Research on 6DOF gripping position measurement strategy of robot based on monocular vision

The purpose of this thesis is to propose a 6DoF grasping position measurement strategy for robots based on monocular vision in response to the problems of high cost and poor stability of 6DoF grasping position measurement of target workpieces when industrial robots are grasping metal workpieces under vision system. The strategy adopts a simple structure, low-cost, easy-to-deploy monocular vision system to collect industrial sample data, and through the introduction of virtual reality technology and generative adversarial network, to realize the purpose of data enhancement for industrial metal small sample objects with surface defects and interference from the external environment, to solve the problem of insufficient data due to the difficulty of acquiring data in complex environments, and to achieve the problem of insufficient data due to the later application of monocular vision system as well. The purpose of continuously updating and expanding the sample data can be achieved by applying the monocular vision system in the later stage. At the same time, combining the deep learning-based multi-keypoint object detection technology and 2D-3D affine transformation, the method realizes the accurate measurement of the 6DoF position of the target workpiece. This method can measure the 6DoF position of industrial metal parts only by 2D plane images, and is a new method to estimate the 6DoF pose of a given object from a single RGB image using monocular vision, which possesses the ability to detect the pose of target objects with low-cost, stable, and strong antiinterference, reduces the cost of using robotic vision guidance technology in the industrial field, and provides a new way of thinking for monocular vision to be adopted by the industrial field. Provide a new idea for monocular vision to be adopted by the industrial field. Experiments show that the method has good detection accuracy for industrial small sample objects.