Construction and optimization of precision assembly model for micro-components of integrated cooperative robot based on vision guidance

Aiming at the requirement of high precision and high efficiency in the precision assembly of micro-components, this paper puts forward a method to build and optimize the precision assembly model of integrated cooperative robot based on visual guidance. The system consists of vision system, cooperative robot system, control system and human-computer interaction interface. The high-precision identification and positioning of workpieces are realized by deep learning technology, and the optimal assembly path is planned by combining dynamic constrained particle swarm optimization (PSO). At the same time, the adaptive trajectory compensation mechanism and improved Hough transform are introduced to further improve the accuracy and stability of the system. The experimental results show that the optimized system is significantly superior to the traditional method in key indexes such as mean error, standard deviation, assembly success rate and single-piece assembly time, which meets the requirements of industrial standards and shows excellent robustness and efficiency in dynamic disturbance and multi-workpiece continuous assembly scenarios. This research provides a new solution for the automation and intelligence of micro-assembly precision assembly, and has important theoretical and practical application value.