本論文提出藉由安裝低成本的感測器，以提供全向移動平台位移與方向角之資訊供回授
控制使用。透過整合多組感測器以有效地完成機器人的路徑規畫與路徑追蹤功能，改善
全向輪移動平台在室內環境中到達指定目的地之性能。透過光流影像可定位出全向輪移
動平台在室內環境中的行走軌跡，再利用陀螺儀與電子羅盤等感測器的結合，我們可計
算出該移動平台在空間中的相對位置與方向。路徑規畫與障礙物閃避的功能透過場效函
數法達成，讓全向輪移動平台能夠自主航行於室內未知環境中，並透過StateFlow建立
監督式控制器來改善場傳統場效函數法的潛在缺點，使得全向輪移動平台能有夠有效的
避開演算法中的陷阱。另外，本文使用的全向輪移動平台會有類似黏滯或無反應區
(Dead-Zone)之效應，故提出一個內模控制器來控制全向輪移動平台來改善此問題所發
生的效應，使得全向輪移動平台能夠精確地追蹤至使用者所期望的路徑，增加平台移動
至目的地的準確度。整體系統之效能並透過實驗來比對與驗證。

The control integration for an omni-wheel service robot is developed in this
research utilizing multiple low-cost sensors. The path planning and trajectory tracking functions are successfully accomplished based on the sensors, and the goal reaching performance of the omni-wheel platform is improved. The displacement measurement and self-localization of the robot are primarily achieved through the optical-flow sensor. The angular movement and robot orientation are derived based on the measurements from the optical flow sensors, the electronic compass, and the gyroscope. The path planning and obstacle avoidance are developed using the potential field approach, and the robot is capable of autonomous moving in an unknown environment. A supervisory control implemented in StateFlow monitors the potential field method to avoid the algorithmic problems caused by the local minima. Furthermore, the dead-zone nonlinearity is observed inherent in the motor drivers. An internal model control is introduced to counteract the nonlinear effect and improve the low level control performance. The overall system performance is evaluated experimentally with different operating scenarios.

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