系統識別號 | U0002-0203201500072000 |
---|---|
DOI | 10.6846/TKU.2015.00040 |
論文名稱(中文) | 基於實務型參數最佳化之人形機器人線上步態訓練系統 |
論文名稱(英文) | Online Gait Training System Based on Practical Parameters Optimization for Humanoid Robot |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 電機工程學系博士班 |
系所名稱(英文) | Department of Electrical and Computer Engineering |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 103 |
學期 | 1 |
出版年 | 104 |
研究生(中文) | 胡越陽 |
研究生(英文) | Yueh-Yang Hu |
學號 | 897440037 |
學位類別 | 博士 |
語言別 | 繁體中文 |
第二語言別 | |
口試日期 | 2015-01-26 |
論文頁數 | 120頁 |
口試委員 |
指導教授
-
翁慶昌
委員 - 龔宗鈞 委員 - 黃志良 委員 - 許陳鑑 委員 - 王偉彥 委員 - 許駿飛 委員 - 鄭吉泰 |
關鍵字(中) |
人形機器人 線上步態訓練系統 實務型粒子群最佳化 實務型全域最佳引導人工蜂群 參數型中樞型態產生器 |
關鍵字(英) |
Humanoid Robot Online Gait Training System PPSO PGABC PCPG |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
本論文針對小型人形機器人提出一個基於實務型參數最佳化之線上步態訓練系統,主要探討行走步態、雜訊環境下之參數最佳化以及線上步態訓練系統等三大項的設計。在行走步態的設計上,本論文提出一個參數型中樞型態產生器(PCPG),其依據腰部與腳部之末端點的關係來產生人形機器人的行走步態。在雜訊環境下之參數最佳化的設計上,本論文提出兩個最佳化演算法:實務型粒子群最佳化(PPSO)以及實務型全域最佳引導人工蜂群(PGABC)。從實驗結果中顯示,所提出的PPSO與PGABC演算法相較於一般的PSO與GABC演算法確實能夠更有效地選取到一組較佳的參數數值。在線上步態訓練系統的設計上,本論文應用所提出的PPSO以及PGABC演算法來自動選取一組較佳之行走步態參數數值,讓人形機器人可以有效且穩定地走到所指定的目標點。首先設計實現一個步態訓練平台,讓人形機器人能夠依據目前之行走步態的參數值來自動地步行至目標點。當機器人走到目標點時,步態訓練平台會自動地將機器人拉回至起始點,然後繼續執行下一組行走步態參數數值的步態訓練。從實驗結果可知,本論文所提出之PPSO與PGABC演算法可以確實有效地找到一組不錯的行走步態參數數值。 |
英文摘要 |
In this dissertation, an online gait training system is proposed for a small-sized humanoid robot based on two practical parameters optimal methods. There are three main designed topics of walking gait, parameters optimization in a noisy environment, and online gait training system. In the walking gait design, a method named Parameter Central Pattern Generator (PCPG) is proposed to generate the walking gait of humanoid robot, and generated by the relationship between waist and feet points. In the parameters optimal design in a noisy environment, two optimal algorithms are proposed: Practical Particle Swarm Optimization (PPSO) and Practical Gbest-guided Artificial Bee Colony (PGABC). Some experiment results are presented to illustrate that the parameter set selected by PPSO and PGABC algorithms is better than that selected by the general PSO and GABC algorithms. In the online gait training system design, the proposed PPSO and PGABC algorithms are applied in the online gait training system to automatically select a better parameter set of walking gait so that the humanoid robot can effectively and stability walk to the assigned goal point. First, a gait training platform is designed and implemented so that the humanoid robot can automatically walk to the assigned goal point according to the current parameters of walking gait. When the robot attains the goal point, the gait training platform will pull it back to the start point. Then the next parameter set will be continuously executed. Finally, some experiment results are presented to illustrate that a better parameter set of walking gait can be efficiently found by the proposed PPSO and PGABC algorithms. |
第三語言摘要 | |
論文目次 |
目錄 目錄 I 圖目錄 IV 表目錄 X 第一章 緒論 1 1.1. 研究背景 1 1.2. 研究動機 3 1.3. 研究目的 4 1.4. 論文架構 5 第二章 參數型中樞型態產生器 6 2.1. 前言 6 2.2. 人形機器人系統設計 7 2.3. 行走軌跡設計 13 2.4. 逆運動學與馬達速度規劃 31 2.5. 實驗結果 34 第三章 PGABC之參數最佳化搜尋 46 3.1. 前言 46 3.2. 全域最佳引導人工蜂群演算法 47 3.3. 實務型全域最佳引導人工蜂群演算法 52 3.4. 實驗結果 57 第四章 PPSO之參數最佳化搜尋 65 4.1. 前言 65 4.2. 粒子群最佳化演算法 66 4.3. 實務型粒子群最佳化演算法 68 4.4. 實驗結果 71 4.5. 參數最佳化實驗結果比較 77 第五章 線上步態訓練系統 81 5.1. 前言 81 5.2. 線上步態訓練系統設計 82 5.3. 步態訓練平台定位測試 86 5.4. PGABC之實驗結果 88 5.5. PPSO之實驗結果 93 5.6. 參數最佳化實驗結果比較 96 第六章 結論與未來展望 101 6.1. 結論 101 6.2. 未來展望 102 參考文獻 103 研究著作 109 期刊論文 109 會議論文 109 學位論文 111 獲獎經歷 112 圖目錄 圖1.1、行走步態參數最佳化問題示意圖 3 圖2.1、第九代人形機器人架構圖:(a)硬體設計與(b)系統架構圖 8 圖2.2、工業電腦實體圖 9 圖2.3、FPGA開發板:(a)正面與(b)反面 10 圖2.4、第九代人形機器人:(a)實體圖與(b)機器人維度圖 11 圖2.5、人形機器人機構設計與尺寸圖 11 圖2.6、自由度設計圖:(a)頭部、(b)腰部、(c)手部以及(d)腳部 13 圖2.7、人形機器人之座標 14 圖2.8、人形機器人旋轉動作:(a) 左腳旋轉與(b)右腳旋轉 18 圖2.9、人形機器人行走軌跡 19 圖2.10、前進行走步態模擬波形圖:(a)腰部、(b)左腳踝關節與 (c)右腳踝關節 22 圖2.11、左旋轉行走步態模擬波形圖:(a)腰部、(b)左腳踝關節與 (c)右腳踝關節 24 圖2.12、右旋轉行走步態模擬波形圖:(a)腰部、(b)左腳踝關節與 (c)右腳踝關節 26 圖2.13、左平移行走步態模擬波形圖:(a)腰部、(b)左腳踝關節與 (c)右腳踝關節 29 圖2.14、右平移行走步態模擬波形圖:(a)腰部、(b)左腳踝關節與 (c)右腳踝關節 31 圖2.15、馬達角度與座標標示圖:(a)側視圖與(b)正視圖 33 圖2.16、PCPG前進實驗圖:(a) 45°視角、(b)右方側視角與(c)正面視角 37 圖2.17、PCPG左旋轉實驗圖:(a) 45°視角、(b)右方側視角與(c)正面視角 39 圖2.18、PCPG右旋轉實驗圖:(a) 45°視角、(b)左方側視角與(c)正面視角 41 圖2.19、PCPG左平移實驗圖:(a) 45°視角、(b)左方側視角與(c)正面視角 43 圖2.20、PCPG右平移實驗圖:(a) 45°視角、(b)左方側視角與(c)正面視角 45 圖3.1、GABC演算法之流程圖 51 圖3.2、收益度計算與進行濾波之流程圖 52 圖3.3、PGABC演算法之流程圖 53 圖3.4、理想函式與最佳適應函式數值位置 58 圖3.5、加入雜訊後當下的最佳適應函式數值位置 58 圖3.6、GABC使用25個食物源搜尋150代之位置 59 圖3.7、GABC收益度數值的變化 60 圖3.8、GABC重複測試100次最佳的收益度數值 60 圖3.9、GABC重複測試100次收斂代數之情況 60 圖3.10、GABC於雜訊環境下找的其中一組解:(a)理想函式圖形與 (b)雜訊環境下函式圖形 62 圖3.11、GABC於雜訊環境下重複測試100次最佳的收益度數值 62 圖3.12、PGABC於雜訊環境下找的其中一組解:(a)理想函式圖形與(b)雜訊環境下函式圖形 63 圖3.13、PGABC於雜訊環境下重複測試100次最佳的收益度數值 64 圖4.1、PSO演算法之流程圖 68 圖4.2、PPSO演算法之流程圖 70 圖4.3、PSO使用50顆粒子迭代50代最佳適應函式數值的位置 72 圖4.4、PSO適應函式數值的變化 73 圖4.5、PSO重複測試100次最佳的適應函式數值 73 圖4.6、PSO重複測試100次之平均收斂代數 73 圖4.7、PSO於雜訊環境下找的其中一組最佳解:(a) 理想函式圖形與(b) 雜訊環境下函式圖形 75 圖4.8、PSO於雜訊環境下重複測試100次最佳的適應函式數值 75 圖4.9、PPSO於雜訊環境下找的其中一組最佳解:(a) 理想函式圖形與(b) 雜訊環境下函式圖形 76 圖4.10、PPSO於雜訊環境下重複測試100次最佳的適應函式數值 77 圖4.11、第二個基準測試函式 78 圖4.12、第三個基準測試函式 78 圖4.13、第四個基準測試函式 79 圖4.14、第五個基準測試函式 79 圖5.1、系統的運作流程:(a) 機器人歸位、(b)跟隨機器人、(c)在抵達終點或跌倒時抬起機器人以及(d)將機器人送回原點 82 圖5.2、線上步態訓練系統之系統架構圖 83 圖5.3、步態訓練系統之定位感測器:(a) 超音波感測器與(b) LifeCam網路攝影機 84 圖5.4、人形機器人上之感測色塊 85 圖5.5、步態訓練平台機構設計:(a) x軸移動機構、(b) y軸移動機構、 (c) 拉升機構以及(d)機器人上的掛鉤 85 圖5.6、行走步態學習的實際情況 86 圖5.7、步態訓練平台圓形追蹤誤差測試之LEGO套件:(a) 95mm與 (b) 160mm 87 圖5.8、步態訓練平台圓形追蹤誤差測試:(a) 95mm與(b) 160mm 87 圖5.9、步態訓練平台圓形追蹤誤差情況:(a) 95mm與(b) 160mm 88 圖5.10、線上步態訓練最佳參數集走到的位置 89 圖5.11、PCPG於模擬中測試五次之軌跡:(a)理想軌跡與(b)有雜訊時軌跡 90 圖5.12、GABC之最佳參數集所走到的位置 91 圖5.13、GABC在理想環境下重複測試100次走到的位置 91 圖5.14、GABC在雜訊環境下最佳參數集走到的位置 91 圖5.15、重複測試100次最佳參數集走到的位置與實際機器人走到的位置比較圖 92 圖5.16、PGABC在雜訊環境下最佳參數集走到的位置 92 圖5.17、重複測試100次最佳參數集走到的位置與實際機器人走到的位置比較圖 93 圖5.18、PSO之最佳參數集所走到的位置 94 圖5.19、PSO在理想環境下重複測試100次走到的位置 94 圖5.20、PSO在雜訊環境下最佳參數集走到的位置 95 圖5.21、重複測試100次最佳參數集走到的位置與實際機器人走到的位置比較圖 95 圖5.22、PPSO在雜訊環境下最佳參數集走到的位置 96 圖5.23、重複測試100次最佳參數集走到的位置與實際機器人走到的位置比較圖 96 圖5.24、未加入修正參數集重複行走15次 98 圖5.25、GABC之參數重複行走15次 98 圖5.26、PGABC之參數重複行走15次 98 圖5.27、PSO之參數集重複行走15次 99 圖5.28、PPSO之參數集重複行走15次 99 表目錄 表2.1、工業電腦規格表 9 表2.2、FPGA開發板規格表 10 表2.3、馬達規格 13 表2.4、機器人行走步態振盪器的共同參數 20 表2.5、機器人前進行走步態之振盪器參數 20 表2.6、機器人前進行走步態參數 22 表2.7、機器人左旋轉行走步態之振盪器參數 23 表2.8、機器人左旋轉行走步態參數 24 表2.9、機器人右旋轉行走步態之振盪器參數 25 表2.10、機器人右旋轉行走步態參數 27 表2.11、機器人左平移行走步態之振盪器參數 27 表2.12、機器人左平移行走步態參數 29 表2.13、機器人右平移行走步態之振盪器參數 30 表2.14、機器人右平移行走步態參數 31 表2.15、機器人前進行走步態參數 35 表4.1、重複測試100次(N=MCN=150) 80 表4.2、10%雜訊,重複測試100次(N=MCN=150) 80 表5.1、機器人前進行走步態參數表 97 表5.2、模擬環境下參數集誤差比較表 100 表5.3、實際測試環境下參數誤差比較表 100 |
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