隨著人們對室內環境的重視，相關環境調節裝置的使用也愈來愈普及，然而其於運作上仍然有諸多限制，如只能在定點進行調節任務、或無法提供足夠的資訊予使用者等，皆會讓使用者無法有效率地使用這些裝置。為了讓環境調節裝置擁有更好的調節效率，本論文整合設置於室內空間之環境感測模組與裝設有環境調節裝置之移動機器平台，提出一個多移動機器人之室內環境調節系統，使調節裝置可以依據環境感測模組所感測之環境資訊判斷室內區域的環境狀況，並移動至需要進行環境調節之區域進行調節環境任務。
依據環境感測模組所感測之環境資訊，此系統將會估測各室內區域的環境狀況，並計算適當的調節位置給予環境調節裝置。本論文是以實驗來驗證環境調節裝置之調節位置對於環境調節效率的影響，並從實驗結果中歸納出環境調節裝置於不同實驗環境狀況下之最佳位置控制策略。於移動機器人方面，本研究是使用Simultaneous Localization and Mapping (SLAM)法建置室內平面地圖、Adaptive Monte Carlo Localization (AMCL)法定位機器人之位置、Probabilistic Road Map (PRM)法規劃移動路徑、以及Light Detection and Ranging(LIDAR) 感測器進行機器人避障控制。藉由前述方法與感測器，本論文制定了移動機器人之移動控制方法，使其在接收到調節環境之任務後，可以導航至任務的目標位置，並於移動途中避免障礙物與多機器人間的碰撞。
本論文所提出之多移動機器人室內環境調節系統除了於Gazebo的3D模擬環境進行模擬以外，亦有架設實驗場地以進行此系統的運作測試，記錄單移動機器人與多移動機器人的移動軌跡及調節成果。

As the living standard raises, humans start to pay attention to the quality of their life, especially for indoor environmental quality (IEQ). In order to improve
the indoor environment, it is a good choice to use the apparatus like humidifier, air purifier, or air conditioner. However, most of these apparatus can only improve one specific space, can only measure the environmental condition nearby them, and unable to know the effect of their position to the efficiency of environmental regulation . This thesis proposes a mobile robot system integrated with wireless sensors mounted in indoor environment. The system consists of two parts, the sensors separated in the indoor space and the autonomous robots that have the function to improve the environment. Through the sensors, the system evaluates the environmental condition of different space, decides which space needs to be improved first, and finds the best position for the robots to execute regulating environmental condition. For the robots, the system builds indoor maps by Simultaneous Localization and Mapping (SLAM), locates robots by Adaptive MonteCarlo Localization (AMCL), and plans routes by Probabilistic Road Map (PRM). Within navigation, from the control methods proposed in this thesis, system can navigate robots to the target position with obstacle avoidance and collision avoidance. Finally, several experiments are conducted to show the feasibility of the system.

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