Butt joints and angle joints exist in all types of industrial production and are often taught for mass production, while the efficiency of teaching is greatly reduced for small batches and frequent changes in the welding environment. Aiming at the problems of small-lot and other production, a 3D camera-based fast identification system for common weld seams is proposed, which is able to quickly identify the weld seam position information. In this paper, based on the 3D large field of view camera and welding robotic arm, first measure the actual accuracy of the 3D large field of view camera, and then calibrate the camera by hand and eye, the calibration error is within the usable range, and finally propose a rapid identification algorithm based on the point cloud weld to identify the weld starting point. Experimental verification shows that the position error is less than 2.79mm to meet the welding process requirements.
In order to realize the high-precision requirements of robotic multi-layer multi-pass welding and to improve the accuracy of weld bevel information recognition, a system based on laser vision for three-dimensional weld bevel recognition and reconstruction is established. Through the line structure optical sensor connected to the welding gun at the end of the welding robot, the welding seam is collected, and the noise generated by the reflections of the weldment and transmission interference is effectively reduced by threshold segmentation, adaptive selection of the region of interest, joint filtering processing, extraction of the center line and refinement of the collected data; Through the processed data still exists a small part of the existence of interference noise, affecting the subsequent recognition accuracy, the point-line projection method will be processed to obtain smooth image information; In its difference calculation, to obtain the feature point mutation information, to realize the accurate extraction of feature points; Through the transformation relationship between coordinate systems, the transformed data information is obtained, followed by computational solving to obtain the characteristic information of the 3D weld seam; The position calculation of the sensor's first frame of light bar information is carried out through the acquisition of the conversion relationship to scan at the optimal position, and the sensor and robot are controlled to acquire at the optimal parameters to obtain the highly reproducible 3D weld bevel's morphology. The experimental results show that the average error of bevel width and height after weld recognition is 0.1607mm and 0.1592mm, which meets the accuracy requirements of robot welding; Meanwhile, the reconstructed 3D weld bevel morphology has high reducibility, which provides a reference for realizing intelligent and high-precision autonomous welding.
In order to cope with the special conditions of hard soil and large soil cutting volume in autonomous slope repair operations, the existing autonomous slope repair process is improved, and a real-time monitoring and feedback system is proposed to be built for the excavator using binocular vision. The system includes the estimation of the total amount of soil cut in the slope repair project, the real-time estimation of the amount of soil cut and the real-time judgement of the slope flatness. After experimental verification, the height error of the binocular camera on the slope detection can be controlled within 3cm, the distance error between the upper edge of the soil pile and the camera is within 2cm, and the error between the estimation of the volume of soil before and after the construction and the actual measurement is within 0.02m³, which meets the accuracy requirements of the autonomous slope repair construction. It meets the accuracy requirements of the slope repair construction.
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