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.
