A development of visual-feedback automatic control for robotic manipulator

• Item type: Symposium(1)
• 公開日: 2021-03-15
• タイトル: A development of visual-feedback automatic control for robotic manipulator
• タイトル / 言語: en
• タイトル: A development of visual-feedback automatic control for robotic manipulator
• 言語: eng
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_5794
• 資源タイプ: conference paper
• 著者所属: Department of Electrical Engineering, Prince of Songkla University
• 著者所属: Department of Electrical Engineering, Prince of Songkla University
• 著者所属(英): en
• 著者所属(英): Department of Electrical Engineering, Prince of Songkla University
• 著者所属(英): en
• 著者所属(英): Department of Electrical Engineering, Prince of Songkla University
• 著者名: Hiroshi, Nakahara; Kittikhun, Thongpull
• 著者名(英): Hiroshi, Nakahara; Kittikhun, Thongpull
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In this work, an automatic robotic manipulator control based on visual information for object tracking is presented. The tracking process employ Recurrent Neural Network technique to predict the location of target object which is used to calculate movement of the robot. We also propose an object analysis based on image processing technique for object width estimation. We implemented the proposed system with an actual robotic manipulator with a gripper for experiments. The experiments shown that object size estimation method results the maximum accuracy at 1.13% and the tacking process can effectively keep the target object in the position suitable to perform grasp.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In this work, an automatic robotic manipulator control based on visual information for object tracking is presented. The tracking process employ Recurrent Neural Network technique to predict the location of target object which is used to calculate movement of the robot. We also propose an object analysis based on image processing technique for object width estimation. We implemented the proposed system with an actual robotic manipulator with a gripper for experiments. The experiments shown that object size estimation method results the maximum accuracy at 1.13% and the tacking process can effectively keep the target object in the position suitable to perform grasp.
• 書誌情報: Proceedings of Asia Pacific Conference on Robot IoT System Development and Platform; 巻 2020, p. 67-68, 発行日 2021-03-15
• 出版者 / 言語: ja
• 出版者: 情報処理学会