本文使用數據處理算法及向量直方圖方法進行自走車的設計，目的是使自走車有未知空間避障及場景建置之功能。我們使用雷射測距儀進行未知環境偵測。透過數據處理法，第一步驟將雷射測距儀682個極座標轉換成直角座標，第二步驟將所有離散點依兩連續點之距離分割成互不相連的區塊，第三步驟將劃分好的區塊分成數個線段的點集合，最後一步透過最小平方直線擬和算法屏除雜訊及沒有嚴格的線性關係，完成線段的特徵參數，進而完成地圖建置。 並利用雷射測距儀所獲得的資料透過向量直方圖的四個階段完成未知環境的避障，第一階段將二維座標障礙物存在的可能性標示為確定性值(Certainty Value)，第二階段將轉換成一維的極性直方圖，建立以每隔α度為一區塊且計算每一區塊平滑化障礙物密度，第三四階段依所有區塊平滑化障礙密度透過所設的閥值大小判斷自走車可行走避開障礙物之方向。文末，舉出模擬實驗實行理論的方法之可行性及效果。 In this thesis we use laser range finder data processing algorithms and Vector field histogram methods applied in wheeled mobile robots. The purpose of wheeled mobile robot's function is to achieve obstacle avoidance and map building on unknown environment. We use the laser range finder to detect unknown environment. Through laser range finder data processing algorithms, the first step will convert polar coordinates into Cartesian coordinates for 682 discrete data points. The second step will separate all discrete data points into mutual non-connected region. The third step will divide the region into several point sets. The final step will to employ the least square fitting algorithm to complete the line segment character parameter. By using the above processing, we can get the global map building. Besides, VFH uses information obtained by the laser range finder to accomplish obstacle avoidance in unknown environment. The first stage will label certainty value to indicate obstacle exists within the cell area. The second stage will construct a one-dimensional polar histogram comprising angular sectors of widthα. The final two stages will judge the smoothed polar obstacle density of blocks. The wheeled mobile robot can avoid obstacles in the direction. Eventually, the simulations are given to verify to the feasibility and effectiveness of the present method.
