No abstract

No abstract

To improve the precision and reduce the movement uncertainty of the industrial robot, a novel hybrid optimization algorithm which combines adaptive genetic algorithm with simulated annealing algorithm is proposed in this article. First, for the sake of increasing the global exploring ability of relevant individuals, the adaptive crossover and mutation operator are used in the phase of adaptive genetic algorithm. If the population optimized by adaptive genetic algorithm is trapped in the local optimal area and simultaneously meets the transformation rule, then it is consequently optimized by simulated annealing to enhance the population diversity and hunt for a better solution so that the probability of finding the global optimal solution is greatly increased. Then, corresponding experiments based on single point repeatability are conducted to acquire data and identify the structural parameters of the industrial robot. Moreover, the single point repeatability test and length test are all implemented at the same time to verify the effectiveness of the proposed method. At last, the result reveals that the proposed method is effective to identify the real structural parameters of the industrial robot, thus enormously decreasing the single point repeatability and length deviation at the same time, which extremely increases the precision and decreases the movement uncertainty of the industrial robot.

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.