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The weld seam detection is required for a welding robot to preplan the weld seam track before the actual welding. The purpose of this paper is to investigate this subject in natural lighting conditions.

This paper presents an efficient algorithm of weld seam detection for butt joint welding from a single image. The basic idea of the approach is to find a pair of weld seam edges in local area first. Then, starting from the two endpoints of each edge, search for the remnant edge by iterative edge detection and edge linking.

The proposed method is insensitive to the variance of the background image and can apply to most shapes of weld seams in butt joint welding.

The proposed method is designed only for butt joint welding, and it is performed before actual welding.

The system is applicable to preplan the weld trajectory for most shapes of weld seams in butt joint welding. In addition, the proposed technique may have some potential applications in the field of tailor‐welded blanks.

The proposed algorithm is based on local image processing and detects the whole weld seam from a single image without giving any initial seam, which is insensitive to the variance of the background image and has low‐computation cost.

The purpose of this paper is to design the localization and tracking algorithms for our mobile welding robot to carry out the large steel structure welding operations in industrial environment.

Extended Kalman filter, considering the bicycle-modeled robot, is adopted in the localization algorithm. The position and orientation of our mobile welding robot is estimated using the feedback of the laser sensor and the robot motion commands history. A backstepping variable is involved in the tracking algorithm. By introducing a specifically selected Lyapunov function, we proved the tracking algorithm using Barbalat Lemma, which leads the errors of estimated robot states to converge to zero.

The experiments show that the proposed localization method is fast and accurate and the tracking algorithm is robust to track straight lines, circles and other typical industrial curve shapes. The proposed localization and tracking algorithm could be used, but not limited to the mobile welding.

Localization problem which is neglected in previous research is very important in mobile welding. The proposed localization algorithm could estimate the robot states timely and accurately, and no additional sensors are needed. Furthermore, using the estimated robot states, we proposed and proved a tracking algorithm for bicycle-modeled mobile robots which could be used in welding as well as other industrial operation scenarios.