Automated 3D vision guided bin picking process for randomly located industrial parts

Abstract

Bin picking has been a research topic for years because of the challenges in image processing, robot motion planning and tool system. However, much of the existing work is not applicable to most real world bin picking problems because they are too simplistic or not robust enough for industrial use. In this paper, we developed a robust random 3D bin picking system by integrating the vision system with the robotics system. The vision system identifies the location of candidate parts, then the robot system validates if one of the candidate parts is pickable; if a part is identified as pickable, then the robot will pick up this part and place it accurately in the right location. An ABB IRB2400 robot with an IRC5 controller was chosen for picking up the parts. A 3D vision system was used to locate the parts. Experimental results demonstrated that the system can successfully pick up randomly placed parts in an industrial setting. This system provides a practical and robust solution for the industrial applications that require 3D random bin picking.

Bin-Picking for Planar Objects Based on a Deep Learning Network: A Case Study of USB Packs

“…Random bin-picking has been the focus of research for many years because of its necessity and high applicability in the industry, and it remains a core topic for improvement in the field of image processing and automated manufacturing. However, because studies have either been based on a simplistic hypothesis or have been insufficiently robust for industries with strict requirements for stability and speed, most research results have been somewhat limited [3]. It is therefore necessary to continue to develop algorithms and build a complete system to overcome challenges in industrial bin-picking.…”

“…Few studies have utilized physical information concerning the object and surface as the main feature to create a robust system that can execute random bin-picking tasks. Martinez et al [3] developed an automatic bin-picking system that provides a complete and robust solution. In their study, useful edge information was used for the recognition part, and 3D surface information was used to calculate the location of the object in the scene.…”

“…Given its crucial role in the fields of industrial manipulation, logistics classification, and household cleaning, random object picking that provides flexibility and high efficiency has attracted considerable attention [3]. However, it is difficult to find systems that can implement random bin-picking tasks for planar objects in a cluttered environment, especially thin objects.…”

Bin-Picking Solution for Randomly Placed Automotive Connectors Based on Machine Learning Techniques

“…Bin-picking solutions have been studied for a long time, and in [13], some limitations and challenges of current solutions for the industry are identified and a system for grasping sheet metal parts is proposed. In [14], a solution is proposed with an ABB IRB2400 robot with a 3D vision system for picking and placing randomly located pieces. More recently, in [15] the authors propose a CAD-based 6-DoF (degree of freedom) pose estimation pipeline for robotic random bin-picking tasks using the 3D camera.…”

Fast and Robust Bin-picking System for Densely Piled Industrial Objects

“…While above studies are verified under simulation, there are some papers give pretty results using real robot. Carlos Martinez et al prove their pick-policy and basic visual system to be robust using ABB IRB2400 robot, and give a practical test method [20]. In addition to eye-to-hand robots, eye-in-hand is also capable to deal with bin-picking problem according to Wen-Chung Chang's vision-based robotic binpicking system in which CCP approach is proposed [21].…”