在這近十年的研究中，自主式機器人已經是一個引起高度注目的研究領域，舉凡是家庭照護系統的應用，探索危險區域，救援任務以及軍事用途上都能夠看見機器人的利用價值。機器人自主式避障行為以及定位系統的表現，對於機器人系統應用在室內環境來說佔有極重要的優先發展基礎而構成本篇論文的兩大討論主題。
在本篇論文探討中，具體的室內環境可做為模仿居家照護場景以及聯合式機器人系統模擬 (Joint system)。 機器人非線性的移動行為和IMU訊號狀態處理是兩大主要目標來克服，論文架構中將提出使用Unscented卡爾曼濾波器來解決IMU系統狀態中因積分產生的累積積分誤差，並使用天花板全向照相機做為修正誤差的參考訊號。主要結構依據攝影機的參考軌跡透過UKF演算法去執行IMU的非線性狀態更新來達到修正誤差並定位的功能。本篇論文並探討了自主式避障行為根據類神經演算法設計，以256種模式來分別環境並做出適當反應使其避障行為成功。雷射測距儀為主要的儀器應用在避障行為上。
實驗結果說明了類神經演算法應用於避障行為的有效性和成立，另外Unscented 卡爾曼濾波器不管在開迴路或閉迴路的演算結構中，皆可以達成收斂的效果以避免累積積分誤差之主要因素。其中實驗結果並評估了關於自主式避障非線性行為的定位誤差分析，以做出將此演算法應用在低花費的慣性導航系統之貢獻。

In recent year, autonomous mobile robot has become an important and popular research topic. It is widely used in the application of homecare system, exploring in dangerous, rescue task and military. The foundation of the mobile robot behavior is the obstacle avoidance, and localization system is also same priority which applied for the indoor environment.
In this thesis, the specific indoor environment is the set up as scenario which is applied for homecare service robot and joint robot system. The nonlinear motion of mobile robot and signal state processing of IMU is the two main proposed approaches to overcome. The methodology in framework is illustrated the Unscented Kalman filter to solve the problem about accumulated error of IMU integrate with ceiling omni-directional camera. The main structure of the algorithm executes and updates of the IMU’s nonlinear state output by observing signal from camera. The other part proposes a Neural Network control system that is able to guide the mobile robots traverse through a maze with arbitrary obstacles. For input data, laser range finder is main sensors for passing on information of environment.
The empirical results show the effectiveness and the validity of the obstacle avoidance behavior of Neural Network control strategy. The evaluation of UKF applied on system state of IMU’s output state can be actuality convergence to overcome the accumulated error and finish the goal of localization.

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