Aiming at the current situation and existing problems of the sanitary ceramics industry, the paper presents the R&D of a set of multi - robot cooperation in the spraying glazing system. Firstly, the paper introduces the requirement of spraying glazing and puts forward the technics of spraying grazing robot. Then the paper introduces the system solution of spraying glazing robot, the working mode of the cooperative multi-robot, and the method of robot trajectory planning. Finally, according to the real experimentation of established spraying glazing robot system, the paper tested and verified the performance of the system. This system plays a positive demonstration role in the sanitary ceramics industry.

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AHA (artificial hummingbird algorithm) is a meta-heuristic optimization algorithm that simulates the unique flight and feeding patterns of hummingbirds. By combining the excellent strategy of the artificial hummingbird algorithm with the framework of the multi-objective optimization algorithm, the multi-objective artificial hummingbird algorithm is created, improved, and further enhanced. Therefore, the research on vision-guided optimization and grasping control of multi-objective robots based on an enhanced artificial hummingbird algorithm has significant academic significance and vast potential for application.

Currently, new energy industries such as electric vehicles and energy storage batteries are experiencing rapid growth throughout the world. As a recognized ideal energy storage element, lithium batteries have also attracted considerable interest. To improve image contrast, the industry employs image preprocessing algorithms. Due to the high requirement of image consistency in the template matching method, which is difficult to meet in practice, and the large apparent difference between defects, it is challenging to design features. Therefore, the framework for defect detection is based on a method of deep learning with strong feature expression capability. In light of the severe imbalance in the number of samples in defect detection, data augmentation and generation methods are used to simulate real samples in order to improve the training effect of deep neural networks and alleviate the burden of data annotation to some extent.