本文之目的在於探討超音波之特性，建立超音波物體辨識法則，並應用超音波於機器人，以建立未知環境地圖。在超音波模型方面，本文針對拍立得6500系列超音波測距模組，提出一個超音波Jump-U反射波模型，利用量測超音波飛行時間之延遲時間和角度的關係，經由迴歸分析求得反射波模型。利用所求得的反射波模型進行超音波飛行時間之模擬量測，實驗結果顯示，模擬量測和實際量測所得之結果非常接近，表示所得之反射波模型足以代表實際之反射波特性。另外，本文提出「單一超音波凹角辨識法則」，利用單一超波音進行360度旋轉掃描，再利用「連續區段判斷法則」，搜尋所有可能的連續區段，接著利用「反射點判斷法則」，找出連續區段的反射點，最後根據「凹角辨識法則」，找出正確的凹角。利用本實驗室所建立的環境探索機器人，進行凹角辨識量測實驗，可成功找出所有凹角與其組成平面。最後，利用探索機器人進行未知環境的量測實驗，透過無線RS-232傳輸，在PC端建立未知環境地圖。實驗結果顯示，根據所提出的「凸角辨識法則」與「凹角辨識法則」，環境中之凸角與凹角地形皆可正確建立其輪廓，所建立之環境地圖，可正確描述實際環境之狀態，未來可應用於機器人之路徑規劃與導航等。

The purpose of this study is to investigate the characteristics of an ultrasonic sensor, then to build an object differentiation algorithm, and finally build an environment map for mobile robot. For ultrasonic model, a Jump-U model of echo pattern is proposed for a Polaroid 6500 series sonar-ranging module. The model is established through a data regression method, utilizing TOF data and the relation between the delay time and the bearing angle of the sensor as input parameters. Experiments are conducted to verify the proposed model by measuring the TOF data of a plane with distances ranging from 100 ~ 200 cm. The results show a close agreement between simulation and measurements. In addition, this study proposes a corner differentiation algorithm by a single ultrasonic sensor for mobile robots. Firstly, use a single ultrasonic sensor to scan the environment in 360 degree. Secondly, use a continuous section search algorithm to find out all possible continuous sections. Thirdly, use a refection point search algorithm to locate the reflection point of any continuous section, and finally devise a corner differentiation algorithm to find the corner. A corner differentiation experiment is performed by an environmental exploration mobile robot, and the results show that all corners and their consisting planes are deduced correctly. The robot explores an unknown environment, and the measured data are transmitted immediately to a personal computer (PC) by a wireless module with RS-232 transmission protocol. The results show that according to the proposed corner and edge differentiation algorithm, all of the corners and edges in the unknown environment can be found and re-built in an environment map accurately. In the future, the map can be applied to path planning or navigation of mobile robots.

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