Due to the complex dynamics of the two-headed fixture of the rectangular coordinate robot,it is difficult to find the op-timal parameter combination of the two-headed fixture.Therefore,based on the rectangular coordinate robot,a parameter opti-mization of robot double-head fixture based on genetic neural network is proposed.The contact force of the double-head clamp joint in clamping the workpiece under different working conditions is analyzed,and the dynamic model of the double-head clamp is built by combining the offset constraint.Based on the dynamic model,the parameter optimization constraints are set,including the vertical and horizontal movement distance,the running speed of the end nodes,the quality of the fixture,the equivalent stress,the deformation of the lead screw,etc.Combined with relevant constraints,the objective function of double-head fixture pa-rameter optimization is constructed,and the genetic neural network is used to solve the objective function to obtain the optimal so-lution of the objective function,which is the result of double-head fixture parameter.The experimental data analysis shows that the parameters of the double-head fixture optimized by the proposed method are closer to the ideal value,and the fixture sensitivi-ty is higher,which can effectively improve its work quality.
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