For more than two decades, machine vision practitioners have been predicting the commercial emergence of robotic random bin-picking—the ability of vision-guided robot arms to locate and pick individual parts from a jumble of parts piled haphazardly in a bin or container.

Highly flexible, random bin-picking systems would produce major savings for manufacturers, the early proponents declared. Human workers would no longer be required to unload incoming parts bins shipped by suppliers. And on machining and production lines, randomized parts piled in bins could replace expensive fixturing, tooling and component feeders used for part orientation.

Unfortunately, the widespread exuberance for the technology in the early 1980s gave way to hard reality later on. The “bin-picking problem” proved more difficult than anticipated. Bin-picking systems developed in the laboratories, it turned out, didn’t translate well into real-world factory applications. “The industry found out that this wasn’t so easy. You had things like partial occlusion with overlapping parts, and lighting variations that really stymied the progress of bin-picking,” notes Adil Shafi, president of Shafi Inc., a Brighton, Mich.-based software solutions provider that specializes in vision-guided robotics. A further complication was that computers of the time tended to choke on the massive amounts of processing required to successfully recognize parts piled randomly in a bin, and to calculate their 3D position and orientation for picking.

Eventually, less taxing 2D vision robot-guidance tasks—such as picking singulated parts from a moving conveyor —became relatively common. But widespread random bin-picking applications never materialized, and to this day remain a challenge. “It’s been a desired, but very elusive and unreachable goal for a long time,” Shafi observes. “A lot of people have referred to bin-picking as the Holy Grail of robotic material handling.”

Now, however, there are growing signs that vision-guided robot bin-picking may be finally moving closer to reality. Robot vendors including Fanuc, Motoman and Staubli have recently demonstrated bin-picking systems at trade shows. At least one North American systems integrator is planning this year to offer “semi-random” and random bin-picking as “standard product” technology for certain types of parts. And a number of automotive industry end-users are experimenting with bin-picking technology, with some early applications already in production.

At a TRW Automotive plant in Woodstock, Ontario, Canada, for example, Manufacturing Engineer Todd Denstedt says that two bin-picking systems designed to unload brake rotor castings are currently working well, and are scheduled to go into production around mid-year. Meanwhile, a Toyota Motor Manufacturing plant in Buffalo, W.Va., is already using five robotic bin-picking systems on its engine part machining lines. Those systems rely on ABB robots equipped with 3D vision technology supplied by Braintech Inc., of North Vancouver, British Columbia, Canada (see sidebar, p.32). All five were all installed during last year’s second half, says Bob Welch, assistant manager of engineering at the Toyota plant. Though Welch declines to provide numbers, he says the five systems met Toyota’s capital expenditure requirement for return-on-investment (ROI) in two years or less.

No one is saying that totally random bin-picking for all kinds of parts is yet practical. Parts such as springs or complicated components with the potential to become tangled are unlikely candidates for early bin-picking applications, for example. Instead, vendors are focusing initially on parts with simpler, easily recognizable geometries, including cylindrical or circular shapes. And in fact, both the TRW and Toyota systems are examples of what industry sources variously refer to as “semi-random,” “semi-structured” or “semi-constrained” bin picking, in which parts are not totally random, but are in some way loosely located in the bin.

Unstack ‘em

In the TRW application, for instance, when brake rotor castings arrive at the plant from suppliers, they are arranged in stacks within wooden bins. “The parts are stacked, and there are no dividers, so they do tend to move during shipment, but they’re fairly well situated,” Denstedt says. Measuring about three-by-four-by-three foot deep, each bin holds 100 to 140 castings.

TRW has previously relied on human operators equipped with lift assist devices to unload the castings, which can weigh up to 331/2 pounds each. In some cases, the plant has also used non-vision equipped pick-and-place systems. The blind pick-and-place systems run on a 20-second cycle time, but due to parts shifting in the bin, they occasionally miss a part, and must then use another 20-second cycle to pick a different part from the bin.

The blind systems work well for low-volume lines that require a casting to be picked and placed every 60 seconds, says Denstedt. But when the plant ...