AI, XR, digital twins set to transform robotics #sciencefather #researchawards #scientists #ArtificialIntelligence #ExtendedReality
The emerging network of mutualistic technologies – including extended reality (XR), artificial intelligence (AI) and sensors – is set to benefit a number of industries and applications, not least robotics.The synergistic effects of these technologies have the potential to advance robotics and radically transform the possibilities of integrating robotics into economies and societies. The potential to drive new markets, increase productivity, and enable novel service applications is substantial.
The advent of embodied AI marks a step towards making AI available across applications. As defined by Nvidia, embodied AI represents “the integration of artificial intelligence into physical systems, enabling them to interact with the physical world”.
Embodied AI goes beyond robotics in the strict sense and applies to smart systems and infrastructures more generally. It extends the capabilities of AI to physical systems – such as buildings, robots and autonomous vehicles like cars, trucks and robotaxis – and by integrating machine learning and computer vision, these systems can unlock the potential of generative AI applications in physical industries.
AI models can leverage data that existing robots collect will operating. These models then inform robotic applications by bridging the gap between simulations and real-world applications. In this context, digital twins will play an important role. These will provide synthetic data that can supplement data collected in the field. This type of data is artificially created data “designed to mimic real-world data,” says IT giant IBM.
Splicing virtual and physical applications
Industrial-machinery manufacturer Siemens is looking at the benefits digital twins have to offer for integrators and users of robots and industrial equipment.Brian McMinn, machine tool business segment manager at Siemens, explains how such virtual environments support computer-numerical-control (CNC) machines in an increasingly digitalised world: “We can dry run in a virtual world before they even start building the machine.”Industrial-robots producer KUKA similarly leverages digital twins to support its product offer. For example, stove manufacturer HASE Kaminofenbau uses digital twins and KUKA welding robots in its operations.Florian Fischer, head of production development at HASE, outlines the advantages: “We wanted to build an ultra-modern, flexible robotic system that will also be able to process future models that don’t even exist yet, without placing constraints on our designers or bringing production to a standstill.”
Toppan offers its own digital-twin solution TransBots to address the changes digitalisation will effect across all spheres of life. The global printing and packaging company applies a very wide view on the future use of digital twins, stating: “As our society is going to be more and more digitised in the future, we will need to implement a digital twin system where information is shared between humans, robots and services in a virtual space, thereby allowing work to be performed efficiently.”
Station Ai in Nagoya, Japan, is using a digital twin solution to establish a “robot-friendly environment” within its open-innovation-focused facility with a participating network of “more than 1,000 startups, partner companies, VCs and other support organisations, and universities”, according to the firm.Training robots in digital twins Digital twins can not only explore layouts and workflows that accommodate for robotic systems, but can provide training grounds for robotic systems to accelerate their use across application areas and lower cost associated with robotic applications. Digital twins can overcome hurdles that currently prevent the use of AI in some applications.
In manufacturing, companies can collect and analyse data from the factory floor to train AI-enabled robotic systems. But many manufacturers lack such data-collection abilities. Similarly, in fairly unstructured operations such as in mining, but also many manufacturing environments (including construction), the complexity of human operations can quickly outstrip the benefits of integrating AI-powered machinery or “cobots”, collaborative robots.
In most manufacturing facilities, digital twins can create synthetic data to train AI-enhanced robotic equipment. In more complex, rapidly changing surroundings, digital twins offer a pathway to facilitate the use of AI systems in the future, particularly if these twins can feed on real-time sensor data that reflect ongoing changes.
The advent of embodied AI marks a step towards making AI available across applications. As defined by Nvidia, embodied AI represents “the integration of artificial intelligence into physical systems, enabling them to interact with the physical world”.
Embodied AI goes beyond robotics in the strict sense and applies to smart systems and infrastructures more generally. It extends the capabilities of AI to physical systems – such as buildings, robots and autonomous vehicles like cars, trucks and robotaxis – and by integrating machine learning and computer vision, these systems can unlock the potential of generative AI applications in physical industries.
AI models can leverage data that existing robots collect will operating. These models then inform robotic applications by bridging the gap between simulations and real-world applications. In this context, digital twins will play an important role. These will provide synthetic data that can supplement data collected in the field. This type of data is artificially created data “designed to mimic real-world data,” says IT giant IBM.
Splicing virtual and physical applications
Industrial-machinery manufacturer Siemens is looking at the benefits digital twins have to offer for integrators and users of robots and industrial equipment.Brian McMinn, machine tool business segment manager at Siemens, explains how such virtual environments support computer-numerical-control (CNC) machines in an increasingly digitalised world: “We can dry run in a virtual world before they even start building the machine.”Industrial-robots producer KUKA similarly leverages digital twins to support its product offer. For example, stove manufacturer HASE Kaminofenbau uses digital twins and KUKA welding robots in its operations.Florian Fischer, head of production development at HASE, outlines the advantages: “We wanted to build an ultra-modern, flexible robotic system that will also be able to process future models that don’t even exist yet, without placing constraints on our designers or bringing production to a standstill.”
Toppan offers its own digital-twin solution TransBots to address the changes digitalisation will effect across all spheres of life. The global printing and packaging company applies a very wide view on the future use of digital twins, stating: “As our society is going to be more and more digitised in the future, we will need to implement a digital twin system where information is shared between humans, robots and services in a virtual space, thereby allowing work to be performed efficiently.”
Station Ai in Nagoya, Japan, is using a digital twin solution to establish a “robot-friendly environment” within its open-innovation-focused facility with a participating network of “more than 1,000 startups, partner companies, VCs and other support organisations, and universities”, according to the firm.Training robots in digital twins Digital twins can not only explore layouts and workflows that accommodate for robotic systems, but can provide training grounds for robotic systems to accelerate their use across application areas and lower cost associated with robotic applications. Digital twins can overcome hurdles that currently prevent the use of AI in some applications.
In manufacturing, companies can collect and analyse data from the factory floor to train AI-enabled robotic systems. But many manufacturers lack such data-collection abilities. Similarly, in fairly unstructured operations such as in mining, but also many manufacturing environments (including construction), the complexity of human operations can quickly outstrip the benefits of integrating AI-powered machinery or “cobots”, collaborative robots.
In most manufacturing facilities, digital twins can create synthetic data to train AI-enhanced robotic equipment. In more complex, rapidly changing surroundings, digital twins offer a pathway to facilitate the use of AI systems in the future, particularly if these twins can feed on real-time sensor data that reflect ongoing changes.
Computer Scientists Awards
For Enquiries: info@computerscientist.net
Website: computerscientists.net
Nominate Now: https://computerscientists.net/award-nomination/?ecategory=Awards&rcategory=Awarde
#sciencefather #researchawards #scientists #researcher #ArtificialIntelligence #ExtendedReality #DigitalTwins #RoboticsInnovation #AIDrivenRobotics #SmartAutomation #IntelligentMachines #FutureOfRobotics #XRTechnology #VirtualReality #AugmentedReality #Industry40 #Industry50 #SmartManufacturing #RoboticsEngineering #AIAutomation #HumanRobotCollaboration #TechInnovation #DigitalTransformation #NextGenRobotic
Comments
Post a Comment