機器人定位於足球機器人中為非常重要的議題，機器人必須透過定位結果來決策策略進行比賽。在足球機器人比賽上，定位系統必須利用場地上與機器人自身的資訊來完成定位。本論文提出以全方位影像系統觀測球場資訊作為基礎，利用粒子濾波器來完成機器人於足球場上的定位設計。粒子濾波器又名為蒙地卡羅法，主要是以貝式濾波器為基礎，應用於機器人定位上，是利用多個機器人感測資訊作為條件，計算機器人位置的事後機率，進而推算機器人位置。本論文使用兩個感測器資訊作為依據，其中一個為感測機器人移動的里程計，用來預測機器人移動的位置以及方向。另一個感測器為全方位影像系統，用來觀測足球場上機器人周圍的標記線，並且可以利用一張影像快速的得到環境資訊完成定位。最後利用程式模擬的方式進行定位演算法的驗證。由於粒子濾波器定位是建立於粒子的機率分佈上，所以定位法的效果是需要以實際的環境進行多次的實驗來改善其成功性，從實驗結果可知所提方法確實可以有效的做機器人的定位。 Robot localization is an important issue for the soccer robot. The robot has to decide strategy based on its position on the soccer field. In the robot soccer competition, the robot only can use its sensor information to achieve its localization. In this thesis, an omnidirectional image-based localization method design by the particle filter is proposed to achieve the localization of robot on a soccer field. The particle filter is also known as Monte-Carlo method. The multi-sensor information is used to be conditions to calculate the posterior probability of robot’s position and estimate its position in the particle filter. In this thesis, the information obtained by two sensors (an odometer and an omnidirectional image system) is used to be conditions. The odometer is used to detect the position and orientation of the robot. The omnidirectional image system is used to observe the marking lines on the field around the robot and the field information in the captured omnidirectional image can be quickly obtained by the proposed method. Some simulation results are presented to illustrate the proposed localization method. The proposed method is built based on the probability distribution of particles, so many experiments on the real environment are needed to improve the success of the proposed localization system. From the experimental results, we can see that the proposed method can estimate the location of the robot effectively.
