Robotic Bin Picking

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Bin picking is a common production process that involves unloading bins, containers, or boxes, one part at a time to place into a machine, conveyor, or workstation. This process can be labor intensive, repetitive, dull, and potentially hazardous to workers depending on the parts being retrieved. This is why many manufacturers have automated bin picking with industrial robots.

Automated bin picking involves integrating an industrial robot with a vision system and a robotic gripper. The FANUC Lr Mate 200id/7L is ideal for automating bin picking. Robotic vision systems are needed in order for the industrial robotic arm to be able to identify individual parts as most bin picking applications involve parts piled on top of one another. Without a vision system robotic manipulators would not be able to detect or locate each part. Either a 2D or 3D camera may be used. A 2D camera is best for less complex bin picking, while a 3D camera is better for more randomized bin picking.

A robotic gripper is necessary in order for the industrial robot arm to be able to retrieve and move parts. The exact type of gripper used for bin picking will depend upon the parts. Vacuum, suction cups, bag, and parallel grippers are all common EOATs used for bin picking.

Robotic bin picking is classified into three different types: structured, semi-structured, and random. Structured bin picking is the easiest to automate with industrial robots. Structured bin picking involves parts that are pre-positioned or stacked within the bin in an organized pattern. This makes bin picking repeatable and predictable for the ABB IRB 1200. Due to the simpler nature of structured bin picking a 2D camera can be integrated with the industrial robot.

Semi-structured bin picking is when parts are positioned within a bin in some sort of pattern. With semi-structured bin picking the pattern is not as predictable as with structured bin picking, but the robot manipulator can still detect individual parts.

Random bin picking has been the most elusive for robotic automation with many considering it to be the “holy grail” of robotic applications. While there still are some limitations and challenges to random robotic bin picking it is more possible now than even just five years ago. Random bin picking involves parts that are completely disorganized within a bin. This may include different part types, varying part orientations, and part overlapping. What makes random bin picking complex for factory robots is there is no predictable pattern and parts can be hidden underneath other parts making it difficult for robotic vision systems to detect them. The development of 3D vision has helped further the progress of random bin picking. 3D vision can help the KUKA KR6 R900 identify individual objects by creating a 3D image of the environment. Some 3D camera systems even provide extra light helping with imaging.

Automating bin picking with articulated robots reduces costs allowing for an ROI within the first two years. Productivity increases as six axis robots can average six to fifteen parts picked per minute. Workers are removed from having to lift potentially heavy or sharp parts repeatedly creating a safer work environment for them. Robots are better equipped to repeatedly lift parts and their accuracy ensures parts are not damaged or incorrectly located.

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