Automated Bin-Picking with Active Vision

Abstract

In this research, an automated robotic bin-picking system employing active vision for picking up randomly distributed plumbing parts is presented. This system employs an actively-controlled single eye-in-hand system to observe structured light projected onto a set of plumbing parts in a bin. By using image processing and iterative closest point (ICP) algorithms, a single plumbing part that could possibly be taken from the bin is detected. Specifically, by projecting stationary structured light patterns onto the set of plumbing objects, the features on the surfaces of plumbing parts can be reconstructed by actively moving the eye-in-hand camera while performing visual tracking of those features. An effective 3D segmentation technique is employed to extract the point cloud of a single plumbing part that can possibly be grasped successfully. Once the object point cloud is obtained, one needs to determine the coordinate transformation from the end-effector to the selected plumbing part for grasping motion. With the point cloud matching result based on utilizing the ICP algorithm, the position and orientation of the selected plumbing part can be correctly estimated if the deviation of the object point cloud from the model point cloud is small. The control command can thus be given to the robotic manipulator to accomplish the automated bin-picking task. To effectively expand the allowed deviation of the object point cloud, an approximate pose estimation algorithm is employed before performing the ICP algorithm. The proposed approach can virtually estimate any pose of the plumbing part and has been successfully experimented with an industrial manipulator equipped with eye-in-hand single-camera vision and a LCD projector fixed in the work space demonstrating the feasibility and effectiveness. The proposed automated bin-picking system appears to be cost-effective and have great potentials in industrial factory automation applications.