隨著科技的日新月異，人們將許多的工作交給了機器人，於是配置各式各樣感測器機器人的研究也跟著蓬勃發展。受限於氣體感測器開始發展的時間較其他感測器晚，直到近年來機器嗅覺的相關發展迅速，基於氣體感測器的機器人氣體追蹤系統也隨之起步，氣體追蹤演算法遂成為一個新的研究主題。
本研究為以輪型機器人系統為基礎，進行氣體源追蹤演算法之開發。機器人配置五組氣體感測器及風感測器，藉此能夠觀測周遭環境。在過去的研究中發現，氣體濃度值固然是氣體追蹤時最重要的資訊，但風的資訊往往也佔了很重要的角色，前人演算法引入了風向的資訊後也大幅增進演算法效率與成功率。同時，透過觀察水中與飛行動物氣體追蹤軌跡與行為模式的啟發，我們利用了機器人周遭的氣體濃度、風向、風速設計了一種新的演算法，演算法的行為模式以水中與空中動物的氣體追蹤軌跡方向為參考。為了能有效比較不同演算法間的效率與差異，本研究將演算法在實體機器人上實踐，並建立了一套可控制的半開放實驗環境與觀測機制。
本研究所提出之演算法使用了五組氣體感測器量測機器人前方氣體濃度分佈的趨勢，再加入由風向與風速產生的影響，算出一組包含方向與大小的追蹤趨勢參考向量，機器人再依據這組向量，透過前進馬達的速差與第三輪速度的改變，在機器人前進的同時調整機器人的方向，以進行氣體源追蹤任務。在完整展示系統與演算法後，我們呈現了實體機器人在實驗環境下不同演算法間的比較結果，除了本演算法外，還包括了Casting、Surge-spiral與Surge-cast演算法。本研究所提出之氣體追蹤演算法與前人演算法相比，在有高成功率的同時，也能維持較高的效率，能確實有效進行氣體源的追蹤並找出氣體源位置。

In this research we present a new system and a new algorithm to achieve odor source tracking task, based upon mobile robot with 5 gas sensors.
Research on odor source localization based on mobile robots suggest that although the gas sensor is the most essential part in tracking algorithm, information from wind sensor also take a critical role. Former approaches to this work brought wind direction into algorithms and significantly improved the efficiency and success rate in laminar flow environment.
Inspired by this observation, we build a mobile robot system with a new odor tracking algorithm. The metal oxide (MOX) semiconductor sensors, wind sensor, distance sensors, and microcontroller are used to implement the task. By adding wind speed as a factor, we propose a new algorithm based on odor concentration of 5 MOX sensors and wind information to achieve the odor following task. In order to evaluate the effect of our algorithm, we compare it with several former algorithms using the robot in the controlled environment.
After a complete derivation of our system and algorithm, we present the results of several experiments that compare the success rate, distance overhead and effective capacity of searching and tracking of proposed methods with other leading approaches. The experimental results showed the improvement in tracking efficiency and the direction towards the odor source is given appropriately with our system and algorithm.

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