循跡搬運機器人--畢業(yè)設(shè)計（優(yōu)秀含CAD圖紙+設(shè)計說明書）

循跡搬運機器人--畢業(yè)設(shè)計（優(yōu)秀含CAD圖紙+設(shè)計說明書）,搬運,機器人,畢業(yè)設(shè)計,優(yōu)秀,優(yōu)良,cad,圖紙,設(shè)計,說明書,仿單 Xxxxx 畢業(yè)設(shè)計(論文)中期檢查表--學(xué)生 機械工程系 系（部） 檢查時間 2014 年 04 月 20 日 論文題目 循跡搬運機器人設(shè)計 指導(dǎo)教師 xxxxx 學(xué)生 姓名 xxxxx 專業(yè) 班級 xxxxx 學(xué)號 xxxxx 目前 已完 成的 任務(wù) 1. 畢業(yè)論文任務(wù)書 2. 畢業(yè)論文文獻綜述 3. 畢業(yè)論文開題報告 4. 設(shè)計圖紙初稿 是否符合任務(wù)書要求的進度 √ 是 否 尚需 完成 的 任務(wù) 1. 論文的整體框架已經(jīng)基本形成，，仍需對論文做一定的調(diào)整和修改，使論文的中心內(nèi)容更加條理和完整，使整體脈絡(luò)更加清晰完整 2. 撰寫論文二稿、三稿，相關(guān)圖紙初稿 3. 撰寫論文定稿，圖紙定型 4. 參加論文答辯 能否按期完成任務(wù) √ 能 不能 存在 的問 題及 擬采 取的 辦法 存在的問題：研究過程的嚴(yán)謹(jǐn)性還需進一步提高。 采取的辦法：在導(dǎo)師的指導(dǎo)下，進一步細化研究過程，調(diào)整語言，避免基本的語法和格式錯誤。 對指 導(dǎo)教 師的 建議 非常滿意 學(xué)生（簽名）：xxxxx xxxxx年 04 月 22 日 注：1.中期檢查要講求實效，主要是找問題，找差距。對中期檢查不合格的學(xué)生提出警告。 2.此表一式二份，一份學(xué)生留存，一份交系（部）存檔。 3.在相應(yīng)填寫或打√ Xxxxx 畢業(yè)論文（設(shè)計）任務(wù)書 題 目 循跡搬運機器人設(shè)計 姓 名 xxxxx 系別 機械工程系 專業(yè) 班級 xxxxx 學(xué)號 xxxxx 設(shè) 計 任 務(wù) 1、檢索并翻譯外文資料 2、實習(xí)及調(diào)查研究 3、總體方案設(shè)計 4、結(jié)構(gòu)及零件設(shè)計 設(shè)計參數(shù)：搬運重量800~1000kg 5、撰寫設(shè)計說明書 時 間 進 度 3.09～3.22 實習(xí)并走訪用戶，完成實習(xí)報告 3.23～4.05 閱讀外文資料，完成外文翻譯 4.06～4.12 整體方案論證及確定，完成開題報告 4.13～5.24 相關(guān)設(shè)計計算、總裝圖及主要零件圖設(shè)計、編寫說明書 （4月21日前完成中期檢查表） 5.25～5.18 指導(dǎo)教師審閱、修改、評閱 5.19～5.31 修改后上交所有畢設(shè)資料 6.01～6.03 答辯 6.04～6.07 修改畢設(shè)，上交全部資料并歸檔（紙質(zhì)及電子） 主要參考 文獻資料 【1】杜志俊.工業(yè)機器人的應(yīng)用及發(fā)展趨勢[J].機械工程師,?2002(5)? 【2】陳忠華.可編程控制器與工業(yè)自動化系統(tǒng)[M].?機械工業(yè)出版社,?2006年?? 【3】孫樹棟.工業(yè)機器人技術(shù)基礎(chǔ)[M].?西安：西北工業(yè)大學(xué)出版社，2006年? 【4】吳振彪.工業(yè)機器人[M].?武漢：華中科技大學(xué)出版社，2004年 【5】謝存禧?張鐵主編.機器人技術(shù)及其應(yīng)用[M].?北京：機械工業(yè)出版社，2008年 【6】成大先.機械設(shè)計手冊.北京.化學(xué)工業(yè)出版社，2004. 【7】 Moore K L Flann N S .A six- wheeled omnidirectional autonomous mobile Robot[J] IEEE Control System Magazine. 2000 20(6): 53--66 【8】 Seo, Yoonho; Egbelu, Pius J. Integrated manufacturing planning for an AGV-based FMS.International 3ournal of Production Economics Volume: 60-61, April 20,1999, pp.473—478. 指導(dǎo)教師簽字： 年 月 日 畢業(yè)論文（設(shè)計） 外文翻譯 題 目： 循跡搬運機器人設(shè)計 系部名稱： 機械工程系 專業(yè)班級： xxxxx 學(xué)生姓名： xxxxx 學(xué) 號： xxxxx 指導(dǎo)教師： xxxxx 教師職稱： 教 授 2015 年 3 月 13 日 中原工學(xué)院信息商務(wù)學(xué)院外文翻譯 摘要 本章介紹了視覺系統(tǒng)對移動機器人跟蹤和控制的一個完整的主題。它包括用于估計改善不良的工作條件，例如噪聲，攝像機鏡頭的失真和非均勻照明的位置及方向的估計方法和移動機器人全局視覺系統(tǒng)。 視覺系統(tǒng)的基本動作被分為兩個步驟。在第一個中，輸入圖像被掃描的像素分為有限數(shù)量。與此同時，分割算法是用來尋找的相應(yīng)的區(qū)域?qū)儆谝活悺Ｔ诘诙襟E中，所有的區(qū)域進行檢查。精選的那些是所觀察到的物體的一部分是通過簡單的邏輯程序裝置制成。所使用的方法的新穎性，重點是完成可能的對象的位置要估計所需要的處理時間的優(yōu)化。 進一步關(guān)于一種方法，以提高在惡劣的工作條件已經(jīng)存在的視覺系統(tǒng)性能提出。一些基礎(chǔ)知識和解決方案，在視覺系統(tǒng)設(shè)計的移動機器人跟蹤伴隨的問題給出。除了用于過濾和改進識別噪聲數(shù)據(jù)而劣化的性能的主要因素被處理，即非均勻照明和攝像機鏡頭畸變的方法。對于前者的問題區(qū)域和它的起源都集中在和通過施加由照明平原定義乘法組件及其補償?shù)娜芤航o出。后者包括兩個步驟。首先，徑向鏡頭畸變的基本面進行了討論。其核查建議的解決方案是通過鏡頭投影的幾何模型來實現(xiàn)的。第二步驟涵蓋透視失真從照相機的傾斜始發(fā)。為它的校正被施加消失點檢測的有效且可靠的方法。如果實施以適當(dāng)?shù)姆绞?，這兩種校正方法有助于視覺系統(tǒng)的性能。 所提出的方法應(yīng)用在機器人足球比賽試驗證實。機器人足球比賽是一個快速的動態(tài)博弈，因此需要一個有效的和強大的視覺系統(tǒng)。為了提高足球機器人視覺系統(tǒng)提出的攝像機標(biāo)定和照度不均勻校正算法的實現(xiàn)結(jié)果。鏡頭校正方法成功地校正由鏡頭引起的失真，從而實現(xiàn)更精確的目標(biāo)位置估計。光照補償提高魯棒性不規(guī)則和不均勻的照明，幾乎總是存在于現(xiàn)實條件下。  1引言 使用彩色攝像機的運動目標(biāo)檢測的方法很多。然而，根據(jù)顏色信息的視覺系統(tǒng)被證明是更簡單，健壯和比大多數(shù)如[3,5,13,16]表示其它識別方法更快。 Sargent等人 [13]開發(fā)了一種快速實時視覺系統(tǒng)與一個特殊的硬件加速的系統(tǒng)，這才有意義，該系統(tǒng)對軟件優(yōu)化或加速度有很大的幫助。移動對象的一個更可靠的視覺跟蹤可以通過使用穩(wěn)健統(tǒng)計和概率分布來實現(xiàn)。后者的一個很好的例子給出了Bradski[2]實現(xiàn)了基于顏色的人臉跟蹤。 Bruce等[3]通過高效顏色分割裝置和一個兩通連通區(qū)域判定算法建議用于移動機器人快速視覺系統(tǒng)。在機器人足球視覺設(shè)計的另一個重要貢獻是由惠氏等人介紹。 [16]，以提供給不同的操場光照條件的魯棒性特別考慮。大多數(shù)方法嘗試，以圖像的像素分類成的預(yù)定義號碼之一。最常見的有：線性顏色的閾值，K近鄰分類，神經(jīng)網(wǎng)絡(luò)為基礎(chǔ)的分類器，分類樹和概率方法[10,1,8]。 本章介紹了當(dāng)前對象的位置和方向在操場上估計的全局視覺系統(tǒng)設(shè)計。我們感興趣的是在MiroSot類足球機器人上沒有位置傳感器。因此，一個準(zhǔn)確的和快速的全球視野，必須設(shè)計用于機器人控制和導(dǎo)航中的部分控制的，動態(tài)變化的環(huán)境中。當(dāng)設(shè)計的視覺系統(tǒng)，以下要求必須完成： ?計算效率， ?高可靠性， ?良好的精度， ?魯棒性噪聲，非均勻照明和不同的配色方案。 最后一個特性是必不可少的系統(tǒng)功能以及當(dāng)參與比賽[16]在不同條件下使用它。 在本文中，隨著不斷的閾值和回步算法，快速的方式呈現(xiàn)，其中特別關(guān)注了效率方面。該閾值可被表示為在三維彩色空間框。這些閾值是由離線學(xué)習(xí)來確定。如果一個輸入像素的顏色落在一個預(yù)定義的盒子，那是屬于這個箱子相關(guān)的類。在第一步驟之后是其中屬于一個類（一個連接區(qū)域）的像素區(qū)別標(biāo)記的第二步驟。以獲得所有完全連接區(qū)域的主要目的，是應(yīng)用一步算法。這兩個步驟都只有一個掃描的圖像。然后邏輯部分和一個簡單的優(yōu)化方法被用來從先前生成的那些選擇適當(dāng)?shù)膮^(qū)域?qū)儆谝活悺＿@樣的邏輯是操場上的物體的位置和方向估計。以改善視覺系統(tǒng)相機校準(zhǔn)和非均勻照明校正算法被實現(xiàn)結(jié)果。由相機透鏡導(dǎo)致的，從而實現(xiàn)更準(zhǔn)確和精確的目標(biāo)位置估計，而后者提高了魯棒性不規(guī)則照明和非均勻照明條件。 改善不良的工作條件下與已經(jīng)存在的視覺系統(tǒng)性能的嘗試接著呈現(xiàn)。兩個主要因素對性能產(chǎn)生不利影響，處理：非均勻照明和攝像機鏡頭失真。對于前者，重點放在問題區(qū)域和它的起源[6]，具有由給定的照明平面中定義的乘法部件的應(yīng)用裝置，用于它的補償?shù)娜芤?。后者包括兩個步驟。首先，在徑向鏡頭畸變基本面上進行了討論[9,14]。對于其驗證建議的解決方案是由透鏡投射[11]一個幾何模型的手段來實現(xiàn)。第二步驟涵蓋透視失真從照相機的傾斜始發(fā)。對于其改正，消失點檢測[4,12]的高效和可靠的方法被應(yīng)用。所提出的方法的適用性在機器人足球測試床確認。為了提高機器人足球視覺系統(tǒng)[5]，這兩個建議的攝像機標(biāo)定和非均勻照明校正算法被實現(xiàn)。 本章安排如下。在第2節(jié)系統(tǒng)的簡要概覽，用于像素分類的方法在部分解釋。第3、第4節(jié)的重點是算法的圖像分割和區(qū)域標(biāo)記。該算法為對象估計說明在第5節(jié)，第6節(jié)恢復(fù)數(shù)據(jù)過濾，攝像機標(biāo)定和非均勻的光量校正的實現(xiàn)。得到的實驗結(jié)果顯示在第7節(jié)。結(jié)論和一些想法，本章最后的結(jié)論和對未來工作的一些想法。 2系統(tǒng)概述 所提出的視覺系統(tǒng)在機器人足球比賽設(shè)置展示。足球機器人的設(shè)置，如圖1所示，由十類（MiroSot機器人形成兩隊）大小為7.5立方厘米，一個長方形的操場面積2.2×1.8米，數(shù)字彩色攝像機索尼dfw-v500，和個人計算機的奔騰4。程序的視覺部分處理輸入的圖像，分辨率為640×480像素，確定位置和方向的機器人和球的位置。每個機器人有兩個方形色塊（圖2）。一個是球隊的顏色和其他識別色標(biāo)。據(jù)FIRA（國際機器人足球聯(lián)合會）的規(guī)則，球隊的顏色是藍色或黃色，球必須是橙色和識別色可以是任何顏色除了團隊和球的顏色。視覺算法在操場上以它們的顏色和形狀的考慮上找到對象。如果一個輸入像素的顏色落在一個預(yù)定義的盒子（定義的閾值），它是屬于這個箱子相關(guān)的類。閾值是在三維顏色空間的盒子。屬于一個類的像素（連接區(qū)域）進行獨特的標(biāo)記。邏輯部分和一個簡單的優(yōu)化方法從先前生成的，選擇合適的地區(qū)。最后，程序的控制部分計算的線性和角速度，使機器人應(yīng)該在下一采樣時刻根據(jù)場上的形勢。這些參考轉(zhuǎn)速是通過無線連接發(fā)送給機器人，他們開始根據(jù)接收到的命令移動。上述循環(huán)重復(fù)30次每秒。 無線電發(fā)射機 籌略 標(biāo)定攝像機 計算機視覺 估計對象 確定連接區(qū)域 離線學(xué)習(xí)/初始化 陰影校正 多閾值 不斷的閾值 圖1：系統(tǒng)概述 識別 合作 圖2：機器人色標(biāo) 8結(jié)論 本章地址是傷腦筋的機器人足球社區(qū)問題；往往是通過瑣碎的問題。然而，有對問題的有效解決，文獻較少，這往往是讓人失望，球隊希望追求其他的問題在足球領(lǐng)域的源（如AI控制）。 建立一個足球比賽中的移動機器人的目的，快速和強大的視覺系統(tǒng)的一個例子。特別考慮到工作和魯棒性問題的優(yōu)化計算。后者是通過對圖像質(zhì)量的改善，如非均勻光照和鏡頭畸變校正方法包含放心。魯棒性是通過具有時效性的算法使圖像處理進一步實現(xiàn)全球。相反，一些視覺系統(tǒng)的機器人足球隊雇傭當(dāng)?shù)氐膱D像處理獲得的視覺系統(tǒng)所需的幀速率的應(yīng)用。這些算法的主要缺點是一個或多個對象的損失（機器人或球）因為一些不可預(yù)知的原因（光照下，碰撞，錯誤）。局部搜索區(qū)域必須被增加直到找到對象，這導(dǎo)致在較大的和不規(guī)則的采樣時間。這不可能發(fā)生的全球圖像處理。然而，該方法的缺點會出現(xiàn)如果大量（超過15）的不同色塊，必須遵循。一些色塊可以成為相機的圖像可能會導(dǎo)致錯誤的對象估計很相似。這個問題將在目標(biāo)跟蹤算法納入未來的工作處理。 進一步的方法建立一個更強大和精確的移動機器人的不良照明和攝像機鏡頭畸變條件下跟蹤視覺系統(tǒng)。為了提高機器人視覺跟蹤結(jié)果，提出了攝像機標(biāo)定和照度不均勻校正算法。前校正由鏡頭引起的失真，從而實現(xiàn)更準(zhǔn)確和精確的目標(biāo)位置估計，后者提高了魯棒性不規(guī)則的照明和非均勻光照條件。所建議的解決方案的適用性在機器人足球比賽中證明，任何不正確或不準(zhǔn)確估計機器人和球的位置導(dǎo)致的游戲（除了策略控制算法完善）。視覺系統(tǒng)的魯棒性是通過攝像機標(biāo)定算法提高。建議的程序為陰影校正被證明是有用的，在光照條件下或多或少保持不變在游戲。程序也假定固定攝像機視圖，在中央視覺系統(tǒng)。在一般的移動機器人，并不總是符合這些條件。如果光照條件的變化，在跟蹤過程中，一個適應(yīng)機制更有效的方法應(yīng)。這將是進一步研究解決。優(yōu)化算法，使視覺系統(tǒng)可以用于實時應(yīng)用在不規(guī)則的照明和攝像機畸變的魯棒性是很重要的。 本文摘自《VISION SYSTEM DESIGN FOR MOBILE ROBOT TRACKING》。  Abstract This chapter introduces a complete thematic of vision system for mobile robot tracking and control. It consists of a global vision system for estimation of positions and orientations of mobile robots and methods for improvement of bad operating conditions such as noise, camera lens distortion and non-uniform illumination. The basic operation of a vision system is divided into two steps. In the first, the incoming image is scanned and pixels are classified into a finite number of classes. At the same time, a segmentation algorithm is used to find the corresponding regions belonging to one of the classes. In the second step, all the regions are examined. A selection of the ones that are a part of the observed object is made by means of simple logic procedures. The novelty of the used approach is focused on optimization of the processing time needed to finish the estimation of possible object positions. Further on an approach to improve an already existing vision system performance under bad operating conditions is presented. Some fundamentals and solutions to accompanying problems in vision system design for mobile robot tracking are presented. Besides methods for filtering and improvement of identified noisy data the two main factors which deteriorate the performance are dealt with, namely, non-uniform illumination and camera lens distortion. For the former the problem area and its origins are focused on and a solution for its compensation by applying multiplicative component defined by illumination plain is given. The latter consists of two steps. In the first, radial lens distortion fundamentals are discussed. The suggested solution for its verification is realized by a geometry model of lens projection. The second step covers the perspective distortion originating from the tilt of the camera. For its correction an efficient and robust method of vanishing point detection is applied. Both correction methods contribute to a vision system performance if implemented in the appropriate manner. Applicability of the presented approaches is confirmed on a robot soccer test bed. Robot soccer is a fast dynamic game and therefore needs an efficient and robust vision system. To improve the results of the robot soccer vision system the proposed camera calibration and non-uniform illumination correction algorithm are implemented. The lens correction method successfully corrects the distortion caused by the camera lens, thus achieving a more accurate and precise estimation of the object position. The illumination compensation improves robustness to irregular and non-uniform illumination which is nearly always present in real conditions. 1 Introduction There are many ways of detecting moving objects using color cameras. However, the vision systems based on color information proved to be more simple, robust and faster than most of other recognition methods as stated in [3,5,13,16]. Sargent et al. [13] developed a fast real-time vision system with the aid of a special hardware accelerated system, which only makes sense when software optimizations or accelerations are not possible. A more reliable vision tracking of moving objects can be achieved by using robust statistics and probability distributions. A good example of the latter is given in the color-based face tracking implemented by Bradski [2]. Bruce et al. [3] suggested a fast vision system for mobile robots by means of efficient color segmentation and a two-pass connected region determination algorithm. Another important contribution to the robot soccer vision design was introduced by Wyeth et al. [16], with special consideration given to the robustness of varying playground illumination conditions. Most of the approaches try to classify the pixels of an image into one of a predefined number of classes. The most common are: linear color thresholding, K-nearest neighbor classification, neural net-based classifiers, classification trees and probabilistic methods [10,1,8]. The chapter presents a design of a global vision system for estimating current object positions and orientations on the playground. The MiroSot category soccer robots we are interested in are without on-board position sensors. Thus a precise and fast global vision has to be designed for robots control and navigation in a partially controlled, dynamically changing environment. When designing the vision system, the following requirements have to be accomplished: ? computational efficiency, ? high reliability, ? good precision, and ? robustness to noise, non-uniform illumination and different color schemes. The last characteristic is essential for the system to function well when using it under different conditions present at competitions [16]. In this paper, a fast approach with constant thresholding and back-stepping algorithm is presented, where a special attention is given to the efficiency aspect. The thresholds can be presented as boxes in 3-dimensional color spaces. These thresholds are determined by means of off-line learning. If an incoming pixel color falls inside one of the predefined boxes, then it is classified as belonging to the class associated with this box. This first step is followed by the second step where the pixels belonging to one class (a connected region) are distinctively labeled. With the main purpose of obtaining all fully connected regions, a back-stepping algorithm is applied. Both steps are done with just one scan of the image. Then the logic part and a simple optimization method are employed to select the proper regions from the previously generated ones. After this logic the positions and orientations of the objects on the playground are estimated. To improve results of the vision system the camera calibration and non-uniform illumination correction algorithm are implemented. The former corrects distortion caused by the camera lens, thus achieving a more accurate and precise objects positions estimation, while the latter improves robustness to irregular illumination and non-uniform illumination conditions. An attempt to improve the already-existing vision systems performance under poor operating conditions is next presented. Two main factors, which adversely affect performance, are dealt with: non-uniform illumination and camera lens distortion. For the former, the focus is placed on the problem area and its origins [6], with a solution for its compensation by means of the application of a multiplicative component defined by an illumination plane given. The latter consists of two steps. In the first, radial lens distortion fundamentals are discussed [9,14]. The suggested solution for its verification is realized by means of a geometric model of lens projection [11]. The second step covers the perspective distortion originating from the tilt of the camera. For its correction, an efficient and robust method of vanishing point detection [4,12] is applied. The applicability of the presented approaches is confirmed on the robot soccer test bed. To improve the results of the robot soccer vision system [5], both the proposed camera calibration and non-uniform illumination correction algorithm are implemented. The chapter is organized as follows. In section 2 a brief overview of the system is given. The method used for pixel classification is explained in section 3. Section 4 focuses on the algorithms for image segmentation and region labeling. The algorithm for object estimation is illustrated in section 5. Section 6 resumes the data filtering, camera calibration and non-uniform illumination correction implementation. Obtained experimental results are shown in section 7. The chapter ends with conclusions and some ideas for future work. 2 System Overview The presented vision system is demonstrated on a robot soccer set-up. The soccer robot set-up, Fig. 1, consists of ten MiroSot category robots (forming two teams) of size 7.5 cm cubed, a rectangular playground of size 2.2×1.8 m, a digital color camera Sony DFW-V500, and a personal computer Pentium 4. The vision part of the program processes the incoming images, of a resolution of 640×480 pixels, to identify the positions and orientations of the robots and the position of the ball. Each robot has two square-shaped color patches (Fig. 2). One is the team color and the other is the identification color patch. According to FIRA (Federation of International Robot-soccer Association) rules, the team color must be blue or yellow, the ball must be orange and identification colors can be any color except the team and ball color. The vision algorithm finds objects on the playground by taking their color and shape into consideration. If an incoming pixel color falls inside one of the predefined boxes (defined by thresholds), it is classified as belonging to the class associated with this box. The thresholds are presented as boxes in three-dimensional color spaces. The pixels belonging to one class (a connected region) are then distinctively labeled. The logic part and a simple optimization method are employed to select the proper regions from the previously generated ones. Finally, the control part of the program calculates the linear and angular speeds, that the robots should have in the next sample time according to the current situation on the playground. These reference speeds are sent to the robots by a wireless connection and they start moving according to the received commands. The above-mentioned cycle repeats itself 30 times per second. 8 Conclusion The issues the chapter address are vexing ones for the robot soccer community; issues that are often passed as trivial. However, there is little literature on effective solutions to the problems, which is often source of frustration to teams who wish to purse other issues in the soccer domain (such as AI and control). An example of establishing a fast and robust vision system for the purpose of mobile robots in soccer game is presented. Special consideration is given to optimization of computational work and robustness issues. The latter are assured by inclusion of methods for image quality improvement such as correction of nonuniform illumination and camera lens distortions. Robustness is further achieved by time-efficient algorithms which enable global image processing. Contrary, some vision systems used by other robot soccer teams employ local image processing to obtain the desired frame rate of the vision system. The major disadvantage of these algorithms is loss of one or more objects (robots or ball) because of some unpredicted reasons (lightening conditions, collisions, bugs). The local search areas have to be increased until objects are found, which results in larger and irregular sample time. This could not happen with global image processing. However, disadvantage of the presented approach can appear if a large number (more than 15) of different color patches have to be followed. Some of color patches could then become quite similar on camera image which could result in wrong objects estimation. The problem will be dealt with in the future work by inclusion of object tracking algorithms. Further on an approach towards establishing a more robust and accurate vision system for mobile robot tracking under poor illumination and camera lens distortion conditions is presented. To improve the results of visual robot tracking, a camera calibration and non-uniform illumination correction algorithm are suggested. The former corrects the distortion caused by the camera lens, thus achieving a more accurate and precise estimation of object position, while the latter improves robustness to irregular illumination and non-uniform illumination conditions. The applicability of the suggested solutions is demonstrated in a robot soccer game, where any incorrect or inaccurately estimated robot or ball position results in poor game-play (apart from perfection of the strategy control algorithm). The robustness of the vision system is therefore improved by means of camera calibration algorithms. The suggested procedure for shading correction proved useful when the illumination conditions remained more or less unchanged during the game. The procedure presented also assumes fixed camera view, as in central vision systems. In general mobile robotics, these conditions are not always met. If illumination conditions change during tracking, a more robust approach with an adaptation mechanism should be applied. This will be addressed in further research. The optimized algorithms presented enable the vision system to be used in real-time applications where robustness to irregular illumination and camera distortions are important. 10 教師評語 教師簽名： 2015 年 月 日