Parameter Estimation for Industrial Robot Manipulators Using an Improved Particle Swarm Optimization Algorithm with Gaussian Mutation and Archived Elite Learning
This work presents the analysis and formulation for optimizing the dynamic model and parameter estimation of all the six joints of a 6DOF industrial robot manipulator by utilizing swarm intelligence to optimize two excitation trajectories for the first three links at the arm and the last three links at the wrist of the robot manipulator. Numerical techniques were used to reduce the observation matrix to a minimum linear combination of parameters, thereby maximizing the identifiable parameters, and the Linear Least Square method was used for parameter identification. An improved particle swarm optimization algorithm with mutation and archived elite learning was proposed for solving the dynamic optimization problem of the industrial robotic manipulator. The basic parameters of the algorithm have been optimized for robotic manipulator analysis. The proposed algorithm is computationally economical while completely dominating other Evolutionary algorithms in solving robot optimization problems. The algorithm was further used to analyze 36 benchmark functions and produced competitive results.
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