In order to accurately identify the dynamic parameters of industrial robots with six degrees of freedom,a parameter identification approach based on an enhanced genetic algorithm was proposed.The dynamic model of the Newton-Euler robot was constructed,and the functional relationship between the torque and dynamic parameters of each joint was clarified.Through the improvement of the genetic algorithm,the optimal excitation trajectory of the robot was obtained,and the whole dynamic parameters of the robot were determined.The coupling effect between nodes was reduced,and the error caused by inconsistent identification of the experimental environment was avoided.Finally,the least squares method was used to calculate the kinetic parameters of the robot to solve the problem of limited recognition accuracy due to the unreasonable selection of initial values.The experimental results show that the optimal excitation trajectory obtained by the method can satisfy the constraints,reduce the optimized time and effectively improve the accuracy and effectiveness of dynamic parameter identification.