Seam tracking with structured light vision has been widely applied into the robot welding. And the precise laser stripe extraction is the premise of automatic laser vision seam tracking. However, conventional laser stripe extraction methods based on image processing have the shortcomings of poor flexibility and robustness, which are easily affected by considerable image noises in the welding processing, such as arc light, smoke, and splash. To address this issue, inspired by image segmentation, with the strong contextual feature expression ability of deep convolutional neural network (DCNN), a novel image denoising method of seam images is proposed in this paper for automatic laser stripe extraction to serve intelligent robot welding applications, such as seam tracking, seam type detection, weld bead detection, etc. With the deep encoder-decoder network framework, aimed at the information loss issue by multiple convolutional and pooling operations in DCNNs, an attention dense convolutional block is proposed to extract and accumulate multi-scale feature maps. Meanwhile, a residual bi-directional ConvLSTM block (BiConvLSTM) is proposed to effectively learn multi-scale and long-range spatial contexts from local feature maps. Finally, a weighted loss function is proposed for model training to address the class unbalanced issue. Combined with the seam image set, the experimental results show that the proposed image denoising network could correctly extract the laser stripes from seam images which could demonstrate that the proposed method shows a high detection precision and good robustness against the strong image noise interference from welding process.

The hybrid-structured flexible manipulator has a complex structure and strong coupling between state variables. Meanwhile, the natural frequency of the hybrid-structured flexible manipulator varies with the motion of the telescopic joint, so it is difficult to suppress the vibration quickly. In this article, the tip state signal of the hybrid-structured flexible manipulator is decomposed into elastic vibration signal and tip vibration equilibrium position signal, and a combined control method is proposed to improve tip positioning accuracy and trajectory tracking accuracy. In the proposed combined control method, an improved nominal model-based sliding mode controller (NMBSMC) is used as the main controller to output the driving torque, and an actor-critic-based reinforcement learning controller (ACBRLC) is used as an auxiliary controller to output small compensation torque. The improved NMBSMC can be divided into a nominal model-based sliding mode robust controller and a practical model-based integral sliding mode controller. Two sliding mode controllers with different structures make full use of the mathematical model and the measured data of the actual system to improve the vibration equilibrium position tracking accuracy. The ACBRLC uses the tip elastic vibration signal and the prioritized experience replay method to obtain the small reverse compensation torque, which is superimposed with the output of the NMBSMC to suppress tip vibration and improve the positioning accuracy of the hybrid-structured flexible manipulator. Finally, several groups of experiments are designed to verify the effectiveness and robustness of the proposed combined control method.

With the rapid development of computer vision and industrial technology, the requirements of the intelligent welding robots are increasing in the real industrial production. The traditional teaching-playback mode and the off-line programming mode cannot meet the automation demand and self-adaptive ability of welding robots. In order to improve the efficiency of welding robots, this paper proposes a novel threedimensional( 3D) path extraction method of weld seams based on stereo structured light sensor. Faced with the low efficiency of the line structured light and the poor robustness of passive vision, the seam extraction based on point cloud processing algorithm is proposed which could well adapt to the weld seams with different types and different groove sizes. Meanwhile, the position information and pose information of weld seam are established to serve the 3D path teaching of welding robot.The experimental results show that the maximum path extraction error of V-type butt joint is less than 0.7mm. The proposed scheme could well serve for the 3D path teaching task before welding.

Recently, laser vision sensors are widely applied in initial point alignment and seam tracking to improve the level of intelligent welding because of good characteristics. However, since the deformation of laser stripe is unobvious at the narrow weld with 0.2mm width, these methods are not applicable for the narrow weld. Moreover, there are rare researches could achieve initial point alignment and seam tracking of narrow weld simultaneously. Therefore, an initial point alignment and seam tracking system for narrow weld is proposed in this paper. At first, a laser vision sensor with extra LED light is used to obtain laser and weld seam image. Besides, the seam feature point is extracted and three dimensional (3D) coordinates could be obtained with vision model. In addition, three controllers including decision controller, initial point alignment controller and seam tracking controller are proposed to achieve initial point alignment and seam tracking control in X and Z axis directions. What's more, feature verification, Kalman Filter (KF) and output pulse verification are designed to improve the accuracy and stability of this system. Finally, many initial point alignment and seam tracking experiments of narrow weld are conducted. Experiment results demonstrate that proposed system could well achieve initial point alignment and seam tracking of planar and curved surface narrow weld.