源於傳統覆蓋控制方法之缺陷與環境模型估測、無線感測器與致動器網路以及多機器人系統間整合研究之缺乏，本論文基於無線感測器網路之資訊以徑向基函數網路建構環境中的重要性分佈，再配合最佳化方法中被廣泛應用的梯度法與Lyapunov穩定性定理，提出一包含機器人環境覆蓋與致動器調節控制方法之環境智慧估測與調節系統。
環境中感測資訊的分佈稱為感測密度函數，而無線感測器與致動器網路中感測器與致動器分別用於蒐集環境資訊與調節環境，其中有限距離非等向致動器裝置於移動機器人上能對特定範圍進行調節，而感測器方面則分為以下兩種，機器人上配置之有限距離非等向感測器稱為高階感測器，能提供特定範圍內之感測資訊；靜置於環境中之低階感測器則能提供特定位置之感測資訊。基於此兩種感測器所蒐集之資訊，以監督式學習訓練建構估測密度函數之徑向基函數網路中基底函數的中心位置、權重以及標準差，藉此達到估測與實際之最大化相似性。
於獲得環境模型後，利用覆蓋控制的方法分別定義使移動機器人上高階感測器位置與轉向角達到最佳分佈的最佳化問題，同時考慮進環境中障礙物資訊後，再以梯度法設計控制器，找出機器人移動時最有效率的移動路徑，並使高階感測器達到資訊量最大化。在機器人不斷往重要性移動的過程中，以Lyapunov穩定性定理設計之調節控制器使機器人上有限距離非等向致動器將環境調節至目標分佈。最後，本論文以理論分析、數值模擬以及實驗驗證所提出系統之最佳化與穩定性。

This thesis proposes a control framework that integrates Wireless Sensor and Actuator Networks (WSANs) with multi-robot control systems. There are two kinds of sensor under WSANs, including pre-deployed stationary sensors only providing the information of their own positions and limited-range anisotropic sensors embedded on mobile agents providing the information of specific regions. According to the sensory information gathered from stationary and embedded sensors, the environment model is constructed by Radial Basis Function (RBF) Networks to approximate the real condition, and supervised learning is used to train parameters of the network. With the estimated environmental distribution, gradient method coverage controllers for robot positions and orientations are designed and applied to drive mobile robots to the optimal sensing configuration. Furthermore, based on Lyapunov stability theorem, a regulating controller for embedded actuators is proposed to simultaneously regulate the designated region to the desired distribution. Theoretical analysis, numerical simulations, and experiment are given to demonstrate the performance of the proposed method.

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