Robotic picking and cleaning system developed

Engineers from MIT and Princeton University have developed a robotic system designed to assist in picking and sorting tasks, from organizing products in a warehouse to clearing debris from a disaster zone.

02/28/2018


The pick-and-place system consists of a standard industrial robotic arm that the researchers outfitted with a custom gripper and suction cup. They developed an object-agnostic grasping algorithm that enables the robot to assess a bin of random objects and determine the best way to grip or suction onto an item amid the clutter, without having to know anything about the object before picking it up. Courtesy: Melanie Gonick, MITUnpacking groceries is a straightforward, albeit tedious task: You reach into a bag, feel around for an item, and pull it out. A quick glance will tell you what the item is and where it should be stored.

Engineers from MIT and Princeton University have developed a robotic system that may one day lend a hand with this household chore, as well as assist in other picking and sorting tasks, from organizing products in a warehouse to clearing debris from a disaster zone.

The team's "pick-and-place" system consists of a standard industrial robotic arm that the researchers outfitted with a custom gripper and suction cup. They developed an "object-agnostic" grasping algorithm that enables the robot to assess a bin of random objects and determine the best way to grip or suction onto an item amid the clutter, without having to know anything about the object before picking it up.

Once it has successfully grasped an item, the robot lifts it out from the bin. A set of cameras then takes images of the object from various angles, and with the help of a new image-matching algorithm the robot can compare the images of the picked object with a library of other images to find the closest match. In this way, the robot identifies the object, then stows it away in a separate bin.

In general, the robot follows a "grasp-first-then-recognize" workflow, which turns out to be an effective sequence compared to other pick-and-place technologies.

"This can be applied to warehouse sorting, but also may be used to pick things from your kitchen cabinet or clear debris after an accident. There are many situations where picking technologies could have an impact," said Alberto Rodriguez, the Walter Henry Gale Career Development Professor in mechanical engineering at MIT.

Building a library of successes and failures

While pick-and-place technologies may have many uses, existing systems are typically designed to function only in tightly controlled environments.

Today, most industrial picking robots are designed for one specific, repetitive task, such as gripping a car part off an assembly line, always in the same, carefully calibrated orientation. However, Rodriguez is working to design robots as more flexible, adaptable, and intelligent pickers, for unstructured settings such as retail warehouses, where a picker may consistently encounter and have to sort hundreds, if not thousands of novel objects each day, often amid dense clutter.

The team's design is based on two general operations: picking—the act of successfully grasping an object, and perceiving—the ability to recognize and classify an object, once grasped.

The researchers trained the robotic arm to pick novel objects out from a cluttered bin, using any one of four main grasping behaviors: suctioning onto an object, either vertically, or from the side; gripping the object vertically like the claw in an arcade game; or, for objects that lie flush against a wall, gripping vertically, then using a flexible spatula to slide between the object and the wall.

Rodriguez and his team showed the robot images of bins cluttered with objects, captured from the robot's vantage point. They then showed the robot which objects were graspable, with which of the four main grasping behaviors, and which were not, marking each example as a success or failure. They did this for hundreds of examples, and over time, the researchers built up a library of picking successes and failures. They then incorporated this library into a "deep neural network"—a class of learning algorithms that enables the robot to match the current problem it faces with a successful outcome from the past, based on its library of successes and failures.

"We developed a system where, just by looking at a tote filled with objects, the robot knew how to predict which ones were graspable or suctionable, and which configuration of these picking behaviors was likely to be successful," Rodriguez said. "Once it was in the gripper, the object was much easier to recognize, without all the clutter."

From pixels to labels

Elliott Donlon (left) and Francois Hogan (right) work with the robotic system that may one day lend a hand with this household chore, as well as assist in other picking and sorting tasks, from organizing products in a warehouse to clearing debris from a disaster zone. Courtesy: Melanie Gonick, MITThe researchers developed a perception system in a similar manner, enabling the robot to recognize and classify an object once it's been successfully grasped.

To do so, they first assembled a library of product images taken from online sources such as retailer websites. They labeled each image with the correct identification—for instance, duct tape versus masking tape—and then developed another learning algorithm to relate the pixels in a given image to the correct label for a given object.

"We're comparing things that, for humans, may be very easy to identify as the same, but in reality, as pixels, they could look significantly different," Rodriguez said. "We make sure that this algorithm gets it right for these training examples. Then the hope is that we've given it enough training examples that, when we give it a new object, it will also predict the correct label."

Last July, the team packed up the 2-ton robot and shipped it to Japan, where, a month later, they reassembled it to participate in the Amazon Robotics Challenge, a yearly competition sponsored by the online megaretailer to encourage innovations in warehouse technology. Rodriguez's team was one of 16 taking part in a competition to pick and stow objects from a cluttered bin.

In the end, the team's robot had a 54% success rate in picking objects up using suction and a 75% success rate using grasping, and was able to recognize novel objects with 100% accuracy. The robot also stowed all 20 objects within the allotted time.

For his work, Rodriguez was recently granted an Amazon Research Award and will be working with the company to further improve pick-and-place technology—foremost, its speed and reactivity.

"Picking in unstructured environments is not reliable unless you add some level of reactiveness," Rodriguez said. "When humans pick, we sort of do small adjustments as we are picking. Figuring out how to do this more responsive picking, I think, is one of the key technologies we're interested in."

The team has already taken some steps toward this goal by adding tactile sensors to the robot's gripper and running the system through a new training regime.

"The gripper now has tactile sensors, and we've enabled a system where the robot spends all day continuously picking things from one place to another. It's capturing information about when it succeeds and fails, and how it feels to pick up, or fails to pick up objects," Rodriguez said. "Hopefully it will use that information to start bringing that reactiveness to grasping."

Massachusetts Institute of Technology (MIT)

www.mit.edu 

- Edited by Chris Vavra, production editor, Control Engineering, CFE Media, cvavra@cfemedia.com. See more Control Engineering robotics stories.



Top Plant
The Top Plant program honors outstanding manufacturing facilities in North America.
Product of the Year
The Product of the Year program recognizes products newly released in the manufacturing industries.
System Integrator of the Year
Each year, a panel of Control Engineering and Plant Engineering editors and industry expert judges select the System Integrator of the Year Award winners in three categories.
June 2018
2018 Lubrication Guide, Motor and maintenance management, Control system migration
May 2018
Electrical standards, robots and Lean manufacturing, and how an aluminum packaging plant is helping community growth.
April 2018
2017 Product of the Year winners, retrofitting a press, IMTS and Hannover Messe preview, natural refrigerants, testing steam traps
June 2018
Machine learning, produced water benefits, programming cavity pumps
April 2018
ROVs, rigs, and the real time; wellsite valve manifolds; AI on a chip; analytics use for pipelines
February 2018
Focus on power systems, process safety, electrical and power systems, edge computing in the oil & gas industry
Spring 2018
Burners for heat-treating furnaces, CHP, dryers, gas humidification, and more
April 2018
Implementing a DCS, stepper motors, intelligent motion control, remote monitoring of irrigation systems
February 2018
Setting internal automation standards

Annual Salary Survey

After two years of economic concerns, manufacturing leaders once again have homed in on the single biggest issue facing their operations:

It's the workers—or more specifically, the lack of workers.

The 2017 Plant Engineering Salary Survey looks at not just what plant managers make, but what they think. As they look across their plants today, plant managers say they don’t have the operational depth to take on the new technologies and new challenges of global manufacturing.

Read more: 2017 Salary Survey

The Maintenance and Reliability Coach's blog
Maintenance and reliability tips and best practices from the maintenance and reliability coaches at Allied Reliability Group.
One Voice for Manufacturing
The One Voice for Manufacturing blog reports on federal public policy issues impacting the manufacturing sector. One Voice is a joint effort by the National Tooling and Machining...
The Maintenance and Reliability Professionals Blog
The Society for Maintenance and Reliability Professionals an organization devoted...
Machine Safety
Join this ongoing discussion of machine guarding topics, including solutions assessments, regulatory compliance, gap analysis...
Research Analyst Blog
IMS Research, recently acquired by IHS Inc., is a leading independent supplier of market research and consultancy to the global electronics industry.
Marshall on Maintenance
Maintenance is not optional in manufacturing. It’s a profit center, driving productivity and uptime while reducing overall repair costs.
Lachance on CMMS
The Lachance on CMMS blog is about current maintenance topics. Blogger Paul Lachance is president and chief technology officer for Smartware Group.
Electrical Safety Update
This digital report explains how plant engineers need to take greater care when it comes to electrical safety incidents on the plant floor.
Maintenance & Safety
The maintenance journey has been a long, slow trek for most manufacturers and has gone from preventive maintenance to predictive maintenance.
IIoT: Machines, Equipment, & Asset Management
Articles in this digital report highlight technologies that enable Industrial Internet of Things, IIoT-related products and strategies.
Randy Steele
Maintenance Manager; California Oils Corp.
Matthew J. Woo, PE, RCDD, LEED AP BD+C
Associate, Electrical Engineering; Wood Harbinger
Randy Oliver
Control Systems Engineer; Robert Bosch Corp.
Data Centers: Impacts of Climate and Cooling Technology
This course focuses on climate analysis, appropriateness of cooling system selection, and combining cooling systems.
Safety First: Arc Flash 101
This course will help identify and reveal electrical hazards and identify the solutions to implementing and maintaining a safe work environment.
Critical Power: Hospital Electrical Systems
This course explains how maintaining power and communication systems through emergency power-generation systems is critical.
click me