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Research on a control method of tunnel cable inspection track robot based on intelligent obstacle crossing

Autor(en):


Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Journal of Physics: Conference Series, , n. 1, v. 2390
Seite(n): 012103
DOI: 10.1088/1742-6596/2390/1/012103
Abstrakt:

In order to improve the resolution ability of the cable inspection robot in the dark environment of the tunnel and enable it to complete the autonomous obstacle avoidance control, a new intelligent obstacle crossing control method for the tunnel cable inspection track robot is studied, and the working environment of the inspection robot is explored, the Denavit-Hartenberg parameter method is used to establish the obstacle crossing model of the inspection robot, the joint variables of each robot are analyzed, and the position and posture of the end effector of the robot are obtained. The minimum value of the sum of moments is obtained by using the approximate solution method, and the winding deflection and torsion angle of the inspection robot is calculated by reverse solution. The robot obstacle-crossing motion workspace is analyzed by using the optimization algorithm, and the three-dimensional coordinate system is established. It is decomposed into the X-Y coordinate system, Y-Z coordinate system, and x-z coordinate system, so as to determine the maximum distance that the end gripper pops up on each axis, and judge whether the inspection robot can achieve obstacle crossing. According to the feedback results, intelligent control is carried out, and experiments are designed to verify the effectiveness of the control method. The results show that the voltage value of the inspection robot obtained by the control method is closer to the theoretical value, which can effectively help the robot to achieve obstacle-climbing operation accurately.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1088/1742-6596/2390/1/012103.
  • Über diese
    Datenseite
  • Reference-ID
    10777565
  • Veröffentlicht am:
    12.05.2024
  • Geändert am:
    12.05.2024
 
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