本論文係探討居家服務機器人同步定位與地圖建立以及行為策略之設計與實現。本論文開發出一個新的居家服務機器人平台，包括機器人運動控制、同步定位與地圖建立、基於已知地圖之自我定位、以及機器人自主導航與行為策略。SLAM模組使機器人可以在陌生環境下學習與認知環境資訊。此模組主要截取雷射之距離資訊與里程計資訊來學習認知陌生環境。本論文藉由結合傳統Rao-Blackwellised 粒子濾波器與KLD-sampling方法來完成實現SLAM模組。自主定位係使用蒙地卡羅法與晶格地圖來記錄定位的資訊。行為策劃部份，則經由檢查雷射距離資訊映射到地圖的晶格是否落在門的區域來偵測門。在全域路徑規劃部份，本論文主要採用A*演算法，並改進ND導航演算法以達成避障的功能。利用融合上述功能所提供之資訊來達成機器人的行為策略。最後，由實驗結果來驗證所設計之機器人系統的可行性與效益。

This thesis mainly concerns about the development of the simultaneous localization and mapping (SLAM) and behavior strategy for the home service robot. We develop a new platform for home service robot, involving many topics such as motion control, SLAM algorithm, self-localization based on known map, safe navigation, and behavior strategy. The main function of the SLAM module is to learn the map for the unknown environment. The information of the SLAM module consists of the distance received by laser scanner and the movement of the robot computed by odometer. In SLAM module, an approach for learning environment is developed by combing the KLD-sampling method and the conventional Rao-Blackwellised particle filter method. The localization based on known map of the developed robot is realized via the Monte Carlo Localization (MCL) and the belief of the robot in its position is represented by a position probability grid map. In behavior strategy, a method for door detection by checking the end points fall into the region of the door is proposed. For navigation part, we adopt the A* algorithm for global path planning. In addition, we improve the Nearness Diagram (ND) method for local obstacle avoidance. The behavior strategy for the home service robot is also examined. Finally, the experiment results demonstrate the capability of SLAM and strategy system, and the efficiency and validity in the home service robot applications.

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