Where to Use Wall-Mounted Robot Transfer Units - Rollon

There are all types of transfer systems, but robot transfer units tend to be a top contender. The reasons go beyond their powerful capabilities, but also their convenience. Specifically, they can be installed on the floor, the ceiling, or even the wall. But which is right for your facility?

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Learn more about robot transfer units, and how they can benefit your operation. And decide whether a wall-mount robot RTU is the right option for you.

What is a Robot Transfer Unit?

Robot transfer or robot transfer units (RTUs) are long railway-type linear axes. The purpose of a robot RTU is to extend the reach of the robots they transfer. This is done through automated workcells, and results in a variety of benefits.

Understanding the Benefits of the 7th Axis Robot Rail

Robot transfer units often help companies maximize use of their investment in robotics. These extensions are called seventh-axis systems when used to transfer common six-axis robot arms. To be clear, this means having six degrees of freedom.

And because of this, these robot transfer units have seen an uptick in adoption over the last two decades. To no surprise, this increase has complemented the dramatic rise in industrial robots and collaborative robots since especially.

RTUs most commonly install like a railway, which means flat on the floor of the automated facility. This rail enables the attached robots to move through various stations of the facility. This is the case for all types of facility executions, such as:

• cutting
• fastening
• coating
• welding
• material handling
• product-finishing processes
• other assembly

However, a floor-mounted railway is not the only application for a robot transfer unit. Some settings benefit from wall and ceiling-mount RTU. This application requires the installation of linear tracks, which are integrated into rigid gantry frames. Wall-mounted robot transfer units straddle an automated scene below and suspend the robot from the linear-track carriage.

Depending on your process and facility, a wall-mounted transfer robot might make sense.

Case in point: One wall-mount Robot Transfer Unit example

Consider the steel-body Rollon RTUw — a rack-and-pinion driven wall-mounted robotic transfer unit. This robot RTU was designed to precisely move robots from Kawasaki, ABB, Fanuc, Yaskawa, Kuka, and other suppliers. The goal was to leverage Rollon’s vast technical expertise in helping machine builders engineer cartesian systems.

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This RTUw includes a both a rigid linear body (serving as the main robot track of travel) and vertical legs made of steel. In fact, the RTUw comes in variations to transfer robots and their payloads from 1 to 15,000 kg. For example, the RTUw-800 carries static payloads of 10,000 to 15,000 kg and dynamic payloads of 3,000 to 5,000 kg.

The designs borrowed from well-proven linear-motion automation. The result was far greater extension (and far lower cost) than any changes to the robotic design itself allowed. That’s especially true as robots have inherently inflexible work volumes.

In contrast, robot transfer units such as the RTUw have theoretically endless strokes. This is because the design incorporates electric-motor-driven rack and pinion sets. Therefore, a facility can constantly move robotic arms between adjacent machining zones, assembly areas, and other workstations.

Robots of all shapes and sizes increasingly populate workplaces, from factories to operating rooms. Many of the bots rely on attaining new skills by trial and error through machine learning. A new method helps such skills transfer between differently shaped robots, avoiding the need to learn tasks from scratch each time. “Practically, it’s important,” says Xingyu Liu, a computer scientist at Carnegie Mellon University and lead author of the research, presented this past summer at the International Conference on Machine Learning. “And research-wise, I think it’s a cool fundamental problem to study.”

Let’s say you have a robot arm with a humanlike hand. You’ve trained its five fingers to pick up a hammer and whack a peg into a board. Now you want a two-fingered gripper to do the same job. The scientists created a kind of bridge of simulated robots between the two that slowly shifts in shape from the original form to the new one. Each intermediate robot practices the designated task, tweaking an artificial neural network until it reaches a threshold success rate, before the controller code is passed on to the next robot in the chain.

To transition between virtual source and target robots, the team created a shared “kinematic tree”—a set of nodes representing limb parts connected by links representing joints. To transfer hammer-whacking skills to the two-fingered gripper, the team adjusted the sizes and weights of the nodes for three of the fingers to zero. In each intermediate robot, the finger sizes and weights got a little smaller, and the network controlling them had to learn to adjust. The researchers also tweaked their training method so the leaps between robots weren’t too big or too small.

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The Carnegie Mellon system, called REvolveR (for Robot-Evolve-Robot), outperformed baseline training methods such as teaching the target robot from scratch. To reach a 90 percent success rate with the gripper, on the hammer task and in other experiments involving moving a ball and opening a door, the best alternative training method required from 29 to 108 percent more trials than REvolveR did, even though the alternative method used more informative training feedback. In further experiments, the researchers tested their process on other types of virtual robots, such as adding new leg sections to a spiderlike bot and having it relearn how to crawl.

“I think the idea is nice,” says University of Oxford computer scientist Vitaly Kurin, who studies robotics and machine learning and was not involved in the work. Although arranging challenges so an AI can transfer skills between tasks is not new, he says, “this interpolation from one robot to another one for transfer is something I haven’t thought of before.”