Automated Trajectory Generation and Deep Learning Implementation for 6-DOF Robot Operated Autonomous Propeller Surfaces Grinding

This research proposes a novel approach to the development of an automated machining system designed for grinding propeller blades. The system integrates trajectory planning for a 6-axis industrial robot, a deep learning application, and compliant grinding equipment. A methodology for robotic trajectory planning is proposed, employing both parametric and point cloud models. Initially, the 3D CAD model of the propeller is used to create a point cloud model. Subsequently, through the integration of the reference path and nearest neighbor search, the surface trajectories are generated. These trajectories can be transformed to align with the robot’s coordinate for grinding process. Furthermore, a Mask R-CNN network architecture, rooted in deep learning instance segmentation, was formulated to detect defects on the surface of each propeller blade. With the implementation of the defect detection model, the system is required to grind only the surface areas that exhibit defects.