A digital twin is a virtual replica of a physical system. It allows you to make better informed strategic decisions about your automation system, helping you to detect and iron out problems long before they become a major problem.

By combining state-of-the-art robotic technology and neural reconstruction, the researchers brought more precision and efficiency to the 3D modeling.

Let’s look at how the team at Dubai’s Robotics Lab used RoboDK to create their innovative system.

Digital Twins: The Future of Robotics

What is a digital twin?

In robotics, a digital twin is essentially a virtual model that closely replicates a physical robotic system.

This model is as detailed as it needs to be for the task at hand — it need not be a hyperrealistic simulation. For example, it will certainly include the kinematic and physical properties of the robot itself. It will also include other components that are important for the robotic task, such as sensors, end effectors, and task objects.

The use of digital twin technology holds tremendous potential. In manufacturing, a digital twin of a robotic arm, for instance, can help you optimize your production processes, identify bottlenecks, and predict maintenance needs without disrupting the robot’s productivity.

RoboDK is a popular platform for digital twin creation. For example, previous research from Western Washington University involved creating a simulated changeable learning factory and connecting it to the physical system to create a digital twin.

Introducing… Dubai’s Robotics Research Lab

The Robotics Research Lab is situated within the Dubai Institute of Design and Innovation. Led by researcher Raffi Tchakerian, this cutting edge facility is committed to pushing the boundaries of robotics and advanced manufacturing.

Tools like RoboDK have been instrumental in advancing research and student projects at the Dubai Institute of Design and Innovation into realms traditionally dominated by seasoned engineers. From 3D printing with sand to bio-printing garments on pre-existing 3D objects, RoboDK stands out as a pivotal enabler in our journey, explains Raffi Tchakerian

As part of the lab’s FabLab setup, Tchakerian’s research team uses a KUKA robotic arm to develop solutions and ideas for advanced manufacturing automation.

In this latest project, the researchers aimed to improve digital twin technology by combining their industrial robot with the latest in neural reconstruction technology.

The Setup: KUKA KR 150 Robotic Arm, Jetson and RoboDK

The aim of this research project was to see how neural reconstruction technology can improve digital twin creation.

To achieve this, the research team used the following hardware and software components:

• KUKA KR 150 Robotic Arm — At the core of the project is the lab’s KR 150 industrial robot. In a variety of manufacturing and other industrial settings, manufacturers and other industries use this 6-axis robotic arm.
• Intel RealSense D435i camera — An off-the-shelf depth camera that combines robust depth sensing with inertial measurements to create point cloud data.
• NVIDIA Jetson Nano — The Jetson is a single-board, AI-powered computer system targeted at embedded applications. We have a version of RoboDK specially designed to run on Jetson boards, opening up a world of new possibilities for AI-powered robotic solutions.
• RoboDK — Finally, the software for the team’s project was based on RoboDK. This widely used robotic offline programming and simulation software is ideal for digital twin creation and already includes the KR 150 in our extensive Robot Library.

The Role of NVIDIA’s Neural Kernel Surface Reconstruction (NKSR)

An important part of the project was NVIDIA’s Neural Kernel Surface Reconstruction (NKSR) technology.

This cutting edge set of algorithms helps to generate highly detailed and accurate 3D meshes from large-scale point clouds of noisy location data.

NKSR technology can scale to large scenes, handle noise, and minimize training requirements. It can reconstruct millions of points in seconds, even when the scan data is messy.

The team used this technology to clean up the point cloud data captured from the RealSense depth camera. These data points were then fed through the NKSR algorithm to create clean models for use with the robotic digital twin.

How the Setup Works

The researchers’ system operates with the following process:

1. The Intel RealSense camera captures a rough 3D model of the scene, creating a point cloud of data.
2. This point cloud is captured by the Jetson Nano board.
3. Each frame of 3D data is synchronized and transformed into a refined point cloud using the Open3D library.
4. An initial mesh representing the scanned object is generated and sent to RoboDK.
5. RoboDK then accurately positions this mesh within the simulated robot scene.
6. The mesh is then further refined using the NKSR algorithm.

This process shows the immense potential for integrating off-the-shelf imaging technology with advanced neural reconstruction for digital twins.

Advancing 3D Modeling with Robotics and Neural Reconstruction

What is next for this type of neural digital twin technology?

The researchers from the Robotics Research Lab showed how you can create powerful simulated digital twins using simple components. Many industries could use this type of setup, from aerospace to pharmaceutical manufacturing.

This project also shows how accessible advanced neural processing algorithms are becoming. With technologies like the NVIDIA Jetson Nano and NKSR algorithms, you can now access powerful functionality in an easy-to-use setup. And with RoboDK, you can seamlessly integrate this functionality with your industrial robot.

If you are looking for a way to integrate your robot with advanced algorithms, this case study is a powerful example of what is possible.

What questions do you have about RoboDK? Tell us in the comments below or join the discussion on LinkedIn, Twitter, Facebook, Instagram, or in the RoboDK Forum.. Also, check out our extensive video collection and subscribe to the RoboDK YouTube Channel.

The post Robotic Digital Twin and Advanced Neural Construction: A Perfect Blend with RoboDK appeared first on RoboDK blog.

In this article, let’s delve into the world of Digital Twins and explore how they are reshaping offline programming, ensuring precision, safety, and efficiency in robotic applications.

Defining Digital Twins

Before we dive into the advantages, let’s demystify Digital Twins. They are sophisticated virtual models that simulate the behavior and characteristics of physical entities, be it a robot or an entire production line. These twins are not mere static replicas but dynamic simulations capable of mimicking real-world scenarios with remarkable accuracy.

One of the significant advantages of Digital Twins in robotic programming is the ability to safely visualize and test various scenarios in a controlled and accurate virtual environment before implementing them in the real world.

Digital Twins in robotic programming can be understood through two primary lenses:

1. Modelling and Simulation: A Secure Rehearsal Space

Modelling and Simulation represent offline programming where code is generated from a virtual model without real-time interactions. It acts as a secure rehearsal space for robots.

2. Real-time Monitoring: Bridging the Virtual-Real Divide

Real-time Monitoring, with tools like TwinBox, enables online communication, allowing immediate feedback between the real and virtual robots.

Benefits of Digital Twins

Digital Twins are pivotal in optimizing offline robotic programming, offering opportunities for innovations, safety, and accuracy in visualizations and applications. By leveraging the potential of Digital Twins, industries can stay ahead in the competitive landscape. They can navigate complex systems with enhanced insights and reliability. The future promises substantial opportunities for advancements and applications of Digital Twin technology in robotic programming. Driving industries towards unprecedented levels of operational efficiency and excellence, Digital Twins also allow programmers and operators to identify potential issues, optimize robot performance, and safeguard both the robot and its environment. Such foresight is vital to prevent expensive errors and system downtime, enhancing the reliability of robotic systems. Furthermore, the improved accuracy of these simulations minimizes the need for fine-tuning paths.

The amalgamation of rich data and detailed simulations empowers engineers and programmers to innovate and enhance robotic applications. The interaction between virtual and real-world data provides profound insights. Improving operational efficiency, enabling predictive maintenance, optimizing resource allocation, and fostering the development of innovative solutions, ultimately enhancing product quality and customer satisfaction.

Transition to the Real World

Builders can create precise virtual models of real robotic cells, enabling them to make necessary adjustments directly in this environment before implementing them on the robot. This ensures synchronization and a seamless transition between the simulated and real worlds. The precision in adjustment and direct communication between the digital twin and the actual robot ensures that every concept developed within the simulation can be accurately translated into the real world, allowing for real-time interaction and consistency.

RoboDK software is at the forefront of exploiting Digital Twins for detailed simulation and offline programming of robots. Facilitating the simulation of complex structures, from entire factory layouts to individual cells, right from a computer. The software also allows for the seamless creation of comprehensible instructions. Ensuring accurate replication of simulations on robots, thus mitigating risks, and promoting safety by providing a platform for testing and visualizing different situations.

Conclusion: the Future of Robotic Programming

In conclusion, Digital Twins are pivotal in optimizing offline robotic programming. Industries that leverage this technology gain a competitive edge by navigating complex systems with enhanced insights and reliability. As we look ahead, the future promises even greater opportunities for advancements and applications of Digital Twin technology. Driving industries toward unprecedented levels of operational efficiency and excellence.

Ready to harness the power of Digital Twins with RoboDK? Take the first step towards revolutionizing your robotic programming and visualization processes today. Download RoboDK’s Trial License to learn more and start your journey towards enhanced robotic programming.

What questions do you have about Digital Twins? Tell us in the comments below or join the discussion on LinkedIn, Twitter, Facebook, Instagram, or in the RoboDK Forum. Also, check out our extensive video collection and subscribe to the RoboDK YouTube Channel.

The post The Digital Twins Advantage: Offline Robot Programming and Visualization appeared first on RoboDK blog.