基於深度神經網路之雙足機器人系統建模

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姓名

王士銘(Shih-Ming Wang) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

基於深度神經網路之雙足機器人系統建模
(System Modeling of Biped Robot with Deep Neural Network)

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至系統瀏覽論文 (2024-10-10以後開放)

摘要(中)

本論文將結合雙足機器人與機器學習，使用機器學習當中的監督式學習(Supervised Learning)方法為基礎，實現一種新創的雙足機器人系統建模方法。相較於傳統的解析方法，本方法因在其運算上的速度優勢，可望能夠提升機器人相關研究的運算效率。本論文主要討論一種通過深度神經網路(Deep Neural Network) 近似科列斯基分解形式(Cholesky-decompostion)雙足機器人系統的質量矩陣(Mass Matrix)的方法。在本論文中，針對一架在單足支撐階段具有5自由度的雙足機器人，基於Euler–Lagrange equation，建立其機器人系統在動態步行中的動力學模型，並利用解析方式推導其在單足支撐階段(Single Support Phase)的質量矩陣。其後利用所推導之解析質量矩陣訓練一個深度神經網路模型映射機器人系統的廣義座標與機器人系統的科列斯基分解形式質量矩陣。在經過 32 小時 50 分鐘的訓練後，能夠獲得一個在驗證階段的近似機器人系統之質量矩陣時有低於 5%的誤差的深度神經網路模型。為證明了此方法的優越性，本論文比較了三種由機器人在單足支撐階段的質量矩陣透過系統拘束方程式求得機器人在碰撞階段的拘束衝量的方法的計算速度。在拘束方程式中使用此種方法近似的質量矩陣進行運算，比使用解析質量矩陣快了5.375 毫秒。同時透過在實際機器人步態軌跡上的驗證，證明了此方法的實用性。

摘要(英)

This thesis investigated a method that uses a deep neural network to encode the biped robot system. In detail, a supervised learning model is trained to approximate the mass matrix of the biped robot system in Cholesky-decomposed form. This method is expected to have higher computational efficiency than traditional system modeling methods. This thesis discusses a biped robot system with 5 degrees of freedom during the single support phase of walking. The ground truth of the supervised learning task is the biped robot system’s analytical mass matrix, derived from the Euler–Lagrange equation. A deep neural network is trained to map the generalized coordinates of the system to the mass matrix in Cholesky-decomposed form with reference to the analytical mass matrix. The training result shows that the neural network accurately encodes the biped robot system′s mass matrix with an approximation error of less than 5% during the testing stage after 32 hours and 50 minutes of training. The discussion compares three ways to get the constraint impulse during the impact phase of bipedal walking via mass matrix. The comparative experiment result shows that computing with the Cholesky-decomposed mass matrix encoded by the deep neural network is 5.375 milliseconds faster than computing with the analytical mass matrix. This thesis also demonstrated the practicality of this method by verifying its approximation error on actual robot gait trajectories.

關鍵字(中)

★ 雙足機器人
★ 深度神經網路
★ 監督式學習
★ 科列斯基分解

關鍵字(英)

★ Biped Robot
★ Deep Neural Network
★ Supervised Learning
★ Cholesky-decompostion

論文目次

摘要i
Abstract ii
誌謝iii
目錄iv
使用符號與定義viii
一、緒論1
1.1 引言........................................................................ 1
1.2 研究動機.................................................................. 1
1.3 研究背景.................................................................. 4
1.3.1 人形機器人之動作規劃....................................... 4
1.3.2 雙足機器人之數學建模....................................... 7
1.3.3 人工智慧於人形機器人之應用.............................. 8
1.4 研究目標與貢獻......................................................... 10
1.5 章節說明.................................................................. 10
二、機器人架構12
2.1 雙足機器人機構設計與配置.......................................... 12
2.1.1 雙足機器人足部機構設計與配置........................... 12
2.2 雙足機器人硬體設計與配置.......................................... 14
2.2.1 控制系統設計與配置.......................................... 14
2.2.2 馬達設計與配置................................................ 14
2.2.3 遙控模組設計與配置.......................................... 16
2.2.4 電源設計與配置................................................ 16
三、機器人數學建模17
3.1 雙足機器人之步態規劃................................................ 17
3.2 單足支撐階段動力學模型............................................. 19
3.3 碰撞階段動力學分析................................................... 23
四、機器人深度學習建模27
4.1 以神經網路近似科列斯基分解形式質量矩陣..................... 28
4.2 機器學習與深度神經網路............................................. 29
4.3 訓練資料集............................................................... 30
4.4 成本函數.................................................................. 31
4.5 模型評價方法............................................................ 32
4.6 訓練指引.................................................................. 33
五、實驗結果與討論35
5.1 近似實驗結果............................................................ 35
5.2 近似質量矩陣之運算效率驗證....................................... 38
5.3 在雙足機器人實際步態上的驗證.................................... 40
六、總結44
6.1 未來展望.................................................................. 44
參考文獻46

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指導教授

曹嘉文(Chia-Wen Tsao)

審核日期

2022-10-13

推文