Abstract:Taking the gear assembly station of the retarder assembly line as a research background, an online modeling learning and parameter optimization method for complex robot assembly process is proposed to solve the problems of low assembly success rate and low efficiency using current offline methods. For the complex and changeable robotic assembly process of gear, the dynamic model of contact states and robot motion is established based on Gaussian process regression (GPR). A new particle

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... swarm optimization algorithm based on generative adversarial algorithm (GAPSO) is proposed for online learning to generate optimization strategy of assembly key parameters. Support vector data description (SVDD) is utilized to detect the new assembly data. The method finally realizes the online modeling and parameter optimization of the assembly process. Experiment results of the gear and spline shaft show that the GAPSO-GPR method is superior to the manual and offline methods in assembly success rate and efficiency, and can be used for online assembly of gears with different batches and specifications, which can meet the actual production demand of the retarder assembly line.