The Convergence Of Industrial Metaverse, AI, And Robotics

A few years ago, there was widespread discussion about the potential of the industrial metaverse. Since then, the focus has shifted more toward the development of underlying technologies. So, what is the current state of the industrial metaverse?

First, it's important to clarify that the industrial metaverse is not entirely new. Rather, it is an evolution of decades of technological accumulation-such as simulation-that allows engineers to interact within 3D digital environments, simulate processes, and incorporate virtual robots and virtual personnel. Here, engineers can modify and validate production processes.

In recent years, the rise of AI has brought new opportunities. When discussing the industrial metaverse, we are not just referring to simulation; we also include AI copilots or AI assistants that help engineers improve efficiency, make better decisions, and collaborate effectively. Engineers can immerse themselves in industrial metaverse environments and collaboratively advance projects within realistic scenarios.

Industrial Metaverse for Seamless Collaboration

The industrial metaverse enables seamless cross-organizational collaboration, real-time visualization and simulation, and co-design and engineering. It integrates digital twins, IoT sensors, and high-fidelity visualization, creating a whole new way of working.

Take battery manufacturing as an example: using Siemens Process Simulate, manufacturers can leverage physics-based digital twins within a shared virtual space. With digital twins built via the Xcelerator software, interactions among robots, AGVs, and humans within the production unit can be precisely simulated. Then, battery assembly lines can be modeled in NVIDIA Omniverse and seamlessly connected with Siemens' digital manufacturing tools. Through the Tecnomatix Connector, Process Simulate research results are transmitted to Omniverse, allowing users to experience high-fidelity visualization and realistic simulation.

Image: Battery production line simulated in Siemens Process Simulate (left) and visualized in high fidelity within NVIDIA Omniverse (right).

Simulation Ensuring Robot Effectiveness in Production

Using Siemens Process Simulate as an example, engineers can develop, simulate, and validate automated processes within a realistic manufacturing context. This is critical: installing robots in the physical world and repeatedly testing scenarios is costly and often impractical.

By transitioning to digital twins or 3D environments, engineers can build production lines containing all robots, equipment, conveyors, and human workers, and test multiple alternatives to optimize workflows. This reduces the risk of post-implementation issues and eliminates the cost of correcting errors or fine-tuning robots after installation.

A major challenge in deploying robots on the shop floor is ensuring they operate according to proper safety standards to protect human workers. While robot vendors provide safety technologies, these measures often assume restricted robot movements to prevent entry into protected areas. Achieving this in a busy shop floor can be difficult, which is where simulation comes in. Engineers can simulate and validate manufacturing environments with restricted zones, addressing robot safety features before actual deployment.

Users can also randomize robot movements and simulate human operator tasks. If other devices or robots are nearby, optimization can be applied. One key approach in these simulations is minimizing any interference between humans and machines. This includes randomizing human operator actions to test potential safety breaches, ensuring robots stop if someone enters a restricted zone, and preventing hazards like hands being trapped. Simulation enables all these scenarios to be addressed within the design environment, reducing real-world risks.

The Role of Virtual Reality in Robot Deployment

In the consumer goods sector, a manufacturer faced challenges communicating with a supplier in another country. Traditional workstation design methods were outdated, so VR technology was used to create an immersive collaborative environment. Engineers could meet remotely via headsets and work in realistic design settings.

Once workstations are designed, manufacturers can review them remotely, assess the design, and propose adjustments as needed. Virtual operators, modeled on sensor-equipped human workers, allow capturing of shop floor motions. These data streams can be tested in the simulation environment using virtual equipment.

This technology stack enables collaboration within the industrial metaverse, allowing informed engineering decisions without traveling or physically installing equipment.

AI-Powered Robotics in the Future

As AI technology advances rapidly, robots are becoming more autonomous, with enhanced perception of the environment and the ability to make intelligent motion decisions.

While these capabilities are still evolving, we will soon see robots autonomously picking items from cluttered containers and placing them on shelves or into packages. Humanoid robots equipped with this intelligence-also known as physical AI-will be able to sense their surroundings and translate it into physical actions.

When AI-enabled robots intersect with simulation, virtual worlds can be created to train and validate robot task performance in various scenarios. Engineers can use feedback from these virtual environments to test and optimize robot perception and behavior.

The convergence of the industrial metaverse, AI, and robotics marks the dawn of a transformative era in manufacturing. AI is no longer just an add-on; it is actively driving intelligent automation within manufacturing simulations. Leading automation companies are investing heavily in industrial AI, applying technologies like generative AI, machine learning, and machine vision to industrial settings. For instance, "agent-style AI" systems can operate with autonomy and decision-making ability, proactively troubleshooting, simulating, and optimizing manufacturing processes without constant engineer input.

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