外筒襯套工藝及車床心軸銑床靠模夾具設(shè)計(jì)【說(shuō)明書+CAD】

購(gòu)買設(shè)計(jì)請(qǐng)充值后下載，資源目錄下的文件所見即所得，都可以點(diǎn)開預(yù)覽，資料完整，充值下載可得到資源目錄里的所有文件?！咀ⅰ浚篸wg后綴為CAD圖紙，doc,docx為WORD文檔，原稿無(wú)水印，可編輯。具體請(qǐng)見文件預(yù)覽，有不明白之處，可咨詢QQ:12401814 工 序 目 錄產(chǎn) 品 型 號(hào)QAI 9-4共 頁(yè)零 組 件 號(hào)YB458-71第 頁(yè)工序號(hào)工 序 名 稱設(shè) 備工序卡片數(shù)附 注5備料110車C620115車C620120粗銑6H11125鏜孔C620130車外圓C620135銑外形6H11140車外圓C620145攻螺紋CH12A150銑圓弧6H11155修銼鉗工臺(tái)160研磨孔研磨頭165檢驗(yàn)檢驗(yàn)臺(tái)170磨3153175車C620180去毛刺鉗工臺(tái)185研磨孔研磨頭190檢驗(yàn)檢驗(yàn)臺(tái)195鈍化1100檢驗(yàn)檢驗(yàn)臺(tái)1工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI 9-4 備料5設(shè) 備鋸床定 位 夾 緊 共 頁(yè)第 頁(yè)備料 50*310.5序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI 9-4車10設(shè) 備C620定 位 夾 緊 共1 頁(yè)第 1 頁(yè) 序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1三爪卡盤2車40外圓柱面車刀3鉆24的孔鉆頭440右側(cè)倒45圓角圓角刀524孔右側(cè)倒45圓角工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4車15設(shè) 備C620定 位 夾 緊 共 1 頁(yè)第 1 頁(yè) 序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1三爪卡盤卡規(guī)2車37右端面車刀3車37外圓柱面4車47外圓柱面5車47右端面工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4粗銑20設(shè) 備6H11定 位 夾 緊 共 1 頁(yè)第 1 頁(yè) 序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1銑床夾具2轉(zhuǎn)盤3粗銑37外圓柱面靠模銑刀4粗銑距中心14mm的平面5粗銑過(guò)渡圓弧R7工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI-94鏜孔25設(shè) 備C620定 位 夾 緊 共 1 頁(yè)第 1 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1軟三爪2專用夾具3鏜孔加工鏜刀4切槽加工切槽刀工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4車外圓30設(shè) 備C620定 位 夾 緊 共 1 頁(yè)第 1 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1車床心軸2尖邊倒圓R0.2-0.3圓角刀3車36.3外圓車刀4車距左端面23mm的平面工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4銑外形35設(shè) 備6H11定 位 夾 緊 共 1 頁(yè)第 1 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1心軸卡規(guī)2銑36.5的圓柱面靠模銑刀3銑距中心13mm的上下兩個(gè)平面4銑過(guò)渡圓弧R3工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI-94車外圓40設(shè) 備C620定 位 夾 緊 共 1 頁(yè)第 1 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1心軸卡規(guī)2專用夾具3車37.7的外圓柱面車刀4車2x0.6的退刀槽5車0.5x45的倒角6右端面倒角工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4攻螺紋45設(shè) 備CH12A定 位 夾 緊 共 1 頁(yè)第 1 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1心軸2去毛刺專用夾具絲錐M4-2塞規(guī)M4x0.7-23螺紋倒角90至底鉆頭塞規(guī)工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4銑圓弧50設(shè) 備6H11定 位 夾 緊 共 1 頁(yè)第 1 頁(yè) 序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1心軸2銑R6.5的圓弧銑刀工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4修銼55設(shè) 備鉗工臺(tái)定 位 夾 緊 共 1 頁(yè)第 1 頁(yè) 序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1修R5銼刀2去外表面毛刺工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4研磨孔60設(shè) 備研磨頭定 位 夾 緊 共 1 頁(yè)第 1 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1三爪卡盤2研磨25.99的孔研磨鉆工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4檢驗(yàn)65設(shè) 備檢驗(yàn)臺(tái)定 位 夾 緊 共 頁(yè)第 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1檢驗(yàn)2-M4x2螺紋 塞規(guī)2檢查37.5外圓游標(biāo)卡尺3檢驗(yàn)25.99內(nèi)孔游標(biāo)卡尺4檢驗(yàn)槽寬7工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4磨70設(shè) 備3153定 位 夾 緊 共 1 頁(yè)第 1 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1專用夾具2磨36.3外圓柱面砂輪3磨螺紋孔右端平面工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4車75設(shè) 備C620定 位 夾 緊 共 1 頁(yè)第 1 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1車倒角2.5x30專用夾具車刀2拋光R0.5砂紙3去磨后毛刺工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4去毛刺80設(shè) 備鉗工臺(tái)定 位 夾 緊 共 1 頁(yè)第 1 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1去外表面上毛刺工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4研磨孔85設(shè) 備研磨頭定 位 夾 緊 共 1 頁(yè)第 1 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1三爪卡盤2研磨26的中心孔研磨鉆工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4檢驗(yàn)90設(shè) 備檢驗(yàn)臺(tái)定 位 夾 緊 共 1 頁(yè)第 1 頁(yè)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1檢查37.7游標(biāo)卡尺2檢查26游標(biāo)卡尺3檢驗(yàn)7.5游標(biāo)卡尺工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4鈍化95設(shè) 備定 位 夾 緊 共 1 頁(yè)第 1 頁(yè) 序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具1將零件鈍化.工 序 卡 片零 件 名 稱材 料硬 度工序名稱工 序 號(hào)外筒襯套QAI9-4檢驗(yàn)100設(shè) 備檢驗(yàn)臺(tái)定 位 夾 緊 共 1 頁(yè)第 1 頁(yè) 與相同編號(hào)的外筒ZL10-30-01成套交庫(kù)序 號(hào)加 工 要 求 說(shuō) 明夾 具刀 具量 具工 序 目 錄產(chǎn) 品 型 號(hào)共 1 頁(yè)零 組 件 號(hào)第 1 頁(yè)序號(hào)工藝裝備名稱夾具圖號(hào)刀具圖號(hào)量具圖號(hào)模具圖號(hào)工序號(hào)備注1鉆頭22102塞規(guī)24D6103卡規(guī)47.5d7154量環(huán)32.5-25 655卡規(guī)32.5d5306靠模銑刀B-14x16207靠模銑刀B-6x15xR0.3358卡規(guī)36.5d7-B409鉆頭L-3.34510絲錐M4-24511塞規(guī)M4x0.74512塞規(guī)M4x0.7-245 6513錐心棒D=2685 9014量環(huán)26-85 9015量環(huán)25.96-2516卡規(guī)40d61017量環(huán)25.99-60 6518錐心棒25.97-70畢業(yè)（設(shè)計(jì)）論文開題報(bào)告系 別 機(jī)電工程系 專 業(yè) 機(jī)械設(shè)計(jì)制造及其自動(dòng)化班 級(jí) 161002 學(xué)生姓名 張宇航 學(xué) 號(hào) 103329 指導(dǎo)教師 鄧修瑾 報(bào)告日期 2014/3/8 畢業(yè)（設(shè)計(jì)）論文開題報(bào)告表論文題目外筒襯套工藝及車床心軸銑床靠模夾具設(shè)計(jì)學(xué)生姓名張宇航學(xué) 號(hào)103329指導(dǎo)教師鄧修瑾題目來(lái)源（劃）科研生產(chǎn)實(shí)驗(yàn)室專題研究論文類型（劃）設(shè)計(jì)論文其 他 1、 選題的意義 外筒襯套零件在產(chǎn)品中一端與液壓助力器外筒組件連接，中間孔與活塞外圓配合，起到一端的支撐作用。因此在機(jī)器中有很重要的作用，在生產(chǎn)中會(huì)大量生產(chǎn) 所以完成這次選題有以下幾點(diǎn)意義: 1.能使我了解外筒襯套的特點(diǎn)和工作原理. 2.能幫我掌握外筒襯套的工藝過(guò)程令設(shè)計(jì)出來(lái)的零件符合精度及表面粗糙度等各方面工藝要求。 3.通過(guò)對(duì)所用夾具進(jìn)行合理設(shè)計(jì)來(lái)滿足工藝要求，從而將自己的理論知識(shí)與實(shí)際相結(jié)合。 4.通過(guò)此次選題可以提高學(xué)生的自主設(shè)計(jì)能力進(jìn)一步滿足企業(yè)對(duì)畢業(yè)生的能力要求。二、基本內(nèi)容及重點(diǎn) 1.零件的的分析(1)零件的作用(2)零件的工藝分析 2.工藝規(guī)程設(shè)計(jì)(1)確定毛坯制造形式(2)定位基準(zhǔn)的選擇(3)擬定工藝路線(4)選擇加工設(shè)備及刀具夾具量具(5)機(jī)械加工余量工序尺寸及毛坯尺寸的確定(6)確定切削用量及基本時(shí)間 3.專用夾具設(shè)計(jì)(1)機(jī)床夾具概述(2)定位基準(zhǔn)選擇(3)切削力和卡緊力計(jì)算(4)問(wèn)題的提出(5)夾具設(shè)計(jì)(6)夾具設(shè)計(jì)中的特點(diǎn)(7)夾具分類(8)夾具設(shè)計(jì)技的發(fā)展(9)夾具的基礎(chǔ)件(10)設(shè)計(jì)方法和步驟 4.說(shuō)明書 三、預(yù)期達(dá)到的成果 設(shè)計(jì)的外筒襯套工藝過(guò)程合理，設(shè)計(jì)出來(lái)的零件工藝規(guī)程滿足圖紙實(shí)際尺寸，表面粗糙度，加工精度等各方面要求。夾具設(shè)計(jì)合理符合工藝要求。四、存在的問(wèn)題及擬采取的解決措施 存在問(wèn)題：1.對(duì)利用CAD軟件制圖不熟練。 2.對(duì)零件圖的理解不夠充分。 3.對(duì)零件加工工藝過(guò)程方面知識(shí)理解不夠深刻 解決方法：1. 認(rèn)真復(fù)習(xí)對(duì)CAD軟件的運(yùn)用。2. 關(guān)于對(duì)零件圖不理解的地方請(qǐng)教老師和同學(xué)。3. 認(rèn)真學(xué)習(xí)零件加工工藝過(guò)程反面知識(shí)彌補(bǔ)自己的不足。 五、進(jìn)度安排1、 分析并繪制零件圖 1周2、 繪制毛坯圖 1周3、 設(shè)計(jì)工藝路線及編制工藝規(guī)程 4周4、 設(shè)計(jì)工藝裝備 3周5、 編寫說(shuō)明書（論文） 2周六、參考文獻(xiàn)和書目1 王先逵編著.機(jī)械制造工藝學(xué)M.北京：清華大學(xué)出版社，19892 鄧文英、宋力宏.金屬工藝學(xué) 第五版 北京：高等教育出版社.2008.43 趙志修主編.機(jī)械制造工藝學(xué)M.北京機(jī)械工業(yè)出版社，19854 肖繼德.機(jī)床夾具設(shè)計(jì)M.北京：北京機(jī)械工業(yè)出版社，20055 關(guān)慧貞、馮辛安.機(jī)械制造裝備設(shè)計(jì)（第三版）.北京：機(jī)械工業(yè)出版社，20096 孫麗媛.機(jī)械制造工藝及專用夾具設(shè)計(jì)指導(dǎo)M.北京：冶金工業(yè)出版社，20077 楊叔子.機(jī)械加工工藝師手冊(cè)M.北京：機(jī)械工業(yè)出版社，20018 朱耀祥，蒲林祥.現(xiàn)代夾具手冊(cè)M.北京：機(jī)械工業(yè)出版社，20109 吳宗澤，高志.機(jī)械設(shè)計(jì)（第二版）.北京：高等教育出版社.2009導(dǎo)師意見指導(dǎo)教師簽字： 年 月 日 系意見 系主任簽字： 年 月 日注：內(nèi)容用小四，宋體Robot companion localization at home and in the office Arnoud Visser Jurgen Sturm Frans Groen Intelligent Autonomous Systems, Universiteit van Amsterdam http:/www.science.uva.nl/research/ias/Abstract The abilities of mobile robots depend greatly on the performance of basic skills such asvision and localization. Although great progress has been made to explore and map extensivepublic areas with large holonomic robots on wheels, less attention is paid on the localizationof a small robot companion in a confined environment as a room in office or at home. Inthis article, a localization algorithm for the popular Sony entertainment robot Aibo inside aroom is worked out. This algorithm can provide localization information based on the naturalappearance of the walls of the room. The algorithm starts making a scan of the surroundings byturning the head and the body of the robot on a certain spot. The robot learns the appearanceof the surroundings at that spot by storing color transitions at different angles in a panoramicindex. The stored panoramic appearance is used to determine the orientation (including aconfidence value) relative to the learned spot for other points in the room. When multiplespots are learned, an absolute position estimate can be made. The applicability of this kind oflocalization is demonstrated in two environments: at home and in an office.1 Introduction1.1 Context Humans orientate easily in their natural environments. To be able to interact with humans, mobilerobots also need to know where they are. Robot localization is therefore an important basic skillof a mobile robot, as a robot companion like the Aibo. Yet, the Sony entertainment softwarecontained no localization software until the latest release1. Still, many other applications for arobot companion - like collecting a news paper from the front door - strongly depend on fast,accurate and robust position estimates. As long as the localization of a walking robot, like theAibo, is based on odometry after sparse observations, no robust and accurate position estimatescan be expected. Most of the localization research with the Aibo has concentrated on the RoboCup. At theRoboCup2 artificial landmarks as colored flags, goals and field lines can be used to achieve localizationaccuracies below six centimeters 6, 8.The price that these RoboCup approaches pay is their total dependency on artificial landmarksof known shape, positions and color. Most algorithms even require manual calibration of the actualcolors and lighting conditions used on a field and still are quite susceptible for disturbances aroundthe field, as for instance produced by brightly colored clothes in the audience. The interest of the RoboCup community in more general solutions has been (and still is) growingover the past few years. The almost-SLAM challenge3 of the 4-Legged league is a good example ofthe state-of-the-art in this community. For this challenge additional landmarks with bright colorsare placed around the borders on a RoboCup field. The robots get one minute to walk around andexplore the field. Then, the normal beacons and goals are covered up or removed, and the robotmust then move to a series of five points on the field, using the information learnt during the first1Aibo Mind 3 remembers the direction of its station and toys relative to its current orientation2RoboCup Four Legged League homepage, last accessed in May 2006, http:/www.tzi.de/4legged3Details about the Simultaneous Localization and Mapping challenge can be found at http:/www.tzi.de/4legged/pub/Website/Downloads/Challenges2005.pdf1 minute. The winner of this challenge 6 reached the five points by using mainly the information ofthe field lines. The additional landmarks were only used to break the symmetry on the soccer field.A more ambitious challenge is formulated in the newly founded RoboCup Home league4. Inthis challenge the robot has to safely navigate toward objects in the living room environment. Therobot gets 5 minutes to learn the environment. After the learning phase, the robot has to visit 4distinct places/objects in the scenario, at least 4 meters away from each other, within 5 minutes.1.2 Related Work Many researchers have worked on the SLAM problem in general, for instance on panoramic images1, 2, 4, 5. These approaches are inspiring, but only partially transferable to the 4-Legged league.The Aibo is not equipped with an omni-directional high-quality camera. The camera in the nosehas only a horizontal opening angle of 56.9 degrees and a resolution of 416 x 320 pixels. Further,the horizon in the images is not a constant, but depends on the movements of the head and legs ofthe walking robot. So each image is taken from a slightly different perspective, and the path of thecamera center is only in first approximation a circle. Further, the images are taken while the headis moving. When moving at full speed, this can give a difference of 5.4 degrees between the top andthe bottom of the image. So the image seems to be tilted as a function of the turning speed of thehead. Still, the location of the horizon can be calculated by solving the kinematic equations of therobot. To process the images, a 576 Mhz processor is available in the Aibo, which means that onlysimple image processing algorithms are applicable. In practice, the image is analyzed by followingscan-lines with a direction relative the calculated horizon. In our approach, multiple sectors abovethe horizon are analyzed, with in each sector multiple scan-lines in the vertical direction. One ofthe general approaches 3 divides the image in multiple sectors, but this image is omni-directionaland the sector is analyzed on the average color of the sector. Our method analysis each sector ona different characteristic feature: the frequency of colortransitions.2 Approach The main idea is quite intuitive: we would like the robot to generate and store a 360o circularpanorama image of its environment while it is in the learning phase. After that, it should aligneach new image with the stored panorama, and from that the robot should be able to derive itsrelative orientation (in the localization phase). This alignment is not trivial because the new imagecan be translated, rotated, stretched and perspectively distorted when the robot does not stand atthe point where the panorama was originally learned 11. Of course, the Aibo is not able (at least not in real-time) to compute this alignment on fullresolutionimages. Therefore a reduced feature space is designed so that the computations becometractable5 on an Aibo. So, a reduced circular 360o panorama model of the environment is learned.Figure 1 gives a quick overview of the algorithms main components. The Aibo performs a calibration phase before the actual learning can start. In this phase theAibo first decides on a suitable camera setting (i.e. camera gain and the shutter setting) basedon the dynamic range of brightness in the autoshutter step. Then it collects color pixels byturning its head for a while and finally clusters these into 10 most important color classes in thecolor clustering step using a standard implementation of the Expectation-Maximization algorithmassuming a Gaussian mixture model 9. The result of the calibration phase is an automaticallygenerated lookup-table that maps every YCbCr color onto one of the 10 color classes and cantherefore be used to segment incoming images into its characteristic color patches (see figure 2(a).These initialization steps are worked out in more detail in 10.4RoboCup Home League homepage, last accessed in May 2006, http:/www.ai.rug.nl/robocupathome/5Our algorithm consumes per image frame approximately 16 milliseconds, therefore we can easily process imagesat the full Aibo frame rate (30fps).Figure 1: Architecture of our algorithm(a) Unsupervised learned color segmentation.(b) Sectors and frequent color transitionsvisualized. Figure 2: Image processing: from the raw image to sector representation. This conversion consumesapproximately 6 milliseconds/frame on a Sony Aibo ERS7.2.1 Sector signature correlation Every incoming image is now divided into its corresponding sectors6. The sectors are located abovethe calculated horizon, which is generated by solving the kinematics of the robot. Using the lookuptable from the unsupervised learned color clustering, we can compute the sector features by countingper sector the transition frequencies between each two color classes in vertical direction. This yieldsthe histograms of 10x10 transition frequencies per sector, which we subsequently discretize into 5logarithmically scaled bins. In figure 2(b) we displayed the most frequent color transitions for eachsector. Some sectors have multiple color transitions in the most frequent bin, other sectors have asingle or no dominant color transition. This is only visualization; not only the most frequent colortransitions, but the frequency of all 100 color transitions are used as characteristic feature of thesector. In the learning phase we estimate all these 80x(10x10) distributions7 by turning the head andbody of the robot. We define a single distribution for a currently perceived sector byPcurrent (i, j, bin) =_1 discretize (freq (i, j) = bin0 otherwise(1) where i, j are indices of the color classes and bin one of the five frequency bins. Each sector isseen multiple times and the many frequency count samples are combined into a distribution learned680 sectors corresponding to 360o; with an opening angle of the Aibo camera of approx. 50o, this yields between10 and 12 sectors per image (depending on the head pan/tilt)7When we use 16bit integers, a complete panorama model can be described by (80 sectors)x(10 colors x 10colors)x(5 bins)x(2 byte) = 80 KB of memory.for that sector by the equation:Plearned (i, j, bin) = Pcountsector (i, j, bin)bin2frequencyBinscountsector (i, j, bin)(2) After the learning phase we can simply multiply the current and the learned distribution to getthe correlation between a currently perceived and a learned sector:Corr(Pcurrent, Plearned) =Yi,j2colorClasses,bin2frequencyBinsPlearned (i, j, bin) Pcurrent (i, j, bin) (3)2.2 Alignment After all the correlations between the stored panorama and the new image signatures were evaluated,we would like to get an alignment between the stored and seen sectors so that the overall likelihoodof the alignment becomes maximal. In other words, we want to find a diagonal path with theminimal cost through the correlation matrix. This minimal path is indicated as green dots in figure3. The path is extended to a green line for the sectors that are not visible in the latest perceivedimage. We consider the fitted path to be the true alignment and extract the rotational estimate robotfrom the offset from its center pixel to the diagonal (_sectors):robot =360_80_sectors (4) This rotational estimate is the difference between the solid green line and the dashed white linein figure 3, indicated by the orange halter. Further, we try to estimate the noise by fitting again apath through the correlation matrix far away from the best-fitted path.SNR =P(x,y)2minimumPathCorr(x, y)P(x,y)2noisePathCorr(x, y)(5)The noise path is indicated in figure 3 with red dots.(a) Robot standing on the trained spot (matchingline is just the diagonal)(b) Robot turned right by 45 degrees (matchingline displaced to the left) F igure 3: Visualization of the alignment step while the robot is scanning with its head. Thegreen solid line marks the minimum path (assumed true alignment) while the red line marks thesecond-minimal path (assumed peak noise). The white dashed line represents the diagonal, whilethe orange halter illustrates the distance between the found alignment and the center diagonal(_sectors).2.3 Position Estimation with Panoramic Localization The algorithm described in the previous section can be used to get a robust bearing estimatetogether with a confidence value for a single trained spot. As we finally want to use this algorithmto obtain full localization we extended the approach to support multiple training spots. Themain idea is that the robot determines to which amount its current position resembles with thepreviously learned spots and then uses interpolation to estimate its exact position. As we thinkthat this approach could also be useful for the RoboCup Home league (where robot localizationin complex environments like kitchens and living rooms is required) it could become possible thatwe finally want to store a comprehensive panorama model library containing dozens of previouslytrained spots (for an overview see 1). However, due to the computation time of the feature space conversion and panorama matching,per frame only a single training spot and its corresponding panorama model can be selected.Therefore, the robot cycles through the learned training spots one-by-one. Every panorama modelis associated with a gradually changed confidence value representing a sliding average on the confidence values we get from the per-image matching. After training, the robot memorizes a given spot by storing the confidence values received fromthe training spots. By comparing a new confidence value with its stored reference, it is easy todeduce whether the robot stands closer or farther from the imprinted target spot.We assume that the imprinted target spot is located somewhere between the training spots.Then, to compute the final position estimate, we simply weight each training spot with its normalizedcorresponding confidence value:positionrobot =XipositioniPconfidenceij confidencej(6) This should yield zero when the robot is assumed to stand at the target spot or a translationestimate towards the robots position when the confidence values are not in balance anymore.To prove the validity of this idea, we trained the robot on four spots on regular 4-Legged fieldin our robolab. The spots were located along the axes approximately 1m away from the center.As target spot, we simply chose the center of the field. The training itself was performed fullyautonomously by the Aibo and took less than 10 minutes. After training was complete, the Aibowalked back to the center of the field. We recorded the found position and kidnapped the robot toan arbitrary position around the field and let it walk back again. Please be aware that our approach for multi-spot localization is at this moment rather primitiveand has to be only understood as a proof-of-concept. In the end, the panoramic localization datafrom vision should of course be processed by a more sophisticated localization algorithm, like aKalman or particle filter (last not least to incorporate movement data from the robot).3 Results3.1 Environments We selected four different environments to test our algorithm under a variety of circumstances. Thefirst two experiments were conducted at home and in an office environment8 to measure performanceunder real-world circumstances. The experiments were performed on a cloudy morning, sunnyafternoon and late in the evening. Furthermore, we conducted exhaustive tests in our laboratory.Even more challenging, we took an Aibo outdoors (see 7).3.2 Measured results Figure 4(a) illustrates the results of a rotational test in a normal living room. As the error in therotation estimates ranges between -4.5 and +4.5 degrees, we may assume an error in alignment ofa single sector9; moreover, the size of the confidence interval can be translated into maximal twosectors, which corresponds to the maximal angular resolution of our approach.8XX office, DECIS lab, Delft9full circle of 3600 divided by 80 sectors(a) Rotational test in natural environment (livingroom, sunny afternoon)(b) Translational test in natural environment (childsroom, late in the evening) Figure 4: Typical orientation estimation results of experiments conducted at home. In the rotationalexperiment on the left the robot is rotated over 90 degrees on the same spot, and every 5 degrees itsorientation is estimated. The robot is able to find its true orientation with an error estimate equalto one sector of 4.5 degrees. The translational test on the right is performed in a childs room. Therobot is translated over a straight line of 1.5 meter, which covers the major part of the free spacein this room. The robot is able to maintain a good estimate of its orientation; although the errorestimate increases away from the location where the appearance of the surroundings was learned.Figure 4(b) shows the effects of a translational dislocation in a childs room. The robot wasmoved onto a straight line back and forth through the room (via the trained spot somewhere in themiddle). The robot is able to estimate its orientation quite well on this line. The discrepancy withthe true orientation is between +12.1 and -8.6 degrees, close to the walls. This is also reflected inthe computed confidence interval, which grows steadily when the robot is moved away from thetrained spot. The results are quite impressive for the relatively big movements in a small room andthe resulting significant perspective changes in that room. Figure 5(a) also stems from a translational test (cloudy morning) which has been conducted inan office environment. The free space in this office is much larger than at home. The robot wasmoved along a 14m long straight line to the left and right and its orientation was estimated. Notethe error estimate stays low at the right side of this plot. This is an artifact which nicely reflectsthe repetition of similarly looking working islands in the office. In both translational tests it can be seen intuitively that the rotation estimates are withinacceptable range. This can also be shown quantitatively (see figure 5(b): both the orientationerror and the confidence interval increase slowly and in a graceful way when the robot is movedaway from the training spot. Finally, figure 6 shows the result of the experiment to estimate the absolute position with multiplelearned spots. It can be seen that the localization is not as accurate as traditional approaches,but can still be useful for some applications (bearing in mind that no artificial landmarks are required).We recorded repeatedly a derivation to the upper right that we think can be explained bythe fact that different learning spots dont produce equally strong confidence values; we believe tobe able to correct for that by means of confidence value normalization in the near future.4 Conclusion Although at first sight the algorithm seems to rely on specific texture features of the surroundingsurfaces, in practice no dependency could be found. This can be explained by two reasons: firstly, asthe (vertical) position of a color transition is not used anyway, the algorithm is quite robust against(vertical) scaling. Secondly, as the algorithm aligns on many color transitions in the background(typically more than a hundred in the same sector), the few color transitions produced by objectsin the foreground (like beacons and spectators) have a minor impact on the match (because theirsizes relative to the background are comparatively small). The lack of an accurate absolute position estimates seems to be a clear drawback with respect tothe other methods, but bearing information alone can already be very useful for certain applications.(a) Translational test i