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Optimization Design and Flexible Detection Method of Wall-Climbing Robot System with Multiple Sensors Integration for Magnetic Particle Testing.
Zhang, Xiaojun; Zhang, Xuan; Zhang, Minglu; Sun, Lingyu; Li, Manhong.
Afiliación
  • Zhang X; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China.
  • Zhang X; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China.
  • Zhang M; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China.
  • Sun L; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China.
  • Li M; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China.
Sensors (Basel) ; 20(16)2020 Aug 15.
Article en En | MEDLINE | ID: mdl-32824186
ABSTRACT
Weld detection is vital to the quality of ship construction and navigation safety, and numerous detection robots have been developed and widely applied. Focusing on the current bottleneck of robot safety, efficiency, and intelligent detection, this paper developed a wall-climbing robot that integrates multiple sensors and uses fluorescent magnetic powder for nondestructive testing. We designed a moving mechanism that can safely move on a curved surface and a serial-parallel hybrid flexible detection mechanism that incorporates a force sensor to solve the robot's safe adsorption and a flexible detection of the curved surface to complete the flaw detection operation. We optimized the system structure and improved the overall performance of the robot by establishing a unified mechanical model for different operating conditions. Based on the collected sensor information, a multi-degree of freedom component collaborative flexible detection method with a standard detecting process was developed to complete efficient, high-quality detection. Results showed that the developed wall-climbing robot can move safely and steadily on the complex facade and can complete the flaw detection of wall welds.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2020 Tipo del documento: Article País de afiliación: China