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A Docking Mechanism Based on a Stewart Platform and Its Tracking Control Based on Information Fusion Algorithm.
Zhan, Gan; Niu, Shaohua; Zhang, Wencai; Zhou, Xiaoyan; Pang, Jinhui; Li, Yingchao; Zhan, Jigang.
Afiliação
  • Zhan G; School of Mechatronical Engineering of Beijing Institute of Technology, Beijing 100081, China.
  • Niu S; School of Mechatronical Engineering of Beijing Institute of Technology, Beijing 100081, China.
  • Zhang W; School of Mechatronical Engineering of Beijing Institute of Technology, Beijing 100081, China.
  • Zhou X; School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China.
  • Pang J; School of Computer Science and Technology of Beijing Institute of Technology, Beijing 100081, China.
  • Li Y; Beijing Zhongxin Hengyuan Technology Co., Ltd., Beijing 100081, China.
  • Zhan J; Beijing Zhongxin Hengyuan Technology Co., Ltd., Beijing 100081, China.
Sensors (Basel) ; 22(3)2022 Jan 20.
Article em En | MEDLINE | ID: mdl-35161517
ABSTRACT
Aiming at the problem of unmanned reconfiguration and docking of ground vehicles under complex working conditions, we designed a piece of docking equipment composed of an active mechanism based on a six-degree-of-freedom platform and a locking mechanism with multi-sensors. Through the proposed control method based on laser and image sensor information fusion calculation, the six-dimensional posture information of the mechanism during the docking process is captured in real time so as to achieve high-precision docking. Finally, the effectiveness of the method and the feasibility of the 6-DOF platform are verified by the established model. The results show that the mechanism can meet the requirements of smooth docking of ground unmanned vehicles.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article