Dynamic Parameter Identification of a 6DOF Industrial Manipulator Considering Friction Model Dynamic parameters identification is essential for motion control of industrial robots. Precise identification is still an open issue due to friction, transmission backlash, and measurement noises of sensors. In this paper, based on a minimum set of parameters considering friction model, a unified identification approach is proposed. With this method, some essential data from robotxe2x80x99s joints motion can be sampled and calculated at the same time, thus all dynamic parameters are also acquired at one time. The 5th order Fourier series is used as the excitation trajectory. The optimal excitation trajectory is determined by minimizing the condition number, and the minimum set of parameters of the robot is obtained by combining with the least square method. Experiment results taken on a 6DOF industrial manipulator Staubli TX60 verified the correctness of the presented method.xe2x88x97

CurrentBased Direct Teaching for Industrial Manipulator In this paper, a novel human direct teaching method of industrial manipulator with large mass and friction based on internal current sensors installed in joints is proposed. Dynamic model for an industrial robot is analyzed in detail, where the gravity torque and friction torque are the main items that need to be considered. Friction torque is influenced by joint speed and temperature. A method to identify joint friction parameters rapidly is proposed, and experiments are carried out to acquire friction characteristics under different temperatures. Then, the implementation method and improvement measures of direct human teaching are introduced in consideration of robot gravity torque and friction torque of joints. Finally, validation tests are conducted to verify the feasibility of the direct teaching method. Experiment result shows that the method based on internal current sensors is capable of realizing direct teaching flexibly and easily.