Mechanical arm track correction method and system, computer and readable storage medium

Abstract

The invention provides a mechanical arm track correction method and system, a computer and a readable storage medium. The method comprises the steps that a theoretical motion path which an actuator will pass through is simulated in advance through a path planning algorithm; the theoretical motion trail of each joint between every two path points is calculated, and the theoretical linear acceleration and the theoretical rotation angle generated by the actuator in the theoretical motion trails are obtained; enabling the servo controller to control the actuator to move according to each path point in sequence; respectively acquiring actual linear acceleration and actual rotation angle generated when the actuator passes through each path point through an inertial sensor, and calculating an error value between an actual value and a theoretical value; and the execution path of the actuator is re-planned according to the error value, so that the actuator moves according to the execution path. In this way, the error value of the mechanical arm can be obtained in real time in the movement process of the mechanical arm, so that the execution track of the mechanical arm is corrected in real time, and wide development prospects are achieved.

Description

technical field [0001] The present invention relates to the technical field of robotic arms, and in particular, to a method, system, computer and readable storage medium for correcting the trajectory of a robotic arm. Background technique [0002] With the advancement of science and technology and the rapid development of productivity, industrial robotic arms have been widely used in current industrial production. The control system of the existing manipulator is a nonlinear open-loop control system, which will be affected by parameter uncertainty and nonlinear interference during the actual control of the manipulator, which will lead to the decrease of the trajectory accuracy of the manipulator. In order to improve the trajectory accuracy of the manipulator in the existing technology, on the one hand, the self-learning ability of the multi-layer neural network and the robust sliding mode technology are used to weaken the influence of the model uncertainty in the control sys...

AI Technical Summary

Problems solved by technology

[0003] However, the above methods all need to modify the parameters of the manipulator model before the movement of the manipulator, and the manipulator model

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