The position accuracy of an aviation drilling robot has detrimental effects on the assembly quality, mechanical strength and life of an aircraft in aircraft manufacturing. To enhance the position accuracy of the robot, a compensation method based on error similarity and error correlation is proposed. Firstly, the positional error similarity in joint space based on the kinematic model of the robot and error correlation are presented to illustrate that co-kriging can be used to estimate the positional error of the robot. Then, a cross-variogram of positional errors is introduced. Co-kriging based on the cross-variogram is applied to estimate the estimated positional errors along the x, y, and z axes and the absolute positional error. The estimated values are filtered by using a median filter method to further enhance the estimation accuracy. The estimated positional errors after filtering are sent to the robot controller for compensation. Finally, simulations and experiments are respectively performed with a simulated robot and an aviation drilling robot to verify the correctness and effectiveness of the proposed method. The experimental results show that the average absolute positional error is reduced to 0.106 mm from 1.393 mm, and the maximum absolute positional error is reduced to 0.294 mm from 1.795 mm. The simulation and experimental results indicate that the proposed method can enhance the position accuracy of an aviation drilling robot and meet the tolerance requirements in aircraft assembly.
To solve the problem of low absolute position accuracy for industrial robots in application, a positional error compensation method combing error similarity and RBF neural networks is proposed. The positional errors experience error similarity when describing the degree of error similarity developed with the error model based on a robot kinematic model. The experimental semivariogram is fitted by using a set of robot joint angles and corresponding positional errors. The bandwidth of the RBF neural network is modified by using the parameter of semivariogram. Then, an RBF neural network is constructed to estimate the positional errors of the target positions. The estimated positional errors are used to modify the target position. The modified position is given to the robot controller. To verify the proposed method, a simulation study and experiments are respectively carried out with a simulated robot and a KUKA KR210 industrial robot. The experimental results show that, after compensation, the average residual positional error is reduced by 91.99% from 1.361 mm to 0.109 mm and the maximum residual positional error is reduced by 85.41% from 1.741 mm to 0.254 mm. In addition, the proposed method can enhance the absolute position accuracy of industrial robots.
To enhance the perpendicularity accuracy in the robotic drilling system, a normal sensor calibration method is proposed to identify the errors of the zero point and laser beam direction of laser displacement sensors simultaneously. The procedure of normal adjustment of the robotic drilling system is introduced firstly. Next the measurement model of the zero point and laser beam direction on a datum plane is constructed based on the principle of the distance measurement for laser displacement sensors. An extended Kalman filter algorithm is used to identify the sensor errors. Then the surface normal measurement and attitude adjustments are presented to ensure that the axis of the drill bit coincides with the normal at drilling point. Finally, simulations are conducted to study the performance of the proposed calibration method and experiments are carried out on a robotic drilling system. The simulation and experimental results show that the perpendicularity of the hole is within 0.2°. They also demonstrate that the proposed calibration method has high accuracy of parameter identification and lays a basis for high-precision perpendicularity accuracy of drilling in the robotic drilling system.
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