研究生: |
孫煜翔 Sun, Yu-Hsiang |
---|---|
論文名稱: |
人形機器人騎乘電動機車時之視覺里程計 Visual Odometry for a Humanoid Robot Riding an E-Scooter |
指導教授: |
包傑奇
Baltes, Jacky |
口試委員: |
杜國洋
Tu, Kuo-Yang 王偉彥 Wang, Wei-Yen 包傑奇 Baltes, Jacky |
口試日期: | 2023/03/31 |
學位類別: |
碩士 Master |
系所名稱: |
電機工程學系 Department of Electrical Engineering |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 英文 |
論文頁數: | 42 |
中文關鍵詞: | 人形機器人 、兩輪車輛 、深度學習 |
英文關鍵詞: | Humanoid Robots, Two-wheeled Vehicles, Deep Learning, ORB SLAM3 |
研究方法: | 實驗設計法 、 比較研究 |
DOI URL: | http://doi.org/10.6345/NTNU202300396 |
論文種類: | 學術論文 |
相關次數: | 點閱:329 下載:10 |
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In recent years, deep learning has been used to develop autonomous driving on four wheels, with the goal of reaching autonomy. Our laboratory is dedicated to developing intelligent humanoid robots and is willing to take on new research fields - autonomous driving of an unmodified humanoid robot on two wheels. Taiwan has a well-established scooter industry, but few researchers have studied the behavior of autonomous scooter driving. Our ambitious plan is to use a large humanoid robot called Thormang3 to develop an autonomous scooter system and attempt to pass the driving test for the Taiwanese scooter license. To achieve self-balancing in a real environment, the current speed detection and control of the scooter are crucial issues. The main contribution of this paper is a speed controller for a two-wheeled electric scooter, using a large humanoid robot to achieve constant speed driving in a real-world environment. Currently, we are using three main methods to obtain the current speed of the scooter: Yolo dashboard speed detection, ORB SLAM3, and a hybrid method.
We will evaluate the accuracy of these methods in an outdoor environment and discuss their advantages and limitations. By using the linearity of the speedometer, we can obtain a rough velocity estimate for the robot using Yolov4 object detection. During the robot's navigation, the rough velocity estimate provides a relatively accurate measure of the real-world scale factor necessary for ORB SLAM3, which helps overcome the inherent disadvantages of monocular cameras and improve real-time velocity extraction.
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