Robot Time Trajectory Optimization Based on Improved Genetic Algorithm
In order to solve the problem of long motion time and easy to fall into local optimal solution in the trajectory planning process of traditional industrial robots,a trajectory optimization algorithm for 6R ro-botic arm based on improved adaptive genetic algorithm was proposed.By adding the improved adaptive adjustment mechanism,the crossover probability and mutation probability can be changed adaptively.First-ly,a 6-DOF manipulator model is established,and the improved D-H parameter method is used to obtain the parameters of the robot connecting rod.Secondly,the trajectory planning is carried out by 4-1-4 polyno-mial interpolation method.With the running time as the optimization goal,the motion trajectory is optimized by improved adaptive genetic algorithm and ant colony algorithm.Finally,the objective function is used to solve the kinematic constraint problem.The MATLAB simulation experiment verifies that compared with the traditional genetic algorithm,the running time of the trajectory is reduced from 12.23 s to 9.05 s,the o-verall running time is shortened by 3.18 s,and the efficiency after optimization is increased by nearly 26%.The fitness is increased by 1.73,which proves that the algorithm can effectively accelerate the run-ning time of the trajectory and improve the working efficiency of the robot arm.
genetic algorithmant colony algorithmmodified D-H methodtrajectory planningdegree of adaptability
万方数据
维普
