基於深度學習之距離估測與自動避障的戶外導航機器人

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Electrical Engineering > Electronic Thesis & Dissertation > Item 987654321/77722

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/77722

Title:	基於深度學習之距離估測與自動避障的戶外導航機器人
Authors:	賴怡靜;Lai, Yi-Jing
Contributors:	電機工程學系
Keywords:	深度學習;自動避障;單張影像深度估測;Google Map API;機器人導引;deep learning;automatic obstacle avoidance;Google Maps API;robot navigation
Date:	2018-07-27
Issue Date:	2018-08-31 14:54:03 (UTC+8)
Publisher:	國立中央大學
Abstract:	本論文主要實現一個於戶外動態環境下能夠自主行走、自動避障及導航的機器人系統。整體的系統架構使用筆記型電腦為主要的控制核心，採用深度學習、影像處理以及馬達控制等技術，由一台攝影機和手機作為路徑規劃與導引的依據。戶外導航機器人採用輪型移動型態，另行設計一個機器人平台，置放所需的硬體設備。機器人由兩個主動輪控制方向行進，另一個被動輪保持車身穩定。基於Google Maps API自行撰寫手機APP，由手機的GPS和電子羅盤感測器，規劃全域性的路線導航，並取得機器人即時的經緯度位置與方向角作為行進依據。障礙物距離偵測主要基於深度學習技術，由雙眼攝影機擷取的影像作為深度卷積神經網絡的訓練資料，其輸出結果是單張影像的視差圖，使用立體視覺幾何關係轉換距離，透過倒傳遞類神經網路針對實際距離再訓練，取得影像中每個像素的實際距離，即可讓機器人上的網路攝影機辨識障礙物距離。在辨識障礙物方面，由語意分割來分辨影像中道路與障礙物。使用模糊理論計算欲切除會造成誤判的岔路區域，最後綜合所有資訊來計算機器人行走的軌跡點，根據此軌跡，使用線性控制來控制馬達，使機器人能夠即時應變並根據規劃的路線行進，以完成自行避障前進的功能。使用者能夠自行選擇目的地，機器人會依照APP規劃的全域路線與即時的路況，使戶外導航機器人有效率地避開障礙物且自動抵達目的地。;This thesis presents an outdoor navigation robot system that can automatically go forward, avoid obstacles and navigate. The laptop is the main controller, and a camera and a smartphone are used to plan path. The system combines advanced technologies such as deep learning, image processing and motor control. The outdoor navigation robot adopts three-wheeled mobile robot. Two driving wheels are used to control the direction and the other passive wheel is used to keep the robot stability. The smartphone application utilizes the Google Maps API, the GPS and the electronic compass sensors get the global route planning. The current latitude, longitude position and direction of the robot are taken for navigation. Obstacle distance detection is mainly based on deep learning technology. The training data images are captured by the stereo camera, and output results are disparity of the single image. Then, the distances are converted by using the triangulation method of computer vision. The back propagation neural network retrains to obtain the actual distance of each pixel in the image. Therefore, the robot with the monocular camera could know the distance between obstacles and itself. Semantic segmentation is utilized to a to distinguish road and obstacles in the image. Fuzzy theory for calculating the area of the road which be cut is designed to avoid walking into the intersection. The navigation trajectory of the robot is computed by all the information. According to the trajectory, the robot can immediately follow the planned route. Finally, the robot can walk along the planned path. Users can select a destination with smartphone application. With the technologies of deep learning and image processing, the outdoor navigation robot can effectively avoid obstacles and arrive at the destination automatically.
Appears in Collections:	[Graduate Institute of Electrical Engineering] Electronic Thesis & Dissertation

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