Localization And Navigation Of Biped Wall-Climbing Robots

The biped wall-climbing robot has excellent obstacle crossing and transition capabilities and the ability to task operations.It is an ideal choice for high-altitude and high-risk operations.Currently,the lack of localization and navigation capabilities in the operating environment is an important reason that restricts the industrial application of wall-climbing robots.In this paper,we used the biped wall-climbing robot that developed by the laboratory.Aiming at the localization and navigation problem of biped wall climbing robot in 3D wall environment,we proposed a monocular vision localization and trajectory deviation correction method based on build information model and Ar Uco tags.The main contents of this paper are as follows:1.Developed two softwares based on Revit for 3D wall environment automatic data extraction from building information model.First of all,the geometric information of structural elements is extracted from building information model according to the basic composition of 3D wall environment.The aruco tags database is generated by iterative closest point algorithm.The experimental results show that the software can effectively extract 3D wall environment map.2.The analysis of global positioning problem of biped wall-climbing robot.We proposed a global vision positioning method of biped wall-climbing robot based on parametric expression map and aruco tags.First,Ar Uco tags recognition and pose estimation were used for local localization of biped wall climbing robot's swinging foot.Data association is performed with the known 3D wall environment map according to the Ar Uco tags' ID,and we can calculate the global localization pose of the swinging foot.Experimental results show that the method is effective and feasible.3.The analysis of the climbing trajectory correction problem for a biped wall-climbing robot.Taking the robot's global localization pose and a theoretical planning trajectory as input,it is committed to solving the deviation value of the current actual pose and the target pose of the trajectory planning.The experimental results show that the single-step foothold deviation of the biped wall-climbing robot can be controlled within 58.1mm,and the multi-step average deviation can be reduced by 44.4%(compared with that without the proposed scheme).4.Added high-definition image wireless transmission function to the suction module of the biped wall-climbing robot,integrates core algorithm modules such as swinging foot visual localization,trajectory foothold correction and autonomous adsorption,which improves the attachment speed,positioning accuracy and reliability of the biped wall-climbing robots' swinging foot.