Multi-Objective Trajectory Optimization of Spray Painting Robot for Ruled Surfaces

Abstract:

To solve the problem of multiobjective tool trajectory of painting robot for ruled surface the fitting film thickness data were collected from the planar painting experiment. By using the MATLAB genetic algorithm toolbox, the β distribution was obtained. The paint thickness growth model for ruled surface was established based on the planar paint thickness distribution. The surface was discretized into a set of points, and the tool path was fitted by the cubic B spline curve. A multiobjective optimization model was built aimed to get uniform paint thickness on curved surface and high painting efficiency, and an improved fast and elitist nondominated sorting genetic algorithm(NSGAⅡ) was applied to solve the model. A serial of trajectory optimization schemes were presented. Finally, the trajectory optimization purpose on ruled surface was realized. The case result was compared to verify the effectiveness and practicability of the proposed method.