Emerging Paradigms in Robotics Simulation

There are three key pillars to robotics simulation: digital twins, synthetic data generation, and cloud/on-premises/offline simulations. Read this Research Highlight to learn how each of these paradigms serves the requirements of robotics manufacturers as they aim to develop efficient and safe products.

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Robotics Simulation Software Demand to More than Double by 2030

Over the past 20 years, robotics simulation has transformed from a niche technology—mainly used in academia and Research and Development (R&D)—into a crucial tool for industry advancement. It now plays a vital role in digital twin, Virtual Commissioning (VC), and Hardware-in-the-Loop (HIL) paradigms. VC, a key subcategory of simulation, has become essential for Systems Integrators (SIs) in speeding up the adoption of robotics solutions, particularly in the major robotics sectors of warehousing and manufacturing.

Software for robotics simulation is forecast to grow from US$584 million in 2023 to US$1.39 billion by 2030, aggregated across all market sectors. The projected revenue growth in robotics operational planning simulation highlights broader trends toward increased automation and the widespread adoption of robotic systems across various market verticals. This simulation covers a range of applications, including workflow optimization, path planning for different robotic types, human-robot collaboration, robotics operator training, predictive maintenance, troubleshooting, and VC.

“Advanced simulation presents an alternative to brute-force hardware prototyping; the capability to simulate any form factor within any workflow permits a robot prototype to be thoroughly tested before physical development begins.”    – George Chodhury, Industry Analyst at ABI Research

 

This resource reviews the three emerging paradigms shaping the future of robotics simulation: digital twins, synthetic data generation, and on-premises/cloud/offline simulation.

1. Robot Digital Twins

A digital twin is a virtual representation of a robotics asset, simulating its functionality and form. It can include Two-Dimensional (2D) or Three-Dimensional (3D) models, and software vendors like Siemens, Dassault Systèmes, and MathWorks offer solutions to create these twins.

Robotics manufacturers like ABB and KUKA also support digital twin technology for testing and simulating processes. Digital twins are used for Virtual Commissioning (VC), high-fidelity simulation, predictive maintenance, and more. Companies like Autodesk and Emesent specialize in both data-centric and geospatial digital twins, adding significant value to industrial operations.

Digital twins can also be a platform for employee training, mobile robot fleet management, operation optimization, and other applications. 

2. Synthetic Data Generation

In the field of robotics, Generative Artificial Intelligence (Gen AI) offers immediate benefits by creating data that closely mimics real-world scenarios and sensor feedback. Many simulation experts are now exploring AI to enhance their capabilities. NVIDIA is a leading advocate for using synthetic data in robot training. The company has made significant investments in this area and now provides solutions like Omniverse Replicator. This tool not only aids in training reinforcement learning algorithms for robotic systems, but also generates random virtual scenes to improve Machine Vision (MV) classification algorithms.

Industrial robotics manufacturers are also embracing synthetic data to boost training efficiency. ABB’s RobotStudio, for instance, supports synthetic data generation and high-fidelity 3D simulation, with optional cloud features. Additionally, artificial data can be used to train human operators in managing robotic systems within dynamically simulated environments.

3. On-Premises, Cloud, and Offline Simulation

Robotics offers a promising opportunity for cloud vendors, with companies like ABB and NVIDIA integrating cloud computing into their robotics simulation platforms. ABI Research forecasts that robotics simulation hosted on the cloud will grow from 7% of all simulation deployments in 2023 to 43% by 2030. Cloud-based simulations enable the use of distributed computing resources, lower training costs for Machine Learning (ML) models, and facilitate rapid software deployment. As high-fidelity simulations demand more computational power, local computing faces higher Capital Expenditure (CAPEX) and latency issues.

While industrial robotics will still require on-premises solutions for low-latency applications, emerging approaches like “cloud bursting” could offer significant benefits. This method uses cloud resources when local hardware is maxed out, which could be advantageous for sectors like warehousing and logistics that use Autonomous Mobile Robots (AMRs) and other computationally-intensive tasks.

Amazon Web Services (AWS) and Microsoft Azure are leading in cloud robotics, with AWS RoboMaker and Microsoft Azure’s support for Robot Operating System (ROS) Gazebo. ABB, an industrial robotic manufacturer, also offers cloud-based simulation with RobotStudio Cloud. Offline simulations using local systems are standard for training robots, with major manufacturers and software vendors providing high-fidelity, robot-agnostic simulators.

Key Companies

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For a more detailed overview of modern methods for robotics simulation and the vendors offering advanced solutions, download ABI Research’s Robotics Software: Simulation, Virtual Commissioning, and Emerging Innovation report.