In fields, such as oil and gas pipelines and nuclear power, narrow-gap welding has often been used for the connection of thick and medium-thick plates. During the welding process, a lack of fusion was prone to occur due to groove size limitations, seriously affecting the service safety of large structures. The vertical oscillation arc pulsed gas metal arc welding (P-GMAW) method was adopted for narrow-gap welding in this study. The influence of the oscillation width on arc morphology, droplet transfer behavior and weld formation during narrow-gap welding was studied. Oscillation widths from 0 to 4 mm were used to weld narrow-gap grooves with a bottom width of 6 mm. The results show that, in non-oscillation arc welding, the arc always presented a bell cover shape, and the droplet transfer was in the form of one droplet per pulse, while the sidewall penetration of the weld was relatively small, making it prone to a lack of fusion. With an increase in the oscillation width, the arc gradually shifted to the sidewall. The droplet transfer mode was a mixed transfer of large and small droplets, and the sidewall penetration continued to increase, which was conducive to the fusion of the sidewall. However, when the oscillation width was wider than 3 mm, it led to the phenomenon of the arc climbing to the sidewall, and the weld was prone to porosity, undercutting and other welding defects. The oscillation width has a major impact on the stability of the welding process in vertical oscillation arc narrow-gap welding.
This paper proposes a robotic state estimation and map construction method. The traditional lidar SLAM methods are affected by sensor measurement noise, which causes the estimated trajectory to drift, especially along the altitude direction caused by lidar noise. In this paper, ground parameters in the environment are extracted to construct the ground factors to compress the trajectory estimation drifting along the altitude direction using the characteristics of constant robot pose relative to the ground. Our method uses tightly coupled lidar and inertial to obtain low-drift lidar odometry factors by factor graph optimization. The optimized lidar odometry factors are then added to a global factor graph, together with ground, loop closure, and GPS factors to obtain accurate robot state estimation and mapping after factor graph optimization. The experimental results show that our method has comparable results with advanced lidar SLAM methods, and even performs better in some complex and large-scale environments.
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