力，进而总结、归纳和获得合理结论，进行较为系统的工程训练，初步锻炼科研能力，提高论文撰写和技术表述能力。为实际工作奠定基础，达到人才培养的目的与要求。2本毕业设计（论文）课题任务的内容和要求（包括原始数据、技术要求、工作要求等）：技术要求：本机器人的总体功能为模拟人体动作。设计两足行走机器人身体及头部结构设计，能够实现摇头功能。工作任务：（1） 查阅资料15篇以上，翻译外文资料3000字，撰写文献综述和开题报告。（2） 完成身体及头部结构总体设计，绘制功能原理图 1 份。（3） 绘制装配图 1 份 （0号图纸）； 部件图 1份；零件图若干。（其中，至少有1张图纸使用计算机绘制）（4） 设计说明书1.5万字左右。(打印)毕 业 设 计（论 文）任 务 书3对本毕业设计（论文）课题成果的要求包括毕业设计论文、图表、实物样品等：(1)开题报告(含文献综述)；(2)外文资料翻译：3,000 汉字以上；(3)总体装配图： 1 张； (4)部件装配图1张；(5)主要零件图：若干张 (总图数约合2张0号图)；(6)毕业设计说明书,字数不少于10,000字,并附有200300汉字的中文摘要及相应的英文摘要。所有设计图表、论文撰写、参考文献标记等均应符合本院的规范化要求。4主要参考文献：1 马香峰.机器人机构学M. 北京: 机械工业出版社, 1991.2 龚振邦等.机器人机械设计M. 北京：电子工业出版社，1995年6月.3 吴瑞祥.机器人技术及应用M. 北京：北京航空航天大学出版社，1994. 4 熊有伦.机器人技术基础M.武汉：华中理工大学出版社，1996.5 王志良.竞赛机器人制作技术M.北京：机械工业出版社，2007.6 马香峰等.工业机器人的操作机设计M. 北京：冶金工业出版社，1996年9月.7 余达太，马香峰. 工业机器人应用工程M. 北京：冶金工业出版社，1999年4月.8 机械设计手册编委会. 机械设计手册M. 北京：机械工业出版社，2007年7月.9 Ernest L. Hall et l. Robotics: A User-Friendly IntroductionM. New York:CBS College Publishing,1985.10 Yoram Koren. Robotics for EngineersM. McGraw-Hill Book Company, 1985.11 成大先.机械设计手册（第4版）.北京：化学工业出版社，2002.12 白井良明著. 王棣棠译. 机器人工程M. 北京：科学出版社，2001年2月.13 秦志强等. 基础机器人制作与编程M. 北京： 电子工业出版社，2007.14 费仁元等.机器人机械设计和分析.北京：北京工业大学出版社，1998.15 陈继荣.智能电子创新制作M. 北京：科技出版社，2007. 毕 业 设 计（论 文）任 务 书5本毕业设计（论文）课题工作进度计划：起 迄 日 期工 作 内 容2009年 3月 9 日 3月15日 3月16日 3月22日3月23日 3月29日3月30日 4月5 日4月6 日 4月12日4月13日 4月26日4月27日 5月3 日5月4 日 5月10日5月11日 5月17 日5月18日 5月24 日5月25日 5月31 日6月1 日 6月7 日6月8 日 6月14 日选题，发放审题表及任务书，熟悉课题完成外文资料翻译完成开题报告(包含文献综述)查阅相关资料，提出几种可行性方案 确定合理方案，进行总体方案论证进行结构部分设计计算相关结构参数，选择标准部件，熟悉相关软件对设计方案进行评价与修改，使之完善绘制总装配图，绘制零件图整理相关资料，撰写并打印设计说明书初步提交设计成果（包括图纸及论文），整改正式提交设计成果和设计说明书准备论文答辩所在专业审查意见：负责人： 年 月 日系部意见：系部主任： 年 月 日PAGE 05010244()弰 :2009 309 614(): : : : 2009 2 26 1 2 幦嶯弰 153000档 弰 1 1 0 11 1.5() 3 (1)()(2)3,000 (3) 1 (4)1(5) (20)(6),10,000,200300 淶4 1 .M. : , 1991. 2 .M. 磬19956. 3 .M. 磬1994. 4 .M.人磬1996. 5 .M.磬2007. 6 .M. 磬19969. 7 . M. 磬19994. 8 . M. 磬20077. 9 Ernest L. Hall et l. Robotics: A User-Friendly IntroductionM. New York: CBS College Publishing,1985. 10 Yoram Koren. Robotics for EngineersM. McGraw-Hill Book Company, 1985. 11 .4棩.磬2002. 12 . . M. 磬20012. 13 . M. 磬2007. 14 .磬1998. 15 .M. 磬2007. 5 2009 3 9 315 316 322 323 329 330 45 46 412 413 426 427 53 54 510 511 517 518 524 525 531 61 67 68 614 飬 () 巽 FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem Michael Montemerlo and Sebastian Thrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 mmde, thrun Daphne Koller and Ben Wegbreit Computer Science Department Stanford University Stanford, CA 94305-9010 koller, ben Abstract The ability to simultaneously localize a robot and ac- curately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. How- ever, few approaches to this problem scale up to handle the very large number of landmarks present in real envi- ronments. Kalman fi lter-based algorithms, for example, require time quadratic in the number of landmarks to in- corporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on an ex- act factorization of the posterior into a product of con- ditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far be- yond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real- world data. Introduction Theproblemofsimultaneouslocalizationandmapping,also knownas SLAM, has attractedimmenseattentionin the mo- bile robotics literature.SLAM addresses the problem of building a map of an environment from a sequence of land- mark measurements obtained from a moving robot. Since robotmotionis subject to error,the mappingproblemneces- sarilyinducesarobotlocalizationproblemhencethename SLAM. The ability to simultaneously localize a robot and accurately map its environment is considered by many to be a key prerequisite of truly autonomous robots 3, 7, 16. The dominant approach to the SLAM problem was in- troduced in a seminal paper by Smith, Self, and Cheese- man 15.This paper proposed the use of the extended Kalman fi lter (EKF) for incrementally estimating the poste- rior distribution over robot pose along with the positions of the landmarks. In the last decade, this approach has found widespread acceptance in fi eld robotics, as a recent tutorial paper 2 documents. Recent research has focused on scal- ing this approach to larger environments with more than a few hundred landmarks 6, 8, 9 and to algorithms for han- dling data association problems 17. AkeylimitationofEKF-basedapproachesistheircompu- tational complexity. Sensor updates require time quadratic in the number of landmarks ? to compute. This complex- ity stems fromthe fact thatthe covariancematrixmaintained bytheKalmanfi ltershas? ? elements,all ofwhichmust be updated even if just a single landmark is observed. The quadratic complexity limits the number of landmarks that can be handled by this approach to only a few hundred whereasnaturalenvironmentmodelsfrequentlycontainmil- lions of features. This shortcoming has long been recog- nized by the research community 6, 8, 14. In this paper we approach the SLAM problem from a Bayesian point of view. Figure 1 illustrates a generative probabilistic model (dynamic Bayes network) that underlies the rich corpus of SLAM literature. In particular, the robot poses, denoted? ? ? ? ?, evolve over time as a function of the robot controls, denoted? . Each of the land- mark measurements, denoted? , is a function of the position of the landmark measured and of the robot pose at the time the measurementwas taken. From this diagramit is evident that the SLAM problem exhibits important condi- tionalindependences. Inparticular,knowledgeoftherobots path ? ? ? ? ? rendersthe individuallandmarkmeasure- ments independent. So for example, if an oracle providedus with the exact path of the robot, the problem of determin- ing the landmark locations could be decoupled into ? inde- pendent estimation problems, one for each landmark. This observation was made previously by Murphy 12, who de- veloped an effi cient particle fi ltering algorithm for learning grid maps. Basedonthisobservation,this paperdescribesaneffi cient SLAM algorithmcalled FastSLAM. FastSLAM decomposes the SLAM problem into a robot localization problem, and a collection of landmark estimation problems that are con- ditioned on the robot pose estimate. As remarked in 12, this factored representation is exact, due to the natural con- ditional independences in the SLAM problem. FastSLAM uses a modifi ed particle fi lter for estimating the posterior over robot paths. Each particle possesses ? Kalman fi l- ters that estimate the ? landmark locations conditioned on the path estimate. The resulting algorithm is an instance of the Rao-Blackwellized particle fi lter 5, 13. A naive im- plementation of this idea leads to an algorithm that requires ? ? time, where is the number of particles in the s1s 2 s t u 2 u t 2 1 z 1 z 2 s 3 u 3 z 3 z t .?.?. Figure 1: The SLAM problem: The robot moves from pose? through a sequence of controls, ? ? ? ? ? ?. As it moves, it observes nearby landmarks. At time , it observes landmark ? out of two landmarks, ? ? ? . The measurement is denoted ? (range and bearing). At time , it observes the other landmark, ? , and at time , it observes ? again. The SLAM problem is concerned with estimating the locations of the landmarks and the robots path from the controls ? and the measurements . The gray shading illustrates a conditional independence relation. particle fi lter and ? is the number of landmarks. We de- velop a tree-based data structure that reduces the running time of FastSLAM to?! #“ ? ? , making it signifi cantly faster than existing EKF-based SLAM algorithms. We also extend the FastSLAM algorithm to situations with unknown data association and unknown number of landmarks, show- ing that our approach can be extended to the full range of SLAM problems discussed in the literature. Experimental results using a physical robot and a robot simulator illustrate that the FastSLAM algorithm can han- dle orders of magnitude more landmarks than present day approaches. We also fi nd that in certain situations, an in- creased number of landmarks ? leads to a mild reduction of the number of particles needed to generate accurate mapswhereas in others the number of particles required for accurate mapping may be prohibitively large. SLAM Problem Defi nition The SLAM problem, as defi ned in the rich body of litera- ture on SLAM, is best described as a probabilistic Markov chain. The robots pose at time $ will be denoted ? . For robots operating in the planewhichis the case in all of our experimentsposes are comprised of a robots % - / ? 6 is the index of the landmark perceived at time $ . For example, in Figure 1, we have 1 ?, and1AB-C/ , since the robot fi rst observes landmark?, thenlandmark ? , and fi nally landmark?fora second time. Many measurement models in the literature assume that the robot can measure range and bearing to landmarks, con- founded by measurement noise. The variable 1 is often referred to as correspondence. Most theoretical work in the literature assumes knowledge of the correspondence or, put differently, that landmarks are uniquely identifi able. Practi- cal implementationsuse maximum likelihood estimators for estimating the correspondence on-the-fl y, which work well if landmarks are spaced suffi ciently far apart. In large parts ofthis paperwe will simplyassume that landmarksareiden- tifi able, but we will also discuss an extension that estimates the correspondences from data. We are now ready to formulate the SLAM problem. Most generally, SLAM is the problem of determining the location of all landmarks and robot poses ?from measurements - ? and controls - ? . In probabilis- tic terms, this is expressed by the posterior ? ? D( ? , where we use the superscript to refer to a set of variables from time 1 to time $ . If the correspondencesare known, the SLAM problem is simpler: ? E( 21 ? (3) As discussed in the introduction,all individual landmark es- timation problems are independent if one knew the robots path ? and the correspondence variables 1 . This condi- tional independence is the basis of the FastSLAM algorithm described in the next section. FastSLAM with Known Correspondences We begin our consideration with the important case where the correspondences 1 -=1? 21 are known, and so is the number of landmarks ? observed thus far. Factored Representation The conditional independence property of the SLAM prob- lem implies that the posterior (3) can be factored as follows: ? E( 21 ? - ? ( 21 ?GF ? H( ? 21 ? (4) Putverbally,theproblemcanbedecomposedinto ?JI / esti- mationproblems,oneproblemofestimating a posteriorover robot paths ? , and ? problems of estimating the locations of the ? landmarks conditioned on the path estimate. This factorization is exact and always applicable in the SLAM problem, as previously argued in 12. The FastSLAM algorithm implements the path estimator ? ? ( 21 ? using a modifi ed particle fi lter 4. As we argue further below, this fi lter can sample effi ciently from this space, providing a good approximation of the poste- rior even under non-linear motion kinematics. The land- mark pose estimators ? ( ? 1 ? are realized by Kalman fi lters, using separate fi lters for differentlandmarks. Because the landmark estimates are conditioned on the path estimate, each particle in the particle fi lter has its own, lo- cal landmark estimates. Thus, for particles and ? land- marks, there will be a total of ? Kalman fi lters, each of dimension 2 (for the two landmark coordinates). This repre- sentation will now be discussed in detail. Particle Filter Path Estimation FastSLAM employs a particle fi lter for estimating the path posterior ? ( 1 ? in (4), using a fi lter that is similar (but not identical) to the Monte Carlo localization (MCL) algorithm 1. MCL is an application of particle fi lter to the problem of robot pose estimation (localization). At each pointin time, both algorithmsmaintain a set of particles rep- resenting the posterior ? ? ( 21 ? , denoted ? . Each particle ? ? 8 ? represents a “guess” of the robots path: ? - 4? ? 6 ? - 4 ? ? ? ? ? ? ? ? ? ? ? 6 ? (5) We use the superscript notation ? ? to refer to the -th par- ticle in the set. The particle set ? is calculated incrementally, from the set ? +* ? at time$ D/, a robot control , and a measurement . First, each particle ? ? in ? +* ? is used to generate a probabilistic guess of the robots pose at time $ : ? ? ? ? ( ? ? ? +* ? ? (6) obtained by sampling from the probabilistic motion model. This estimate is then added to a temporary set of parti- cles, along with the path ? +*? . Under the assumption that the set of particles in ? +* ? is distributed according to ? ? +* ? ( +* ? +* ? 1 +* ? ? (which is an asymptotically cor- rect approximation), the new particle is distributed accord- ing to ? ( +* ? 21 +* ? ? . This distribution is commonly referred to as the proposal distribution of particle fi ltering. After generating particles in this way, the new set ? is obtained by sampling from the temporary particle set. Each particle ? ? is drawn(with replacement)witha probability proportionalto a so-called importance factor ? ? , which is calculated as follows 10: ? ? - target distribution proposal distribution - ? ? ( 21 ? ? ? ( +* ? 21 +* ? ? (7) The exact calculation of (7) will be discussed further below. The resultingsampleset ? is distributedaccordingto anap- proximation to the desired pose posterior ? ( 21 ? , an approximationwhichis correctas the numberof particles goes to infi nity. We also notice that only the most recent robot pose estimate ? ? ? +* ? is used when generating the parti- cle set ? . This will allows us to silently “forget” all other pose estimates, rendering the size of each particle indepen- dent of the time index $ . Landmark Location Estimation FastSLAM represents the conditional landmark estimates ? ( ? 1 ? in (4) by Kalman fi lters. Since this estimate is conditioned on the robot pose, the Kalman fi lters are attached to individual pose particles in ? . More specifi - cally, the full posterior over paths and landmark positions in the FastSLAM algorithm is represented by the sample set C-4 ? ? ? ? ? ? ? ? ? ? ? ? 6?(8) Here ? ? and ? ? are mean and covariance of the Gaus- sian representing the , -th landmark , attached to the -th particle. In the planar robot navigation scenario, each mean ? ? is a two-element vector, and ? ? is a 2 by 2 matrix. The posterior over the , -th landmarkpose is easily ob- tained. Its computation depends on whether or not 1 -?, thatis, whetherornot was observedattime $ . For 1 - , , we obtain ? (? 21 ? (9) ?)(? ? +* ? 1 ? ? ( ? +* ? 1 ? !#“%$# every time a particle is added to , its has to be copied. Since each particle contains ? landmark esti- mates, this copyingprocedurerequires ? ? time. How- ever, most of this copying can be avoided. Our approach makes it possible to execute a FastSLAM iteration in?! #“ ? ? time. The basic idea is that the set ofGaussians in eachparticleis representedbya balancedbi- nary tree. Figure 2 shows such a tree for a single particle, in the case of 8 landmarks. The Gaussian parameters ? ? and 8,87,7 k? 3? FT 6,65,5 k? 1? FT 4,43,3 k? 3? FT 2,21,1 k? 1? FT k? 6? FT k? 2? FT k? 4? FT mmmmmmmmmmmmmmmm 8,87,7 k? 3? FT 6,65,5 k? 1? FT 4,43,3 k? 3? FT 2,21,1 k? 1? FT k? 6? FT k? 2? FT k? 4? FT mmmmmmmmmmmmmmmm 3,3 k? 3? FT k? 2? F T k? 4? F T mm new?particle old?particle 8,87,7 k? 3? FT 6,65,5 k? 1? FT 4,43,3 k? 3? FT 2,21,1 k? 1? FT k? 6? FT k? 2? FT k? 4? FT mmmmmmmmmmmmmmmm 8,87,7 k? 3? FT 6,65,5 k? 1? FT 4,43,3 k? 3? FT 2,21,1 k? 1? FT k? 6? FT k? 2? FT k? 4? FT mmmmmmmmmmmmmmmm 8,87,7 k? 3? FT 6,65,5 k? 1? FT 4,43,3 k? 3? FT 2,21,1 k? 1? FT k? 6? FT k? 2? FT k? 4? FT mmmmmmmmmmmmmmmm 8,87,7 k? 3? FT 6,65,5 k? 1? FT 4,43,3 k? 3? FT 2,21,1 k? 1? FT k? 6? FT k? 2? FT k? 4? FT mmmmmmmmmmmmmmmm 8,87,7 k? 3? FT 6,65,5 k? 1? FT 4,43,3 k? 3? FT 2,21,1 k? 1? FT k? 6? FT k? 2? FT k? 4? FT mmmmmmmmmmmmmmmm 3,3 k? 3? FT k? 2? F T k? 4? F T mm new?particle old?particle Figure 3: Generating a new particle from an old one, while modi- fying only a single Gaussian. The new particle receives only a par- tial tree, consisting of a path to the modifi ed Gaussian. All other pointers are copied from the generating tree. ? ? are located at the leaves of the tree. Clearly, accessing each Gaussian requires time logarithmic in ? . Suppose FastSLAM incorporates a new control and a new measurement . Each new particle in will differ from the corresponding one in +* ? in two ways: First, it will possess a different path estimate obtained via (6), and second, the Gaussian with index 1 will be different in ac- cordance with (9). All other Gaussians will be equivalent to the generating particle. When copying the particle, thus, only a single path has to be modifi ed in the tree representing all Gaussians. An example is shown in Figure 3: Here we assume 1 - * , that is, only the Gaussian parameters ? ? and ? ? are updated. Instead of generatingan entirely new tree, only a single path is created, leading to the Gaussian 1 - * . This path is an incomplete tree. To complete the tree, for all branches that leave this path the corresponding pointers are copied from the tree of the generating particle. Thus, branches that leave the path will point to the same (unmodifi ed) subtree as that of the generating tree. Clearly, generating such an incompletetree takesonlytimelogarithmicin ? . Moreover, accessing a Gaussian also takes time logarithmicin ? , since the number of steps required to navigate to a leaf of the tree is equivalent to the length of the path (which is by defi nition logarithmic). Thus, both generating and accessing a partial tree can be done in time?! #“ ? . Since in each updating step new particles are created, an entire update requires time in ?! #“ ? . Data Association In many real-world problems, landmarks are not identifi - able, and the total number of landmarks ? cannot be ob- tained triviallyas was the case above. In such situations, the robot has to solve a data association problem between momentary landmarks sightings and the set of landmarks in the map .