本論文提出一用於全自主式電動車導航控制系統之感測器融合系統。本系統融合雷射測距儀、磁力計、以及慣量感測器的資料，並可使載具在目標軌跡上移動。藉由模糊邏輯方法改善卡曼濾波器的效能，我們可以利用慣量感測器量測載具的狀態，並以模糊式控制器控制本載具的行為。而為了增進載具行進間的安全，我們利用雷射測距儀來偵測並迴避環境障礙。
雖然卡曼濾波器常用於感測器融合之應用，但卡曼濾波器需要全球定位系統及慣量感測器的動態模型、隨機誤差模型、以及感測器系統前一刻的資料。因此，卡曼濾波器所估測出的數值深受隨機模型、感測器失效、以及系統線性化模型誤差之影響。本論文利用一模糊式感測器融合系統增進卡曼濾波器的效能。藉由感測器融合技術所得的資訊，本載具可清楚分辨指定路徑及環境中的障礙，再結合自動控制系統，本載具即可完成自動導航的任務。

This thesis presents the design and implementation of a sensor fusion system for navigation and control of an autonomous electric vehicle. The system integrates signals of laser range finders, magnetometers and inertial measurement units (IMUs). The system keeps the vehicle moving on the desired trajectory. Using Kalman filter with fuzzy-logic module, we are able to get the state of the vehicle and decide the behavior of the vehicle by a fuzzy controller. To enhance the safety of the vehicle, we utilize laser range finder to perform collision prevention. Kalman filter is used commonly to perform data fusion and is considered as the benchmark for sensor data integration. This method requires a dynamic model of global position system (GPS) and IMU errors, a stochastic model of IMU errors, and a priori data of sensor systems. The accuracy of Kalman filter is influenced by stochastic modeling, outage of some sensors, and the divergence that results from approximations during any linearization process and system mismodeling. To improve the performance of Kalman filter, a fuzzy-logic based sensor fusion system is proposed. With the fused sensor information, the vehicle can recognize the desired trajectory and the obstacles in the environment. Combining the sensors’ measurement and the control strategy, a navigation and control system of an autonomous electric vehicle is accomplished in this thesis.

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