A method for robot dual-axis hole assembly using learning-based genetic evolution algorithm

Abstract

The invention relates to a robot dual peg-in-hole assembling method utilizing a genetic evaluative algorithm based on learning and belongs to the technical field of robot automatic assembling. The method has the advantages that a regression prediction model is utilized to predict the fitness value of an actual robot assembling process, only a small number of optimal genes are subjected to actual tests every time, the assembling control algorithm in an actual environment is optimized on the basis of a small number of tests, and the assembling process is improved; the regression model of supportvector machines is utilized, the method has strong fitting ability to a complex nonlinear system containing noise, theoretical optical convergence can be achieved, the local minimum problem is avoided, the calculation complexity of the method depends on the number of the support vector machines instead of the dimensionality of sample spaces, and the dimensionality disaster problem can be avoidedto a certain degree.

Description

technical field [0001] The invention relates to a robot biaxial hole assembly method based on a learning genetic evolution algorithm, and belongs to the technical field of automatic assembly of robots. Background technique [0002] Under the trend of rapid development of intelligent manufacturing, robotic automatic assembly technology has a huge market demand, and has been more and more used in various assembly fields of industry. For the most basic dual-axis hole assembly task in the current industrial assembly task, the actual effect is better. Zhang Kuangen from the Robotics and Automation Research Office of the Institute of Mechanical and Electrical Engineering, Department of Mechanical Engineering, Tsinghua University wrote in his paper "Force control for a rigid dual peg-in- The method based on fuzzy force control proposed in "hole assembly" is based on the detailed analysis of the contact state of the biaxial hole fit, the analysis of the contact force models under di...

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Problems solved by technology

However, the assembly effect of this fuzzy force control method depends on the selection of its relevant control parameters, and its control parameters can only be selected based on experience, and the optimization of the control parameters of this fuzzy force control method requires experiments in the actual assembly environment , but in many cases such as large component assembly or some special actual environments cannot be used to conduct a large number of experiments to optimize parameters, coupled with noise in the real environment, the actual assembly environment becomes a complex nonlinear system that is difficult to optimize , for the existing optimization algorithm, due to the inability to conduct a large number of experiments to obtain its experimental results, it is impossible to optimize the actual robot assembly process

Although the genetic evolution algorithm has been proven to be an optimization algorithm with high robustness for the real noise environment, it is not possible to conduct frequent experiments in the actual environment to verify the specific value of the fitness function of the parameters to be selected.

Therefore, the fact that a large number of experiments cannot be provided in the actual robot assembly environment is the main problem that limits the use of existing optimization algorithms such as genetic evolution algorithms in actual environments.

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