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Autonomous Path Travel Control of Mobile Robot Using Internal and External Camera Images in GPS-Denied Environments

発表形態:
原著論文
主要業績:
主要業績
単著・共著:
共著
発表年月:
2021年12月
DOI:
10.20965/jrm.2021.p1284
会議属性:
指定なし
査読:
有り
リンク情報:

日本語フィールド

著者:
Keita Yamada, Shoya Koga, Takashi Shimoda, and Kazuya Sato
題名:
Autonomous Path Travel Control of Mobile Robot Using Internal and External Camera Images in GPS-Denied Environments
発表情報:
Journal of Robotics and Mechatronics 巻: 33 号: 6 ページ: 1284-1293
キーワード:
autonomous motion control of mobile robot, monocular camera, deep learning, GPS-denied environments
概要:
抄録:
In this study, we developed a system for calculating the relative position and angle between a mobile robot and a marker using information such as the size of the marker of the internal camera of the mobile robot. Using this information, the mobile robot runs autonomously along the path given by the placement of the marker. In addition, we provide a control system that can follow a trajectory using information obtained by recognizing the mobile robot when reflected in an external camera using deep learning. The proposed method can easily achieve autonomous path travel control for mobile robots in environments where GPS cannot be received. The effectiveness of the proposed system is demonstrated under several actual experiments.

英語フィールド

Author:
Keita Yamada, Shoya Koga, Takashi Shimoda, and Kazuya Sato
Title:
Autonomous Path Travel Control of Mobile Robot Using Internal and External Camera Images in GPS-Denied Environments
Announcement information:
Journal of Robotics and Mechatronics Vol: 33 Issue: 6 Page: 1284-1293
Keyword:
autonomous motion control of mobile robot, monocular camera, deep learning, GPS-denied environments
An abstract:
In this study, we developed a system for calculating the relative position and angle between a mobile robot and a marker using information such as the size of the marker of the internal camera of the mobile robot. Using this information, the mobile robot runs autonomously along the path given by the placement of the marker. In addition, we provide a control system that can follow a trajectory using information obtained by recognizing the mobile robot when reflected in an external camera using deep learning. The proposed method can easily achieve autonomous path travel control for mobile robots in environments where GPS cannot be received. The effectiveness of the proposed system is demonstrated under several actual experiments.


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