Automatic feature extraction and optimal path planning for robotic drawing

Abstract

Automatic robotic drawing is a fantastic demo to show the combination of intelligence and robot technique. It requires automatic feature extraction, complex robot path planning and optimization, which can make many contributions in industrial manufacturing. In this paper, we propose a hybrid method by combining local binarization with global binarization to extract features. The extracted features which will be drawn by a robot refer to some cells with complex shapes. The path of drawing should be optimized because the drawing process is time consuming. Therefore, these cells need to be partitioned into multiple cells due to its complexity. The path of drawing each cell can be generated separately, and the trajectories of drawing all cells need to be connected to form a complete trajectory. Here we propose a new solution to optimize the connection while considering the acceleration and deceleration of the robot tool. All these algorithms have been implemented and attractive result is achieved by experiment. The proposed method can be used in many industrial applications such as painting and grinding.