Vision Based Object Detection and Segrigation on UR platform

Abstract

Pick and place using collaborative robots (cobots) is a common application in industry. In a cyber-physical system, a smarter cobot with vision sensing can decrease the uncertainty in decision-making for acquiring the position of objects in a scene. In this thesis, a UR5 cobot is used as a cobot to automatically detect objects on a tabletop utilizing a monocular wrist camera. An improved statistical method is designed by formulation of a mathematical relation. A novel error reduction approach is also introduced in this research. These methods are used for estimating the position of the object (w.r.t to the manipulator coordinate system) using the object detected in the camera coordinate system. At first, correspondence between simulated world coordinates and image coordinates is used to make a relation. This is followed by an error reduction approach by capturing multiple images and gradually moving towards the target center. The object location accuracy of 95.35% was achieved using the statistical method. The error is reduced up to 2mm which is compensated since the gripper is still able to pick the object. Our proposed method can be used to accurately approach an object’s location and can be used effectively in pick-and-place applications using robotic manipulators. i.e. Fruit Sorting.