A solution method of robot inverse kinematics based on particle swarm optimization algorithm

Abstract

The invention relates to a robot inverse kinematics solution method based on a particle swarm optimization algorithm, comprising the following steps: 1) establishing a kinematics model according to the joint parameters of the robot, obtaining a kinematics positive solution formula, setting the position and attitude matrix of the target point, Determine the fitness function; 2) Initialize the particle population, calculate the individual fitness value of the initial population, and obtain the individual optimal value and the global optimal value; 3) Use the dynamic inertia weight that changes with the number of iterations to update the position and speed, calculate the new fitness value, and judge whether to update; 4) perform genetic mutation operation on the particle population, and calculate the fitness value; 5) judge whether the termination condition is met according to the fitness value and the number of iterations. Compared with the prior art, the present invention solves the shortcoming that the traditional PSO algorithm is easy to fall into the local optimal value, and can enhance the local search ability by reducing the number of iterations to improve the convergence speed and accuracy.

Description

technical field [0001] The invention relates to a method for solving the inverse kinematics of a robot, in particular to a method for solving the inverse kinematics of a robot based on a particle swarm optimization algorithm. Background technique [0002] The inverse kinematics solution of tandem robots is the basis for the research of robot trajectory planning and control. Only by converting the spatial pose into joint variables through the inverse kinematics solution, can the robot end effector be programmed and controlled according to the spatial pose. [0003] The inverse solutions of robot kinematics include numerical method, geometric method and algebraic method, etc. After satisfying the Pieper criterion (that is, the three adjacent joint axes of the robot intersect at one point or the three axes are parallel), each of the six transformation matrices is used to obtain each axis angle θ. A commonly used numerical method is to use the radial parameter a in the D-H par...

AI Technical Summary

Problems solved by technology

First, let the PSO be initialized as a group of random particles (random solutions). In each iteration, the particles update themselves by tracking two "extreme values". The optimal solution is selected through continuous iteration, and its inverse solution is determined according to its fitness value. , but the existing particle swarm optimization algorithm is easy to fall into the local optimal solution

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