Precise welding of the T-joints between aircraft hyperbolic panels and stringers is required. Therefore, a method of solving inverse kinematics equations for a cooperative welding robot with multiple manipulators based on neural networks was investigated. To build an effective Denavit-Hartenberg(DH)model for this robot, sample data was obtained considering the movement ranges of the robot joints.Based on back propagation(BP) and radial basis function(RBF) neural networks, 18 joint sub-spaces were mapped to the workspaces of three manipulators. The high-dimensional and nonlinear inverse kinematics problem was transformed into a multi-input and multi-output prediction model. The results revealed that the prediction model of solving the cooperative welding robot kinematics equations was quite accurate. Moreover, compared with the BP-based model, the calculation process of the RBF-based prediction model was slower, but yielded more accurate predictions.
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