Study on robot inverse calibration
机器人逆标定方法研究

An innovative robot calibration approach: inverse robot calibration based on neural network,was proposed in this paper,based on the analysis of traditional calibration approach.This method took the robot actual poses and corresponding joint errors as inputs and outputs of a feed-forward neural network respectively, so as to achieve the real-time joint errors in arbitrary pose through the neural network,and pose accuracy was improved only through correcting the joints angles.This calibration came down all error effects to joint errors and need not resolve the inverse kinematics model,and achieved arbitrary joint errors realtime compensation.Calibration results were compared with those obtained by traditional parametric methodologies.Simulation and experimental results show that this method is more effective compared with the traditional calibration methods, and avoids the complex modeling and parameters identification.