本篇論文針對含未知參數或參數變動之麥卡倫全方位行動機器人，分別發展該機器人之SoPC-based里程計算法，路徑追蹤控制器，非奇異終端滑模控制器，全域路徑規畫以及其實驗。該兩種控制器皆利用Lyapunov 穩定理論證明其近似穩定特性，並藉實驗數據以及電腦模擬，證明其定位控制與軌跡追蹤的性能。在全域路徑規劃之設計程序中，整合粒子群與實數型基因演算法之方法被用來找尋已知地圖中的最佳路徑路徑，並結合上述的運動控制器近，可達成良好的定位與導航。模擬結果與實驗數據顯示論文提出的全域路徑規畫法具有有效性。 This thesis develops techniques and methodologies for navigation system design and SoPC implementation of a Mecanum wheeled omnidirectional mobile robot (MWOR). The navigation system is composed of four new modules: odometry, kinematic motion controller, nonsingular terminal sliding-mode dynamic motion controller, and global path planner, which have been implemented using the SoPC technology. The odometry is constructed by using a numerical method and a kinematic model of the robot, in order to keep track of the current position and orientation of the robot over short distances. Both kinematic motion control and nonsingular terminal sliding-mode dynamic control are well derived to achieve simultaneous point stabilization and trajectory tracking. A hybrid PSO (particle swarm optimization)-RGA (real-coded genetic algorithm) algorithm is proposed to find an optimal path between a starting and ending point in a given grid environment. Simulations and experimental results are conducted which have shown the feasibility and effectiveness of the proposed control methods.