Application of genetic algorithm PSO in parameter identification of SCARA robot

Abstract

This paper first establishes the kinematic model and dynamic model of Selective Compliance Assembly Robot Arm(SCARA) robot based on Denavit-Hartenberg method and Lagrange equation. Then the model is simplified to reduce the computation, the kinetic equation is transformed into a linear form to get the observation matrix and the parameters to be identified. An incentive trajectory is designed to finish the parameter identification. A genetic algorithm and particle swarm optimization(GAPSO) is introduced to overcome the problem of premature convergence. In GAPSO algorithm crossover operator and mutation operator of genetic algorithm are embedded in the process of PSO. Through GAPSO, the accuracy of parameter identification and convergence rate have been improved. Simulation studies show that this algorithm is more reliable and efficient than the least square method and basic PSO method.