Optimization method of minimum jump trajectory of manipulator based on multi-objective particle swarm optimization

Abstract

The invention discloses a minimum saltus trajectory optimization method of a mechanical arm based on a multi-objective particle swarm optimization algorithm. The method comprises the following steps: firstly, generating trajectories of joints from a task start point to a finish point through path nodes by using the cubic spline function; according to the generated trajectories, determining the minimum saltus mean values of the joints corresponding to an optimized objective function in the optimized objective function; determining fitness functions of the joints according to the optimized objective function and the constraint condition of a task; and finally, obtaining a Pareto optimal solution set corresponding to the optimized objective function according to the fitness functions of the joints by means of the multi-objective particle swarm optimization algorithm, and then selecting the optimal trajectory planning scheme from the optimal solution set according to a certain principle. According to the invention, the saltus values of the joints are comprehensively considered, and the aspects of reducing the maximum value of all joint saltus in the path and uniformity and equilibrium distribution of the joint saltus are remarkably improved. By using the method, the executing error of the mechanical arm can be effectively reduced, and abrasion to the joint mechanism in the moving process is reduced.

Description

technical field [0001] The invention relates to a method for optimizing the minimum jerk trajectory of a mechanical arm based on a multi-objective particle swarm algorithm, and belongs to the technical field of robot control. Background technique [0002] With the in-depth research and technological development in the field of automatic control and robotics, manipulators have been widely used in the fields of semiconductor manufacturing, industry, medical treatment, military and space exploration. The research hotspots and difficulties of manipulators mainly focus on trajectory planning and the In terms of precise control on the basis, trajectory planning, as the basis of trajectory control, is of great significance to the operating efficiency, stability, working accuracy and energy consumption of the manipulator. [0003] Trajectory planning refers to given the starting point and the end point, according to the task requirements to find an optimal effective path connecting ...

AI Technical Summary

Problems solved by technology

[0005] The existing trajectory optimization methods for minimum jerk are mainly divided into two categories: one is single-objective optimization based on a single objective function (such as an important joint of the manipulator), such as the algorithm based on interval analysis proposed by Piazzi et al. , the trajectory is interpolated by the cubic spline function, and the maximum absolute value of the jump of the path preset by the execution time is globally minimized based on the max-min method. The global level of jerk in the task process, that is, only the jerk of a certain joint is considered, and the jerk of each joint cannot be considered comprehensively, resulting in better performance of one or several joints in the manipulator, while the performance of other joints Slightly worse, the unbalanced jerk of each joint will increase the execution error of the manipulator and cause the wear of the joint mechanism; the other is the method based on the genetic algorithm to plan the path with the smallest global jerk. Due to the complexity of the genetic algorithm principle, the calculation cost Too large, not conducive to promotion and application

Images