本論文旨在設計與實作一套四足機器狗系統，以解決其步態生成及姿態規劃問題。透過正逆運動學公式推導，計算出對應於四肢的 12 顆伺服馬達的關節角度，以驅動機器狗執行各項動作。系統首先於數位孿生環境中進行模擬驗證，以確認所產生的姿態符合預期，並最終部署於實體四足機器狗上進行實際測試。
在姿態規劃方面，本研究設計一套基於樹狀結構的姿態關係圖，從機器狗初始的靜止姿態出發，依循以下四個步驟推導出可行的姿態節點：第一步，改變機身姿態，並透過逆向運動學計算出馬達角度，再使用正向運動學推算機身與各關節的實際位置與姿態。第二步，將該姿態導入數位孿生環境中進行驗證，以確認其是否符合靜態穩定條件。第三步，若經驗證為穩定姿態，則將此節點納入本論文所使用的樹狀結構資料集。本系統考慮六個自由度，包括三個平移自由度（上下、前後、左右）與三個旋轉自由度（繞 X、Y、Z 軸），並透過遞佪搜尋不斷擴展姿態資料集，直到機器狗無法維持穩定姿態為止。第四步，結合樹狀結構與抬腳時機的規劃，使四足機器狗能順利完成前進與橫移動作。
在運動控制方面，本研究設計一套簡潔且模組化的控制架構，採用 ROS2 (Robot Operating System 2) 作為中介通訊與訊號處理平台，並結合數位孿生進行動作模擬與視覺化驗證，可於虛擬環境中快速進行實驗與參數調整，並且可以直接移植至實體機械狗。整體硬體系統使用，但不限於 12 顆伺服馬達驅動，對應四足的三關節設計。
目前已實作四種運動模式，分別為：原地姿態調整、單腳抬升前進、雙腳同時抬升前進，以及橫向移動。

This thesis aims to design and implement a quadruped robotic dog system to address challenges in gait generation and posture planning. By deriving forward and inverse kinematics equations, enabling the robot to perform various movements. The system is first validated through simulations in a digital twin environment to ensure the generated postures meet expectations, and is subsequently deployed on a physical quadruped robot for real-world testing.
For posture planning, this study proposes a tree-based posture graph, beginning with the robot’s initial static posture. The planning process starts by modifying the robot’s body posture and computing the corresponding motor angles using inverse kinematics. Forward kinematics is then applied to estimate the actual positions and orientations of the body and its joints. Next, the generated posture is tested within the digital twin environment to verify whether it satisfies static stability conditions. If confirmed as stable, the posture is added to the tree-structured dataset constructed in this study. The system considers six degrees of freedom—three translational (vertical, forward/backward, lateral) and three rotational (roll, pitch, yaw)—and uses recursive searching to continuously expand the posture dataset until no further stable postures can be found. Finally, by combining the posture tree with foot-lifting timing strategies, the quadruped robot is enabled to perform both forward and lateral locomotion smoothly.
In terms of motion control, this study develops a concise and modular control architecture, utilizing ROS2 (Robot Operating System 2) as the middleware for communication and signal processing. The digital twin environment is used to simulate and visualize motion, enabling rapid testing and parameter tuning in a virtual setting, with seamless deployment to the physical quadruped robot.
The hardware system is primarily driven by 12 servo motors, corresponding to the three-joint design of each of the robot’s four legs.
Currently, four motion modes have been implemented: in-place posture adjustment, single-leg stepping, dual-leg stepping for forward locomotion, and lateral movement.

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