Dynamics Parameter Identification of Articulated Robot

Abstract

Dynamics parameter identification in the establishment of a multiple degree-of-freedom (DOF) robot’s dynamics model poses significant challenges. This study employs a non-symbolic numerical method to establish a dynamics model based on the Newton–Euler formula and then derives a proper dynamics model through decoupling. Initially, a minimum inertial parameter set is acquired by using QR decomposition, with the inclusion of a friction model in the robot dynamics model. Subsequently, the least squares method is employed to solve for the minimum inertial parameters, forming the basis for a comprehensive robot dynamics parameter identification system. Then, after the optimization of the genetic algorithm, the Fourier series trajectory function is used to derive the trajectory function for parameter identification. Validation of the robot’s dynamics parameter identification is performed through simulation and experimentation on a 6-DOF robot, leading to a precise identification value of the robot’s inertial parameters. Furthermore, two methods are employed to verify the inertia parameters, with analysis of experimental errors demonstrating the effectiveness of the robot dynamics parameter identification method. Overall, the effectiveness of the entire calibration system is verified by experiments, which can provide valuable insights for practical engineering applications, and a complete and effective robot dynamics parameter identification scheme for a 6-DOF robot is established and improved.