Guide to Robot Transfer Units (RTU) - LazerArc

08, Sep. 2025

 

Guide to Robot Transfer Units (RTU) - LazerArc

Markets and Applications for Linear Robot Transfer Units

Introduction to Linear Robot Transfer Units

Being fully programmable and with costs continually decreasing for more functionality and capacity, robots can provide significant ROI in manufacturing operations. Robots are ideal for repetitive operations and can significantly improve manufacturing productivity and capacity as well as the health and safety of workers.

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Traditional 6 axis robots have one limitation however, they are usually mounted in a fixed position, such as being bolted to the floor or mounted on a pedestal. This limits their reach and potential orientation to an operation and therefore limits their application.

Adding a 7th axis to a traditional 6 axis robot can provide significant opportunities to increase a robots utility and productivity.

Adding a 7th axis of movement, lateral sliding to a 6 Axis Robot

The 7th axis of movement is provided by a Robot Transfer Unit, also called a Linear Motion Track, Robot Positioning Track, Robot Slide or Robot Transfer Shuttle. The system is often called a '7 Axis Robot' system or '7th Axis Robot'.

Other Robot Transfer Unit Synonyms:

Robot Transport Unit

Linear Transfer System

Transfer Robot

Linear Transfer Automation

RTU

Advantages of 7th Axis Robot

Robot Linear Transfer Systems have Multiple Advantages

Adding an extra axis to a robots functionality extends the envelope of work of the robot, meaning that it can now operate along a workpiece for machining long grooves or adding pinstriping. The robot can now operate between multiple workstations, advancing a workpiece or transporting tools to multiple machines. Parts can be moved along machining and inspection centers to advance production processes. Adding a strict linear motion means that the robot now has the ability to move a part without changing the part's orientation.

In addition to tending multiple machines, 7 axis robots are used for picking pallets or other warehousing tasks. Adding reach to a 6 axis robot enables smaller, less expensive robots to handle tasks which previously required larger robots with more reach.

7 axis robots are used across the automotive, aerospace, packaging, solar/wind, medical, consumer goods industries, and more. Robot Transfer Units (RTUs) enhance operations such as drilling and routing, composite layup, welding, trimming, fastening and assembly, painting and coating. 7 axis robots extend warehousing operations such as loading and unloading, palletizing, product picking and moving products to and between production lines.

Adding the seventh axis to a robot enables a single robot to perform multiple functions along an assembly line. The robot is positioned for each operation with a single carrier piece instead of requiring several robots to be positioned along the line, saving considerable capital expenditures.

Capabilities and Options of Linear Robot Transfer Units

Users can choose from a floor-mounted, elevated (gantry) or inverted configuration.

An elevated Robot Transfer Unit (RTU) moves the rail system vertically off of the floor. This could mean that it is at a height needed to fully access machine tool doorways or it could be elevated well above operations along a high rail much like a gantry crane. Inverted RTUs place the robot upside down on an elevated rail system, enabling the robots to reach down.

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Elevating robot transfer units for overhead operations saves floor space for more compact machine placement. It also enables moving working materials and pallets closer to the area for faster production rates, enabling worker freedom of view and movement and improving access to all machinery by maintenance personnel.

Capabilities:

  • Transfer times of 1.5 meters per second
  • Standard accuracy – DIN 6 on gear rack +/-0.”/ft
  • Heavy duty linear bearings offer high tilting moments
  • Optional armoloy coating on gear rack for lube free operation
  • Low backlash Gear Reducer

Advantages for Design Engineers and Special Machine Builders of Linear Robot Transfer Units

Companies installing robots on 7th axis robot transfer units have sped up production, decreased the need for additional floor space and reduced the amount of time required to provide return on investment for each robot.

Another advantage to seventh axis robotic slides is that more than one robotic arm can be placed on the same slide for additional productivity and flexibility.

When evaluating 7th axis robot rail systems the following impacts machine reliability and productivity:

  • a direct drive gear reducer and gear rack, achieves higher accuracy and better performance with less components
  • robust tubular steel construction is designed for easy maintenance and accessibility
  • made in USA provides better technical support from the initial inquiry to ongoing maintenance
  • oversized linear bearing guideways enable handling more mass
  • oversized rack and pinion systems increase design safety factors
  • high precision guard rails, rack and pinion system and closed loop motor system improve accuracy and lifespan
  • rack and pinion linear drives and machined keyed ends allows for unlimited length and for sections to be added or removed easily for repurposing
  • The ability for the system provider to provide non-standard lengths
  • lubrication systems will in part define the lifespan of the system, self-lubricated linear guide systems and scrapers are available as well as self lubricating systems for the rack and pinion drive
  • the degree of accuracy required, extremely high accuracy designs should be available from your RTU partner
  • flexibility of robot brands so that you can minimize the number of robot brands that you support
  • cable tray location flexibility to accommodate your work environment and cable management system options
  • protection covers for linear bearings to allow for environmental conditions
  • color

Considerations of Robot Transfer Units

When adding a 7th axis to your robot system, you now have to consider the cycle loads on the robot and any actuators and effectors.

Adding mobility to a robot usually means that the duty cycles are going to increase. Will the system be running 24/7 or for a few hours a day? Is the lifetime of the system a few years or 10 or 20 years? A well-designed robot linear transfer unit will meet and exceed the duty life of the robot itself.

Linear Transfer Units need to be designed for their operating environment. In a machining setting with metal chips or in environments that have grease, cutting oils or other liquids, the transfer unit will need the right protection to ensure it meets its design life. Managing liquids, shavings, dust, swarf, contaminants and corrosives can be important design factors.

Maintenance needs to be quantified and managed. Self-lubricating systems extend operating cycles between lubrication and maintenance and can pay for themselves. Cable design is important as cables have a flexure rating that is dependent upon the bend radius and the number of cycles. Poorly designed cables can mean decreased cycles between replacement.

Industrial robots can impart significant force, enough to cause injury or damage. Adding additional robot movements along a linear transfer unit means that additional safety considerations are necessary. Fencing, curtains or other barriers are often required to isolate workers from rapidly shuttling robots. Stop switches and mechanical stops can prevent robots from running off the ends of tracks.

A significant challenge in onboarding a robot transfer unit is programming them to synchronize with the robots they carry. Working with an experienced design partner can ensure that you are capable of programming and operating the completed system.

Because they are integrated with multiple operations, robot transfer units provide operating ‘leverage’. They can make an entire system of machines, operators and workers far more efficient, however when they fail or require maintenance this means that the entire downtime cost of the system is now in play. This means that selecting the right vendor partner is critical to ensuring maximum ROI.

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Transforming Robots Help to Transfer Skills - Scientific American

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.”

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