本研究研發一款具有高自由度的12-DOF自主巡檢機器狗，採用馬達驅動足形式類別設計，並在腿部設計上有所創新，本研究將兩顆馬達並排且上下相反放置，並在側面加入一顆馬達，運用兩組四連桿和一個三連桿組成的結構，並使用兩個共同軸心搭配軸承設計，通過軸承之間的空心轉動，實現四組腿步模組都具有3個自由度獨立運行擺動，賦予機器狗具有高度自主性、穩定性和適應性；此外為實現自主導航移動的機器狗，身體內部配置為本研究電路的核心，電路板搭載微型控制（MCU）和其他回饋感測器、電池輸入接口及放電口等；身體上則搭載可攜式小電腦、相機與光學雷達；系統採用分散式架構，使用Ubuntu 18.04的ROS平台進行地圖建置、自動導航、讀取機器人狀態與接收感測器回傳資訊。
本研究首先對機器狗腿部的運動學進行分析，利用MATLAB對逆向運動學的參數進行模擬，驗證機器狗腿步運動空間和步態（如Walk，Trot）的可行性。為實現機器狗在行走過程中的穩定性，本研究結合多種感測器，進行步態驗證，通過誤差校正後，將行走時的Pitch與Roll的誤差限於2.5°內，確保機器狗的穩定行走。在自主導航方面，結合Hector SLAM和AMCL技術進行地圖上定位，實現機器狗自身位置的精確估計。在確定機器狗的起始點與目標點後，利用預先建立好之地圖，應用Timed-Elastic-Band Local Planner（TEB）演算法進行路徑規劃，通過各項成本的限制不斷修正導航路徑，確保在導航過程中能根據環境變化自動調整路徑。本研究實驗結果顯示，機器狗抵達目標點的誤差皆在8cm合理範圍內，顯示出良好的導航精度。

In this study, we developed a highly flexible 12-DOF autonomous patrol quadruped robot, adopting an motor-driven leg design with innovative leg structures. Two motors are placed side by side and oppositely oriented, with an additional motor on the side, employing two sets of four-bar linkages and one three-bar linkage structure. Two coaxial bearings are utilized, allowing hollow rotation between the bearings and enabling four leg modules to have three degrees of freedom for independent movement. This design provides the robot with high autonomy, stability, and adaptability. The core of this research is the internal circuitry, which includes a microcontroller unit (MCU), feedback sensors, battery input interface, and discharge ports. The quadruped robot is equipped with a portable NVIDIA Jetson Nano mini-computer, camera, and LIDAR on its body. A distributed architecture is adopted, using Ubuntu 18.04's ROS platform for map building, autonomous navigation, robot status reading, and sensor data reception.
In this study, we initially conducted an analysis of the kinematics of the robot dog's legs and used MATLAB to simulate the parameters of inverse kinematics, verifying the feasibility of the robot's leg movement space and gaits (such as Walk and Trot). To ensure the stability of the robot during walking, we combined various sensors to validate the gait. After error correction, the Pitch and Roll errors during walking were limited to within 2.5°, ensuring stable walking in different environments. In terms of autonomous navigation, we integrated Hector SLAM and AMCL technologies for map-based positioning, accurately estimating the robot's location. After determining the starting and target points, we used a pre-built map and applied the Timed-Elastic-Band Local Planner (TEB) algorithm for path planning. The navigation path was constantly adjusted based on various cost constraints, ensuring automatic adjustment of the path according to environmental changes during navigation. The experimental results showed that the robot's error in reaching the target point was within a reasonable range of 8cm, demonstrating excellent navigation accuracy.

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