Adaptive chicken swarm optimization algorithm for identifying structural parameters of 6-DOF mechanical arm

Abstract

Abstract Accurately identifying the structural parameters of the mechanical arm can effectively increase its precision. Firstly, the kinematic model of the mechanical arm is constructed by adopting the MDH method. Secondly, based on the single-point conical hole repeatability, the objective function characterizing the single-point repeatability error is established. Thirdly, an adaptive chicken swarm optimization algorithm (mCSO) is put forward to tackle the issue of low convergence accuracy of chicken swarm optimization algorithm (CSO). Then, combined with the objective function characterizing the single-point repeatability error, the structural parameters of the mechanical arm are identified using algorithms CSO and mCSO, respectively. Finally, repeat the single-point conical hole repeatability experiment using the mechanical arm before and after identification. The experimental result reveals that the single-point repeatability error of the mechanical arm after mCSO identification is greatly reduced.