隨著各式各樣的產業逐漸步入自動化的階段，例如貨物的運輸、手術的操作，越來越多需求在尋找能由機器人代勞的可能，此時器械、機械的運動學設計就成為產品研發之初，相當必要的環節。
機械動力傳遞主要分成齒輪、凸輪、和連桿三種方式，其中本研究著眼於連桿機構的運動學設計。運動學設計又涵蓋運動學分析以及運動學合成，本研究將以一使用基因演算法針對運動目標，生成連桿機構構型的運動學合成方法為基礎，加上連桿機構運動學分析方法，對前者的運動學合成結果進行運動學分析，並對指定輸入角度/位移分析對應的輸出構型，使預測生成機構的運動結果更加完整，也改善前者（運動學合成方法）在計算接頭座標時，原先使用約束疊加法在小角度上的限制和誤差。
在以上分析過程中，本研究為從任意生成的連桿機構，自動取得必要的資訊以帶入分析，需要進行一自由度修正的動作，將多餘而不影響機構運動方式的連桿排除於分析式外，進而計算出完整且精準的分析結果。
本研究主要分為五個階段：連桿機構學的應用方式、基因演算法結果的資料擷取、自由度修正、最佳解機構構型運動學分析、對指定輸入的對應輸出構型。最後詳細介紹整體分析流程，並以圖示呈現最佳機構構型與輸出的運動學分析結果、構型運動方式，以及與原方法的結果比較。

As various industries gradually enter the stage of automation, there is an increasing demand to find tasks that can be performed by robots, such as transportation of goods or delicate operations like surgeries. In such cases, the kinematic design of mechanisms and machines becomes a crucial aspect during product development.
Mechanical power transmission can be mainly classified into three methods: gears, cams, and linkages. This study focuses on the kinematic design of linkage mechanisms. Kinematic design consists both kinematic analysis and kinematic synthesis. In this research, we build upon a work utilizes genetic algorithms to generate kinematic synthesis of linkage mechanisms based on motion objectives. We combine it with a kinematic analysis method for linkage mechanisms to analyze the kinematic synthesis results and predict the output configurations corresponding to any input angles/displacements. This enhances the completeness of predicting the motion results of the generated mechanisms and improves the limitations and errors present in the kinematic synthesis original method when calculating joint coordinates using the constrained superposition method at small angles.
Throughout the analysis process, this study automates the acquisition of necessary information from any generated linkage mechanism for analysis. A mobility correction is performed to exclude redundant linkages that do not affect the motion of the mechanism from the analysis equations, resulting in comprehensive and accurate analysis results.
The study is divided into five sections: application of linkage mechanisms kinematics, data extraction from genetic algorithm results, mobility correction, kinematic analysis of optimal solution mechanism, and determination of corresponding output configurations for arbitrary inputs. The overall analysis process is described in detail, and the results of kinematic analysis, motion characteristics, and a comparison with the original method are presented using illustrations to showcase the optimal mechanism configuration and its corresponding kinematic analysis results.

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