基於ZigBee智慧型環境與移動式機器人之位置感知系統設計

Abstract

位置感知機器人系統透過智慧型環境中特定物體、使用者或機器人本身之位置及這些位置所在的感測資訊提供適當的服務。ZigBee無線感測網路由於成本低廉且功耗較低，適合用來佈建智慧型環境。本論文之主旨即在於利用ZigBee建構之智慧型環境，結合移動式機器人，提出一套能主動提供服務的位置感知服務系統。為了方便建置ZigBee無線網路之位置感知系統，本論文發展一套以機率為基礎的無線訊號強度定位演算法。此演算法只需要一次的校正即可應用於不同的環境，並維持一定的準確度。另一方面，為了提供更低廉的機器人自主導航解決方案，本論文提出以單眼視覺為基礎的地平面偵測方式進行環境中物體的偵測及物體距離量測，藉此可進而達成環境結構的估測，以利機器人避障控制。本方法僅需單一攝影機即可在移動平台上偵測其障礙物之距離，不需要使用特殊攝影機或者融合其他感測器資訊。本方法之特色在於融合了反向投影以及色彩分割以穩定的分類地面/非地面區域，可以更加穩定在不同特性的地面和障礙物環境中同時偵測出靜態與動態障礙物。在實際實施應用上本方法僅限制地面為平面，不限定環境中地面及障礙物材質或顏色特性、不需要特定標號、亦不需要先對環境或障礙物特性進行學習。本方法估測結果為2D平面上物體之距離與分佈地圖，因此除了可直接用以取代傳統距離感測器，亦可根據所建構之環境地圖進行路徑規劃。本論文在整合實驗中展示此位置感知系統針對入侵者偵測和即時提供使用者服務兩個情境應用，以驗證所發展方法之有效性。

A location aware system provides location information of objects, users and mobile robots from sensor nodes deployed in the intelligent environment. The information can be used to support various intelligent behaviors of a service robot in day-to-day application scenarios. This thesis presents a probability-based approach to building a location aware system. With this approach, the inconsistencies often encountered in received signal strength indicator (RSSI) measurements are handled with a minimum calibration. By taking off-line calibration measurement of a ZigBee sensor network, the inherent problem of signal uncertainty of to-be-localized nodes can be effectively resolved. The proposed RSSI-based algorithm allows flexible deployment of sensor nodes in various environments. The proposed algorithm has been verified in several typical environments and experiments show that the method outperforms existing algorithms. The location aware system has been integrated with an autonomous mobile robot to demonstrate the an on-demand robotic intruder detection system. To provide a low-cost autonomous navigation solution, we have developed monocular vision system to estimate distances between the robot and obstacles based-on inverse perspective transformation (IPT) in image plane. A robust image processing procedure is proposed to detect and segment drivable ground area within the camera view. The proposed method integrates robust feature matching with adaptive color segmentation for plane estimation and tracking to cope with variations in illumination and camera view. After IPT and ground region segmentation, a distance measurement result is obtained similar to that of a conventional laser range finder for mobile robot obstacle avoidance and navigation. The merit of this method is that the mobile robot has the capacity of path finding and obstacle avoidance by using a single monocular camera. Practical experimental results on a wheeled mobile robot show that the proposed imaging system successfully estimates distance of objects and avoid obstacles in an indoor environment. Several interesting integrated experiments are presented in this thesis to demonstrate the effectiveness of the location aware robotic system in a home setting.