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.
For the complex test environment and uneven road surface, a sensor multisource fusion framework was used for point cloud map construction and localization. The internal parameters of IMU were first calibrated, and then the spatial parameters of LiDAR and IMU were calibrated. The calibrated multi-sensor fusion algorithm has better map building effect and stronger localization robustness, which is valuable for mobile robot localization and navigation applications, through experiments at Taian Jichuang robot test site.
In recent years, the rapid development of artificial intelligence technology, operating robots in industrial production applications are becoming more and more widespread, mechanical gripping jaw is an important part of the operating robot, the operation effect is directly related to the operating robot can complete various production operations. This paper introduces the design of robot gripping jaws for grasping special workpieces. By analyzing and summarizing the functional requirements and system components of robot gripping jaws, the main points of gripping jaw design are proposed, and a new mechanical gripping jaw structure is designed. The simulation results show that the designed mechanical gripper meets the requirements for safe gripping of special workpieces.
Wire and arc additive manufacturing is a technology to fabricate solid metal components by layer-by-layer surfacing using arc as energy carrier beam. Wire and Arc Additive Manufacturing has the advantages of high material utilization, high molding efficiency, low equipment cost and unlimited size of molded parts, etc., which has received wide attention from scholars in various countries. However, poor forming accuracy limits the development of this technology. In this paper, a quadratic regression model between weld pass size and welding voltage, welding current and welding speed was established by using the quadratic universal rotary assembly experiment method in single-layer single-pass Wire arc additive manufacturing test. The model could effectively predict weld pass size.
In view of the harsh environment of the construction site, and the difficulty of directly measuring the height and volume information of the mound unloaded in the construction site, a volume measurement method based on binocular vision is proposed. The experimental results show that the error rate of volume measurement is less than 6% and the error rate of soil height measurement is less than 3.5%, meeting the construction requirements. It can achieve the volume estimation, soil height information extraction and soil positioning of the bulldozer in the process of travel, and provide information support for the planning of the bucket movement trajectory of the bulldozer.
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