Compensation Method of Robotic Arm Positioning Error Under Extreme Cold and Large Temperature Difference Based on BP Neural Network

Abstract

Aiming at the problem that the robotic arm is deformed by temperature and affects the positioning accuracy in the extremely cold and large temperature difference environment, a positioning compensation method of the robotic arm based on BP neural network is proposed. Taking the robotic arm UArm Swift Pro as the research object, the thermal deformation of the robotic arm in the extremely cold and large temperature difference environment is analyzed first, which shows that the temperature has a great influence on the positioning accuracy of the robotic arm, and the necessity of error compensation is proved; The kinematic model of the robotic arm is established by the D-H method; the positioning error compensation model based on the BP neural network algorithm is established through experiments, and the error comparison curve before and after compensation is obtained. The results demonstrate the effectiveness and feasibility of the positioning error compensation method of the robotic arm.