Accuracy compensation of a spraying robot based on RBF neural network

Abstract

In order to realize automatic aircrafts spraying, it is necessary to improve the positioning accuracy of spraying robot, which has received much attention with the increasing demands for robotic automation. This paper studies accuracy compensation of a spraying robot with six degrees of freedom (DOF). First, Denavit-Hartenberg (D-H) matrix method is employed to establish kinematics analysis and get the ideal position of robot end. Secondly, Radial Basis Function (RBF) neural network algorithm is used to improve the robot absolute positioning accuracy. Finally, simulations based on MATLAB are made to prove that calculation and analysis are correct. The systematic and parametric analysis about the spraying robot in this paper provides a foundation for the design, control and trajectory planning in future.