臺灣博碩士論文加值系統

智慧工廠的核心之一是運用機器人的生產效率和製程的精準度來推動產業的提升，而有效增加協作型機器人與人的互動效率，是邁向工業4.0的關鍵之一。人和機器人的互動作業中，若作業員能有效預測機器人行動路徑及意圖，將能夠提升人機協作的流暢，讓作業員提早準備，減少協作過程中的延遲甚至是中斷。一般在相關研究中，均相信人類的遞交或抓取等手部運動的本身，即提供較充分的路徑及意圖資訊，因此針對此等作業中，人與人之間會有較流暢的互動。本研究目的即在探討，機器手臂運動路徑有那些因素對於作業員在互動預測機械手臂目的地的時候，有最大的影響，進而提供互動作業員較佳之行動路徑及意圖判斷。

The production efficiency and process accuracy brought by robots to shop floor is essential to the successful adaption of an intelligent factory. And effectively increase the interaction efficiency between collaboration between a robot and a human operator is one of the keys moving toward Industry 4.0. During human-robot interaction, if an operator can effectively predict the path and intention of a robot, it will improve the fluency of interactive tasks and enable the operator to prepare in advance to reduce delay or interruption. It is generally believed in related research that some movements made by human hands, such as hand-over or grasp, provide adequate information regarding path prediction and intention. Such information allows a fluent interaction between human operators. This study is to explore the factors that have the greatest impact when the operator is collaborating with robot and predicting its destination, and thus provide the operator with better motion path and intentional judgment.

誌　　謝 I
摘　　要 II
ABSTRACT III
目錄 IV
圖目錄 VI
表目錄 IX
一、 緒論 1
1.1研究背景與動機. 1
1.2 研究目的. 2
1.3 研究架構. 3
二、 文獻探討 4
2.1動作路徑規劃(MOTION PLANNING). 4
2.1.1機器人預測人類行為. 4
2.1.2人類預測機器人行為. 6
2.1.3擬人運動軌跡. 7
三、 研究設備及方法 10
3.1研究設備與系統介紹. 10
3.1.1協作型機器人. 10
3.1.2 PhaseSpace動作擷取系統(Motion Capture). 10
3.2實驗設計. 11
3.2.1路徑設計. 11
3.2.2遞交空間設計. 14
3.3實驗作業及變數. 19
3.3.1 實驗作業. 19
3.3.2 實驗變數. 20
3.3.3 Dynamic Time Warping – DTW. 22
3.4 實驗程序. 23
四、 實驗分析與結果 24
4.1主觀量測 24
4.1.1路徑與遞交點的分析 24
4.1.2路徑與遞交平面的分析 28
4.2客觀量測. 41
4.2.1動態量測下路徑與遞交點的分析. 41
4.2.2動態量測下路徑與遞交平面的分析. 45
4.2.3靜態量測下遞交面分析. 58
五、 結果討論與未來展望 60
5.1結果討論. 60
5.1.1主觀量測結論. 60
5.1.2客觀量測結論. 60
5.1.3靜態量測結論. 60
參考文獻 61

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