Compensation result optimization method and device, electronic equipment and storage medium

Abstract

The invention relates to the field of mechanical arm control, in particular to a compensation result optimization method and device, electronic equipment and a storage medium. The compensation result optimization method comprises the following steps: acquiring planned motion data of the mechanical arm; acquiring actual motion data of the mechanical arm; inputting the planning motion data into the trained compensation model to obtain a moment prediction correction value; acquiring a correction coefficient according to the planning motion data, the actual motion data and the moment prediction correction value; calculating a torque optimization correction value according to the torque prediction correction value and the correction coefficient; and controlling the mechanical arm to move according to the torque optimization correction value. According to the method, the compensation result can be further optimized by utilizing the optimization model, and unreasonable compensation results are reduced, so that the stability of the mechanical arm during movement is improved.

Description

technical field [0001] The invention relates to the field of robotic arm control, and in particular to a compensation result optimization method, device, electronic device and storage medium. Background technique [0002] When the manipulator actually moves, it is often affected by many factors, resulting in frictional force to cause motion errors of the manipulator. For example, the actual speed of the manipulator is different from the planned speed due to gear wear; another example is the lack of grease at the joints of the manipulator. The frictional resistance between the structures increases so that the actual position reached by the end of the robot arm differs from the target position. [0003] In the prior art, a friction compensation model is often set in the control system of the manipulator, and the model is trained by collecting a large amount of data, so that the model can consider the influence of friction when controlling the motion of the manipulator to calcu...

AI Technical Summary

Problems solved by technology

[0004] However, the compensation torque calculated by the friction compensation model is not reliable at some times. For example, when the manipulator moves close to zero speed or the acceleration is large, the compensation torque calculated by the learning model often exceeds the limit, so that the manipulator moves obviously. deviation and cause the instability of the mechanical arm movement, this problem cannot be effectively solved simply by iterative training of the friction compensation model

Images