Method for solving robot inverse kinematics based on particle swarm optimization algorithm

Abstract

The invention relates to a method for solving robot inverse kinematics based on a particle swarm optimization algorithm. The method comprises the following steps: 1) establishing a kinematic model according to joint parameters of a robot, obtaining a kinematic positive solution formula, setting positions of target points and an attitude matrix, determining a fitness function; 2) initializing a particle population, calculating an individual fitness value of an initial population, obtaining an individual optimal value and a global optimal value; 3) updating the position and speed of the particlepopulation using dynamic inertia weight which changes with the number of iterations, calculating a new fitness value, and determining whether to update; 4) performing genetic variation operation on the particle population, calculating the fitness value; 5) determining whether a termination condition is satisfied according to the fitness value and the number of iterations. Compared with the priorart, the method solves a shortcoming that a traditional PSO algorithm easily falls into local optimal values, and can enhance local search capability and improve convergence speed and precision by reducing the number of iterations.

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 solution of inverse kinematics of tandem robot is the basis of research on robot trajectory planning and control. Only by converting the space pose into joint variables through the kinematics inverse solution can the programming control of the robot end effector according to the space pose be realized. [0003] There are numerical methods, geometric methods, and algebraic methods for the inverse solution of robot kinematics. 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), use the relationship between the six transformation matrices to obtain each axis angle θ. A commonly used numerical method is to use the radial paramet...

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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