Neng Wang

The large-format, long-distance welding of heat exchanger plates is widely used in shipbuilding, oil and gas, power and metallurgical equipment, rail transportation, and other fields. To address issues such as low automation and information silos in actual welding production, this paper proposes an intelligent weld seam identification and trajectory generation method, accurately achieving coordinate generation for large-format, long-distance heat exchanger plate welding. The method investigates a camera calibration model based on coordinate transformation, preprocesses collected weld seam images, develops an edge approximation algorithm using median filters for denoising, and proposes a two-stage fusion strategy of “deep learning localization + optimized operator refinement” for edge intelligent identification. This strategy utilizes deep learning object detection for the fast and robust coarse localization of weld regions, combined with optimized operators for high-precision, efficient pixel-level edge extraction. Finally, a weld trajectory coordinate generation program based on the Hough transform algorithm is developed, enabling the rapid automatic welding of plates by welding robots. Experiments demonstrate the accurate identification of heat exchanger plate welds with an error of only 0.33%, meeting welding requirements. The method shows the fast integration of identification and welding information: overall welding efficiency improved by over 100%, and we achieved strong real-time performance, compatibility, and low hardware requirements.

The joints running trajectory of a robot directly affects it’s working efficiency, stationarity and working quality. To solve the problems of slow convergence speed and weak global search ability in the current commonly used joint trajectory optimization algorithms, a joint trajectory planning method based on slime mould whale optimization algorithm (SMWOA) was researched, which could obtain the joint trajectory within a short time and with low energy consumption. On the basis of analyses of the whale optimization algorithm (WOA) and slime mould algorithm (SMA) in detail, the SMWOA was proposed by combining the two methods. By adjusting dynamic parameters and introducing dynamic weights, the proposed SMWOA increased the probability of obtaining the global optimal solution. The optimized results of 15 benchmark functions verified that the optimization accuracy of the SMWOA is clearly better than that of other classical algorithms. An experiment was carried out in which this algorithm was applied to joint trajectory optimization. Taking 6-DOF UR5 manipulator as an example, the results show that the optimized running time of the joints is reduced by 37.674% compared with that before optimization. The efficiency of robot joint motion was improved. This study provides a theoretical basis for the optimization of other engineering fields.

In order to obtain the optimal perspectives of the recognition target, this paper combines the motion path of the manipulator arm and camera. A path planning method to find the optimal perspectives based on an A* algorithm is proposed. The quality of perspectives is represented by means of multi-view recognition. A binary multi-view 2D kernel principal component analysis network (BM2DKPCANet) is built to extract features. The multi-view angles classifier based on BM2DKPCANet + Softmax is established, which outputs category posterior probability to represent the perspective recognition performance function. The path planning problem is transformed into a multi-objective optimization problem by taking the optimal view recognition and the shortest path distance as the objective functions. In order to reduce the calculation, the multi-objective optimization problem is transformed into a single optimization problem by fusing the objective functions based on the established perspective observation directed graph model. An A* algorithm is used to solve the single source shortest path problem of the fused directed graph. The path planning experiments with different numbers of view angles and different starting points demonstrate that the method can guide the camera to reach the viewpoint with higher recognition accuracy and complete the optimal observation path planning.