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This article also tackles a range of assumptions associated with robots: repeatability vs accuracy, the interplay between robot payload and stiffness, as well as flexibility vs in-process adaptability.

A Data-Driven Approach to Improving Robot Performance, Productivity & Efficiency

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IONA is a network of sensors designed to monitor and control industrial robots. The system can monitor the movement of the robot but also the relationship between the robot and the fixture or work object that it’s performing a task on.

IONA is built on the principle of a network of sensors (nodes) permanently and discreetly deployed in a manufacturing environment. By observing motion from multiple vantage points, accuracy is increased and line-of-sight issues are removed. The expansible system scales to your needs and works at a cell to a factory level.

The System is formed part of:

Nodes: Each node tracks the 6DOF motion of targets in 3D within its field of view. It is robustly designed and is connected via a single data and power POE cable. Nodes can be mounted on pre-existing guarding and are able to account for environmental instability.

Tile: Each tile is comprised of a pattern of retro-reflective spheres. These tiles are located sparsely around the cell to provide a reference frame. They are also used to track additional points of interest such as: parts, fixtures and tooling.

TCP Targeting: Discrete Targeting on the robot end-effector to permanently monitors the position and the relationship with the work object.

Controller: The controller manages the network of nodes, combining the 6DOF locations each node returns and calculates an accurate result using the proprietary IONA algorithms. Data output is

The system consists of a series of nodes. These are simply mounted on automation cell guarding around the periphery of a robot cell. And they’re able to capture data from lots of different line of sight meaning that you can continually monitor a process as that process is running. The nodes themselves don’t need stable mounting. They can resect their own position in space so they can be mounted simply on the cell guarding itself. To connect the series of nodes. It’s a simple Power over Ethernet cable. So one cable that provides the data interface but also the powering to the node. This makes installation and setup of the system very simple and can be done in a short space of time.

The sensors themselves measure small retro reflective spherical targets. These are passive targets and don’t need any cabling to connect them. These targets can be placed at strategic points on the robots or on the fixture that the robot is working on, or even to represent the cell datum structure. From the targets we’re able to construct 3D coordinate frames. Within our software then we can track how these frames move and also the relationship between the various frames that we’re monitoring.

The IONA system consists of both hardware and software. The hardware is the series of nodes, the controller, the targeting everything that’s required to capture the data.

The IONA system also consists of our software, our ORA platform. ORA is used to collect the data and also perform analysis before feeding that data back into the system

The IONA system is robot agnostic. We can work with any robot, be that a KUKA, or ABB, a FANUC. The things that change may be how we interact and pass data to the robot controller.

The data from IONA can be used at three key stages of manufacturing.

  • It can be used at the commissioning stage to correct for the differences that occur between the simulation or the offline programme and the physical setup of the cell. This data can also be used to feed back into the digital environment to update the digital twin, meaning that there is an exact replica in the digital world as operating in the physical world.
  • The second stage that we can use this data at is as a process enabler to increase the accuracy of an automated process.
  • We can do this in a number of ways. We can achieve a better alignment between the work object and the robot itself but we can also use our data to perform a calibration in situ on the robot. This calibration can be run as a one off when a cell is first set up, or it can be run each time a process operates.
  • We can also use our data as part of a live feedback loop. So the process is continuously correcting and iterating based on the alignment or repositioning of elements within the cell.This can improve the dynamic accuracy of any given process.
  • The third stage, when we can use our data is as part of a process control approach. By continually monitoring a process over time we can start to spot the trends in the data and understand whether our

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