近年來，機器人相關產業迅速發展。除了常見的工業用機器人之外，家庭照護機器人或是救援機器人也相繼被開發出來並且運用在實際情況中。
由於自主型移動機器人機動性高的特性，所以可以應用的範圍非常廣。許多研究多以超音波感測器、雷射測距儀或是立體攝影機協助進行地圖建構的任務。本研究選擇使用雷射測距儀作為地圖建構的設備。
本研究之自主型移動機器人將雷射測距儀裝載於移動式掃描平台，藉由改變掃描平台的角度以掃描環境，並記錄所回傳的距離資訊。本研究之雷射測距儀也用於決定自主型移動機器人的移動模式以防止機器人隨機於環境中移動。除此之外，研究者結合電子羅盤與RFID 進行機器人基準點與方位之判斷使地圖資訊能夠更加精確。
本研究將雷射測距儀回傳的距離資訊進行座標轉換後，擷取機器人前方3.5公尺至4.5公尺區間的距離資訊做聯集，其聯集後的資訊延展成為該區間所擁有的障礙物情況。
本研究利用正立方體的方式來表示環境中障礙物的佔有情形，並稱之為立方地圖。立方地圖能夠有效地建構出環境中障礙物的分佈情形，並且容易進行地圖更新。
本研究提出一套立方地圖建構的系統，經由研究者設計的機器人移動機制，機器人能夠順利地在環境中行走，並且擷取環境中的距離資訊。將這些大量的距離資料經過本研究的運算方法後，可以有效的減少運算量。在後續研究中希望提供立方地圖給校園巡邏機器人作為火災偵測或巡邏使用。

In recent years, the robot industry has developed rapidly. Besides industrial robots we are familiar with, family-care robots and rescue robots are developed and applied in practical conditions.
Because autonomous robot has high mobility, it can be applied widely. In most researches, researchers commonly use ultrasonic sensor, laser range finder and stereo camera to do map building tasks. In our research, we use laser range finder to accomplish our research.
We equip the laser range finder with scan platform. By pan-tilting the scan platform, we can receive and record range data of environment. We also take the advantage of range data to decide which movement mode to avoid robot moving randomly in the environment. In addition, we combine with electronic compass and RFID to improve our map more accurately.
We transform range data into Cartesian coordinate system by trigonometric function calculation. We capture the interval of one meter between 3.5 meter and 4.5 meter which is in front of robot to be the union. The union stands for the condition of occupancy in the interval.
We use voxels to present the condition of occupancy in the environment. We call it “Voxel Map.” We can establish the condition of occupancy in the environment and update map efficiently by applying voxel map.
We develop a system to build voxel map. Under the movement rules we design, the robot can move smoothly and collect range data in the environment. By calculating a great a quantity of range data in our algorithm, we can reduce the amount of computation efficiently. In future work, we can provide voxel map information to campus patrol robot for fire detection patrol or routine patrol.

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