It is challenging to automate the shoe upper adhesive spraying process using a robot due to the three-dimensional curved shape of the shoe upper. This paper proposes a method to automate the shoe upper adhesive spraying process with a 3-D measuring device and an industrial robot. The adhesive spraying automation process consists of the following steps, First, a transformation matrix calibration is performed to make the points measured by the 3-D measuring device and the robot end-effector points the same. Second, the shoe gauge line that connects the shoe adhesive spaying line measured by the 3D measurement device is smoothed. Lastly, the target points of the robot end-effector to quantitatively spray the adhesive are selected and the robot end-effector position/orientation to operate the robot is generated. The proposed method was validated on the test bed of a shoe upper spray system. With the method proposed in this paper, even non-robot experts can measure shoe gauge line data with a 3-D measuring device and the shoe upper adhesive spraying process can be automated without manually operating a robot.

Industrial robot manipulators require high absolute position accuracy of the end effector to perform precise and complex tasks. However, manufacturing errors cause differences between nominal and actual parameters, and errors between the expected and actual positions of the end effector, resulting in undesired lower absolute position accuracy. Accordingly, to increase the absolute position accuracy of the end effector, kinematic calibration is required to correct the nominal parameters close to the actual parameters. However, in this study, redundancy of parameters may occur from the overlapping degrees of freedom of parameters in adjacent frames, which causes the problem of unnecessarily correcting many parameters in the optimization process. Thus, to solve this problem and use only the necessary parameters, this paper focuses on the linear relationship of redundant parameters and proposes a method of automatically discriminating and removing it through the Pearson Correlation Analysis. Additionally, through simulations on the two manipulator models, we verify the accuracy of redundancy of parameters determined by the proposed method, and demonstrate consistency and efficiency by comparing the results before and after redundancy removal.