Companies are swiftly adopting digital technology, with the whole industry moving towards the Fourth Industrial Revolution (FIR) or Industry 4.0. The ultimate end goal of almost all industries is to be self-sustainable, with automation at its core eventually like the Skyscraper Window Washing Robots. Subsequently, the industry has to adapt and integrate robotic technology in its operational process to reach this goal.
Robotics technology is rapidly evolving in both accessibility and usability. With this evolution, the technology is getting better and more usable, but the robotics market is also more valuable. Consequently, the robotics industry is currently one of the biggest markets of the technology paradigm.
In hindsight, the market also reflects this favorable shift of the industry towards robotics technology. As a result, researchers forecast that the global robotic industry market was more than 27 Billion US Dollars in 2020, with the other estimation that it will cross 74 Billion US Dollars by 2026. This increase in the market value represents an annual increase of more than 17%.
Furthermore, these figures will only increase in rate and estimation in the future post-COVID-19 pandemic era where work-from-home and remote technology is experiencing an enormous boost in development, accessibility, and adoption.
Robotics especially shines in industries where the work is either reparative or dangerous. Industries see this in retrospect in which various industries and production use robotic technology to achieve automation in repetitive and potentially dangerous tasks. For example, in the cleaning industry, skyscraper window cleaning is a hazardous task. With skyscrapers towering very high, the workers usually have to climb onto a platform that is hanging potentially even a hundred floors above with the support of a couple of wires.
Cleaning windows properly is just one of the problems when you are dangling hundreds of floors above the ground. With such high risk, the window cleaning industry rides on workers with steel nerves and a good cleaning capability. Although this business is lucrative, the lack of such workers and even companies that perform such risky jobs. The industry is also declining due to the same reason. With over 40 Billion US Dollars worth of market revenue every year, the window cleaning industry faces a lack of young talents to replace the old and trusty workers. The fact that above 74% of workers with training for such jobs are over 40 years old reflects this problem. Consequently, although a lucrative business, it's a dangerous job facing a severe lack of replacement workers.
One of the primary solutions to this problem is to remove workers from the task altogether. Therefore, replacing the window cleaning workers with robots is one of the possible solutions to eliminate human risk and increase efficiency and potential. Hence, window cleaning robots are growing in popularity in this business.
But to know how this all works, we first have to know about robots.
What is a Robot?
A robot is a programmable machine that can automatically perform specific tasks or take particular actions without requiring human assistance.
People usually imagine robots as machines with humanoid shapes with high intelligence, at least that is the depiction of robots in media and science fiction. But unlike robots in popular media and science fiction cultures, robots come in different forms, sizes, and uses. Furthermore, a robot is any machine with some level of processing power and can perform specific tasks without needing human intervention.
Read more: How DeepMind Is Reinventing Robotics!
For instance, the disk-shaped machine/device that cleans the floor automatically while moving on its own and avoiding obstacles is a robot. Similarly, various toy robots and robotic kits are already available in the consumer market. Drones are also a type of robot that can fly autonomously, balance themselves and follow directions from human operators. Apart from the consumer market, robots are also widely in use in industrial settings. The most widespread use of robotic technology and robots is seen in industries and production sites.
A robot is not a singular device or a machine; instead, it combines various components, systems, and incoherence to perform multiple tasks. These components include sensors, processors, storage systems, power supplies, mechanical parts like wheels, arms, chains, cameras, actuators, rotors, motors, etc. These components, devices, and systems work together efficiently to behave like a singular unit and perform various tasks with collaboration and communication.
With the advancement of technology, various systems, including sensors, processing power, battery power, storage systems, motors, actuator systems, and digital systems, are getting more modern and efficient. With the constant evolution of these components, they are increasingly getting complex. However, increasing complexity also increases the ease of use, efficiency, and capability of these components. The whole robotic engineering paradigm reflects this increase with robots getting smarter, more capable, and more efficient in performing various tasks and jobs with increasing levels of autonomy.
The Case of Skyscraper Window Washing Robots
Skyscrapers are, as their name reflects, very tall and usually stand over 150 meters high. On the other hand, mega-skyscrapers are well over 600 meters in height with more than hundreds of floors. These skyscrapers also require a vast amount of maintenance, including cleaning their windows. But these skyscrapers are so tall that regular cleaners cannot clean them. So they need professional window cleaners.
Professional window cleaners usually stand atop a platform hanging beside skyscrapers and are controllable by a crane. This crane can take them downwards or upwards and sideways across the building. Although the media onto which the workers stand to have railings for safety, it still hangs above hundreds of floors above where one small mistake or mishap can end horribly. Besides these factors, the weather is also a significant factor that can increase the risk of window washing, especially if it is a windy season. So it is a pretty risky job from every corner possible.
Moreover, besides the danger of hanging beside such tall buildings, window cleaning is also a challenging job, with few workers even willing to climb onto the platform that's dangling hundreds of meters above the ground. This same case is why this business is so lucrative in the first place. Unfortunately, this is also becoming why new recruitments are getting complicated. With over 74% of the trained professional window washers being over 40 decades old, the replacement rate with young blood is thin.
Even if one does not fear heights, the job has a significant risk of losing their life, making it unattractive to many. The risk factor is very unfavorable with humans on the scale.
How Robots Make Skyscraper Window Washing More Safe?
Right off the bat, when robots are the ones cleaning the skyscraper windows, we can eliminate the risks of having humans on platforms besides the skyscrapers. When robots are replacing almost every human labor, it is essential to look into this factor where the risk of losing life is more than human labor. It will significantly reduce the risks along with having massive leverage if something does go wrong. Thus, we can remove the heavyweight of having potential dangers for humans.
With robots, maintenance along with cleaning is straightforward to perform. With the advancement in remote technology or even autonomous technology, window cleaning robots can leverage this by being controllable by humans or even independent at their tasks. With robots, workers can permanently bolt or fix the robots onto the lift mechanisms, significantly reducing the time consumption that would otherwise be used in checking harnesses or straps for human workers. It will reduce the turnaround times between jobs and save time significantly.
Not only will window cleaning become safer, but it will also become more efficient and fast while consuming fewer resources. Another significant advantage of using window cleaning robots is the economic benefit. These robots can work at almost any condition without stopping and even multiple robots for faster turnaround times between different jobs. As a result, it will undoubtedly bring more returns from the investment.
It will increase the work capacity of window cleaning companies and make the whole gig more economical for consumers. It will also be fascinating for the skyscraper owner to see the robot cleaning the window rather than being guilty of risking human life. Meaning it will attract more consumers and even less time between the cleaning cycles. It will increase the market value and revenue of the whole industry altogether.
FS Studio, therefore, provides robotic services like Offline Robotic Programming or even robot training and software development that can cater to the window cleaning business. FS Studio’s collective experience and knowledge from decades of research and development with solutions like Robotic Simulation Services alongside emerging technologies like AR and VR.
With expertise in Artificial Intelligence and technologies like Machine Learning (ML) and Big Data, FS Studio provides intuitive solutions for product development and innovative R&D technologies. FS Studio offers cutting-edge solutions for present problems and issues. It also empowers its clients with solid solutions that will also help them solve and tackle future challenges.
Skyscraper window washing robots are a massive step for window washing companies. They are safer, efficient, and cost-effective on top of enabling new opportunities and possibilities not only in end jobs but also on business fronts. With the Industry 4.0 approach, industries are transforming themselves towards digital technology to strive for automation. This goal relies heavily on robotic technology that enables intuitive solutions like skyscraper window washing robots.
Artificial Intelligence (AI) is a transformative technology. Not only can it enable autonomy and machines that can make intelligent decisions, but it can also even reinvent the technological wheels of various industries. Robotics, being an emergent technology to enable autonomy, AI is a beautiful tool that can help flourish the true capability of robotics technology. And Google's AI partner, DeepMind is reinventing robotics once again.
Today, AI is around us everywhere. From different apps to different devices/gadgets and various services we use, AI mainly integrates with these apps, devices/gadgets, or services. With this, AI provides us a superior experience of use with devices capable of making intelligent decisions and predictions. Moreover, AI is very persistent in modern life, with AI in various voice assistants, recommendation systems in services from e-commerce sites to media consumption platforms, and intelligent solutions to make predictions or autonomous decisions.
With these services and devices, AI has already become an integral part of our lives. Therefore, it is only natural that industries and companies use AI to boost their company performance on the consumer and product development and innovation front in such a scenario. One of these industries where AI has much potential is the robotics industry.
The robotics industry in itself is revolutionary, with capabilities to enable autonomy in industries. However, the endeavors of enterprises and various industries pose a massive challenge for robotics to fulfill them alone. So developers and researchers worldwide are trying to embed AI into robotics technology to usher the robotic industry to a new level.
With the help of AI, robots will not only be intelligent, but they will also be more capable and efficient. They will be able to form elegant solutions and make intelligent decisions. Moreover, they will be able to control and move a physical body which is very hard to program and build from the ground up. Furthermore, with the decision-making and prediction prowess of the system with convergence of robotics and AI, revolutionary and even unseen developments are possible.
DeepMind is reinventing robotics, and its developers have certainly caught up with this revolutionary possibility. The search giant Google's AI partner, DeepMind, is now working on this problem of convergence of AI with robotics. Raia Hadsell, the head of robotics at DeepMind, said, "I would say those robotics as a field is probably ten years behind where computer vision is." It demonstrates the lack of distinct development in robotics even when tech-like computer vision embedded in robots is already very far ahead.
The problem lying here is, though, more complex. Alphabet Inc, the parent company of Google and DeepMind, understands this daunting AI incorporation with robotics. More daunting challenges and longstanding problems remain in the Robotics-AI paradigm alongside challenges of gathering adequate and proper data for various AI algorithms to train and test them.
For instance, problems like training an AI system to learn new tasks without forgetting the old one? How to prepare an AI to apply the skills it knows for a new task? These problems remain primarily unsolved, but DeepMind is reinventing robotics to tackle the issues.
DeepMind is mainly successful with its previous endeavors with AlphaGO, WaveRNN, AlphaStar, and AlphaFold. However, with various breakthroughs and revolutionary developments, DeepMind is now turning towards these more complex problems with AI and Robotics.
However, a more fundamental problem remains in robotics. With their AlphaGO AI, DeepMind is reinventing robotics and successfully trained it through the data from hundreds of thousands of games of Go among humans. Apart from this, additional data with millions of games of AlphaGO AI playing with itself was also in use for its training.
However, to train a robot, such an abundance of data is not available. Hadsell remarks that this is a huge problem and notes that for AI like AlphaGO, AI can simulate thousands of games in a few minutes with parallel jobs in numerous CPUs. However, for training a robot, for instance, if picking up a cup takes 3 seconds to perform, it will take a whole minute to just train 20 cases of this action.
Pair this problem with other problems like the use of bipedal robots to accomplish the same task. You will be dealing with a whole lot more than just picking up the cup. This problem is enormous, even unsolvable, in the physical world. However, OpenAI, an AI research and development company in San Francisco, has found a way out with robotic simulations.
Since physically training a robot is rigid, slow, and expensive, OpenAI solves this problem using simulation technology. For example, the researchers at OpenAI built a 3D simulation environment to train a robot hand to solve a Rubik's cube. This strategy to train robots in a simulation environment proved fruitful when they installed this AI in a real-world robot hand, and it worked.
Despite the success of OpenAI, Hudsell notes that the simulations are too perfect. She goes on to explain, "Imagine two robot hands in simulation, trying to put a cellphone together." The robot might eventually succeed with millions of training iterations but with other "hacks" of the perfect simulation environment.
"They might eventually discover that by throwing all the pieces up in the air with exactly the right amount of force. With exactly the right amount of spin, that they can build the cellphone in a few seconds," Hudshell says. The cellphone pieces will fall precisely where the robot wants them, eventually building a phone with this method. It might work in a perfect simulation environment, but this will never work in a complex and messy reality. Hence, the technology still has its limitations.
For now, however, you can settle with random noise and imperfections in the simulations. However, Hudsell explains that "You can add noise and randomness artificially. But no contemporary simulation is good enough to recreate even a small slice of reality truly."
Furthermore, another more profound problem with AI remains. Hadsell says that catastrophic forgetting, an AI problem, is what interests him the most. It is not only a problem in robotics but a complexity in the whole AI paradigm. Simply put, catastrophic forgetting is when an AI learns to perfect some task. It tends to forget it when you train the same AI to perform another task. For instance, an AI that learns to walk perfectly fails when training to pick a cup.
This problem is a major persistent problem in the Robot-AI paradigm. The whole AI paradigm suffers from this complexity. For instance, you train an AI to distinguish a dog and a cat through computer vision using a picture. However, when you use this same AI to prepare it for classification between a bus and car, all its previous training becomes useless. So now it will train and adjust its "learning" to differentiate between a bus and a car. When it becomes adept in doing so, it may even gain great accuracy. However, at this point, it will lose its previous ability to distinguish between a dog and a cat. Hence, effectively "forgetting" is training.
To work around this problem, Hadsell prefers an approach of elastic weight consolidation. In this approach, you task the AI to assess some essential nodes or weights (in a neural network). Or "learnings" and freeze this "knowledge" to make it interchangeable even if it is training for some other task. For instance, after training an AI to its maximum accuracy for distinguishing between cats, dogs, and you, task the AI to freeze its most important "learnings" or weights that it uses to determine these animals. Hadsell notes that you can even freeze a small number of consequences, say only 5%, and then train the AI for another classification task. This time says for classification of car and a dog.
With this, the AI can effectively learn to perform multiple tasks. Although it may not be perfect, it will still do remarkably better than completely "forgetting," as in the previous case.
However, this also presents another problem: as the AI learns multiple tasks, more and more of its neurons will freeze. As a result, it would create less and less flexibility for the AI to learn something new. Nevertheless, Hudsell this problem is also mitigable by a technique of "progress and compress."
After learning new tasks, a neural network AI can freeze its neural network and store it in memory/storage to get ready to learn new jobs in a completely new neural network. Thus, it will enable an AI to utilize knowledge from previous tasks to understand and solve new tasks but will not use knowledge from new functions in its primary operations.
However, another fundamental problem remains. Suppose you want a robot that can perform multiple tasks and works. In that case, you will have to train the AI inside the robot in each of these tasks separately in a broad range of scenarios, conditions, and environments. However, a general intelligence AI robot that can perform multiple tasks and continuously learn new things is complex and challenging. DeepMind is reinventing robotics and now working continuously to solve these AI-Robot problems. Like DeepMind, FS Studio is also hard at work with its collective experience and knowledge over decades. FS Studio is also improving its services like Robotic Simulation Services, Offline Programming, and Digital Twins for reinventing the paradigm of robotic research and development with AI at its center.
The chip giant NVIDIA and Open Robotics partnership may mark a significant stride in the robotics and Artificial Intelligence industry.
NVIDIA is one of the most potent entities for chips manufacturing and computer systems, along with Open Robotics being a giant in the robotics space. This partnership brings these two giants together to develop and enhance Robot Operating System 2 (ROS 2).
As put forth by Chief Executive of Open Robotics, Brian Gerkey, users of the ROS platform were using NVIDIA hardware for years for both building and simulating robots. So the partnership aims to ensure that ROS2 and Ignition will work perfectly with these devices and platforms.
ROS is not a new technology. From its inception in 2010, ROS has been a vital source of the developmental platform for the robotics industry. Also supported by various big names like DARPA and NASA, ROS is an open-source technology that combines a set of software libraries, tools, and utilities for building and testing robot applications. ROS2 is the new version with many improvements upon the old ROS and was announced back in 2014.
However, Open Robots’ Ignition simulation environment primarily focused and targeted the traditional CPU computing modes over these years. Conversely, on the other hand, NVIDIA was pioneering and developing AI computing and IoT technology with edge applications in their Jetson Platform and SDKs (Software Development Kits) like Isaac for robotics, NVIDIA toolkits like Train, Adapt, and Optimize (TAO). All this simplifies AI development and deployment of AI models drastically.
Read more: Are You Still Manually Teaching Robots?
NVIDIA was also working on Omniverse Isaac Sim for synthetic generation of virtual data and simulation of robots. Jetson platforms are open source and are available to developers. But now, with its combination with the Omniverse Issac Sim, developers will be able to develop physical robots and train them using the synthetic data simultaneously.
The NVIDIA and Open Robotics partnership majorly focus on the ROS2 platform, and it’s boosting its performance on the NVIDIA Jetson edge AI and its GPU-based platforms. The partnership primarily aims to reduce development time and performance on various platforms for developers looking to integrate technologies like computer vision and Artificial Intelligence (AI) and Machine Learning (ML), and deep learning into their various ROS applications.
Open Robotics will improve data flow, management, efficiency, and shared memory usage across GPUs and other processing units through this partnership. This improvement will primarily happen on the Jetson edge AI platform from NVIDIA.
This Jetson Edge platform is an AI computing platform and is mainly a supercomputer-based platform. Furthermore, Isaac Sim, a scalable simulation application for robotics, will also be interoperable with ROS1 and ROS2 from Open Robotics.
The NVIDIA and Open Robotics partnership will work on ROS to improve data flow in various NVIDIA processing units like CPU, GPU, Tensor Cores, and NVDLA present in the Jetson AI hardware from NVIDIA. It will also focus on improving the developer experience for the robotics community by extending the already available open-source software.
This partnership will also aim that the developers on the ROS platform will be able to shift their robotic simulation technology between Isaac Sim from NVIDIA and Ignition Gazebo from Open Robotics. It will enable these developers to run even more large-scale simulations with the enablement of even more possibilities. As put by the CEO of Open Robotics, Operian Gerkey, “As more ROS developers leverage hardware platforms that contain additional compute capabilities designed to offload the host CPU, ROS is evolving to make it easier to take advantage of these advanced hardware resources efficiently.”
It implies that developers will openly leverage processing power from different hardware platforms with more powerful, low-power, and efficient hardware resources. So, for example, ROS can now directly interface with NVIDIA hardware and take its maximum advantage, which was hard to do before.
The NVIDIA and Open Robotics partnership also put forward possibilities of results to come out around 2022. With a heavy investment of NVIDIA towards computer hardware, modern robotics can now utilize this hardware for enhanced capabilities and more heavy AI workloads. Furthermore, with NVIDIA's expertise in inefficient data flow in hardware like GPU, the robotics industry can now utilize this efficiency to flow large amounts of data from its sensors and process them more effectively.
Gerkey further explained that the reason for working with NVIDIA and their Jetson Platform specifically was due to NVIDIA’s rich experience with modern hardware relevant to modern robotic applications and efficient AI workloads. The head of Product Management, Murali Gopal Krishna, also explained that NVIDIA’s GPU accelerated platform is at the core of AI development and robot applications. However, most of these applications and development are happening due to ROS. Hence it’s very logical to work directly with Open Robotics to improve this.
This NVIDIA and Open Robotics partnership also brought some new hardware-accelerated packages for ROS 2, aiming to replace code that would otherwise run on the CPU, with Isaac GEM from NVIDIA. These latest Issac GEM packages will handle stereo imaging and color space conversion, correction for lens distortion, and processing of AprilTags and their detection. These new Issac GEMs are already available on the GitHub repository of Nvidia. But it will not include interoperability between Isaac Sim from NVIDIA and Ignition Gazebo from Open Robotics as per expectations of it arriving in 2022.
Meanwhile, though, the developers can explore and experiment with what's already available. The simulator on GitHub already has a bridge for ROS version 1 and ROS version 2. It also has examples of using popular ROS packages for navigation and manipulation through boxes nav2 and MoveIT. While many of these developers are already using Isaac Sim to generate synthetic data for training perception stacks in their robots.
This latest version of the Isaac Sim brings significant support for the ROS developers. Along with Nav2 and MoveIT support, the new Isaac Sim includes support for ROS in ROS April Tag, Stereo camera, TurtleBot3 Sample, ROS services, Native Python ROS support and usage, and even the ROS manipulation and camera sample.
This wide range of support will enable developers from different domains and fields to work efficiently in robotics. For example, developers will quickly work on domain-specific data from hospitals, agriculture, or stores. The resultant tools and support released from the Nvidia and Open Robotics partnership will enable developers to use these data and augment them in the real world for training robots. As Gopala Krishna put it, ”they can use that data, our tools and supplement that with real-world data to build robust, scalable models in photo-realistic environments that obey the laws of physics.” He claimed with the remark that Nvidia would also release pre-trained models.
On the remark about performance uplift in these perception stacks, Gopala Krishna said, “The amount of performance gain will vary depending on how much inherent parallelism exists in a given workload. But we can say that we see an order of magnitude increase in performance for perception and AI-related workloads.” Nvidia’s Gopala Krishna also remarked that the program would increase performance and much better power efficiency with appropriate processor use for an acceleration of different tasks.
Gopala Krishna also noted that Nvidia is working closely with Open Robotics to streamline the ROS framework for hard accelerations. The framework will also see multiple new releases of its hardware-accelerated software package, Isaac GEM. Some of these releases will focus on robotics perception, while further support for more sensors and hardware will arrive on the simulation technology side. The release will also contain samples that are relevant to the ROS community.
This development will aid the growing market of robotics. Especially after the COVID, the growth of the robotic market seems to skyrocket, with more and more industries and companies lining up to use and adopt robotics, from manufacturing and production lines to health care and agriculture usage.
Nvidia and Open Robotics partnership will see the advancement of AI and technologies like Machine Learning and Deep Learning at a rapid pace now with the support of NVIDIA hardware in robotics. Researchers estimate that the global robotics market will cross 210 Billion US Dollars. This estimate is likely to increase with the rapid development of AI and technologies like semiconductor technology, sensors, networking technology with 5G.
This collaboration between Nvidia and Open Robotics will only add valuation to this market with innovative platforms like Nvidia Isaac and ROC, helping developers develop more efficient, robust, and innovative robots and robotic applications.
It will also help the open-source community of robot development since this partnership brings together two of the most significant robotics development communities with ROC and Nvidia Isaac. Furthermore, FS Studio collaborates with this growing community to release its robotic simulation solution, ZeroSim, alongside the Nvidia and Open Robotics partnership. Thus, it will help the development bring together with collaboration and push the robotic development further. Now with the dawn of Industry 4.0, companies are moving towards digital technology. This movement can be seen with industries adopting digital solutions with robotics in different fields from production and manufacturing to the board paradigm of human-robot collaboration possibilities.
Simulation in the digital twin can help the aerospace, manufacturing, and robotics industries in many ways.
How many times have you bought a new product only to find out it's defective? It is a huge problem that has affected many people in the manufacturing industry, and with simulation, we can help! A digital twin can be used for testing products before they are released. Let me show you how this works.
Imagine we have an airplane manufactured by company ABC Corp, and they want to make sure that their new plane design will work well without any defects. To know if their plane is safe enough, they need a simulator that displays the different flight conditions to test for safety. If the makers detect any problems during this simulation, they can fix them before production begins, so no one gets hurt.
This blog post explores the importance of simulation in digital twin technology for aerospace, manufacturing, and robotics. We will discuss why simulation is essential to these industries and how we can use it to improve efficiency.
Let's get started!
Simulation in digital twin for Aerospace:
Aerospace companies have become more competent. They are using digital twins to eliminate unplanned downtime for engines and other systems. Today, airlines can keep their aircraft in service longer due to digital twins' warnings.
A digital twin is the computer model of how an asset behaves in the aviation world. It accounts for variables like weather and performance to predict outcomes. The virtual model also provides actionable advice on what to do if things go wrong based on simulated scenarios. This strategy has been so effective at airlines that aircraft are flying more hours than ever before!
Digital twins are capable of recommending mission adjustments that will decrease wear on equipment, thus increasing longevity and success rate for a given operation.
Data analytics are a vital component of digital twins and can predict when an asset will fail. The sensors receive the data in real-time on specific failure points.
The models make predictions and help determine how long the running equipment has left before needing replacement or repair. It saves companies both money and valuable resources like human labor that would otherwise go towards maintenance efforts if they were done manually instead of digitally predicted.
Creating a digital twin is challenging without the necessary data. However, data about calibration details, the geometry of components, and mechanical assemblies could be enough for creating an effective model that will help improve quality assurance testing.
According to Aviation Today, "Boeing has been able to achieve up to a 40% improvement in quality of parts and systems it uses to manufacture its planes with the "digital twin." Essentially this means that before any aircraft component enters production, they are analyzed digitally using high-powered computers.
Imagine if you could test out how your new car will perform in any weather. Well, with digital twin replication that's possible! This virtual 3D model can go through a range of simulated environments like being underwater or enduring freezing temperatures - all before it ever leaves the assembly line.
Alongside these simulations are data fusion techniques that help gather information on an asset by combining different datasets such as images from sensors embedded into machines. Data fusion evolves alongside technological advances, keeping up-to-date with the piled-up data in volume, velocity, and variety. It can be crucial for businesses who want their products ready for anything life throws at them!
Data is the driving force in our industry. We produce an unimaginable amount of data every day, and it has to be processed by machines so that we can make sense out of it.
The flow from raw data to high-level understanding requires a complex fusion process at different levels: sensor-to-sensor, sensor-to model, and model--model fusion.
Designing a digital twin for one or more critical systems like airframe, propulsion & energy storage, life support, avionics, and thermal protection is recommended for success.
Digital Twin Simulation for Robotics:
For example, let's say you're building a machine that picks up parts from its bin. You want it to know where the function is and how big it is so your robot can grab them correctly without any mistakes or hiccups in production.
We need an algorithm trained by images of the items on top of our bins - which would then tell us what size each item was. We will also need a video feed captured by cameras positioned overtop these bins, giving us that cameras above images
A great example is bin-picking; people must manually place parts in many different configurations for a machine-learning algorithm to learn how it should pick up a part automatically.
This method is an example of supervised learning. When training a supervised learning algorithm, the training data will consist of inputted images paired with their correct outputs like bounding rectangles and labels describing what objects are in each image (e.g., "box," "can," etc.).
There's a lot to consider when you're teaching robots how to complete tasks. In addition to training them on what the job looks like, it also takes repetition before being trusted with delicate and potentially dangerous materials.
The robot must have had multiple rounds of practice for every task for its skill sets not only get better but continue improving overtime without any hiccups or errors that could lead to injury accidents down the line
A robust automation solution can take weeks and even months to converge, depending on the task. For example, a complex system will require more time than one which has few components. Additionally, some of your parts might be unavailable or still in production already - this could limit you from accessing them for training purposes.
"Digital Twin" is making significant leaps forward in industrial robotics, assisting manufacturers by not only setting up systems but also validating them for robust reliability using machine learning and integrated vision techniques. As a result, it can shorten the time taken significantly from months or years down to days.
In a virtual environment, the avatar replaces the real robot. So instead of spending all day in front of video screens and keyboards, it's now easy to do everything from your couch: launch a simulation on your computer and let the machine work for you!
In addition, the costs go down by about 90% because there are no lab fees or equipment setup charges.
Next, you bring your robotics into the physical world from the virtual.
The machine learning algorithm helps to learn what everyday objects and scenes look like when viewed by this device so that its actions are more in line with our expectations for how we would behave if given these inputs.
You can teach an old robot new tricks using AI-based facial recognition software!
Digital Twin: The Future of Manufacturing:
Digital twins are the future of manufacturing. With a digital twin, you can test and simulate before any mistakes happen with physical prototypes—saving time and money from costly errors that could have occurred through experimentation on materials or manufacturing processes.
In addition, manufacturers will never again risk releasing a defective product to market because they know what works beforehand thanks to their virtual representation by way of a "digital twin."
It is getting to market faster than their competitors is a challenge for companies. However, it can be possible with a digital twin as it cuts long steps shorter and reduces changes in production.
The product life cycle happens in the virtual environment where we can make all improvements much easier and quicker- perfecting efficiency and development time.
Imagine you have created this beautiful virtual prototype that has all the potential features. But, instead of wasting time test
One of the best features of digital twin technology is that it can help you predict problems before they happen. So, for example, every time one broke down, its virtual copy would start to analyze data from sensors to pinpoint any potential troubles.
It can solve many potential issues without any intervention from an operator by using predictive maintenance software that collects various sources of data through sensor readings to identify likely future complications with machinery. As a result, if you replace worn-out parts sooner rather than later, your manufacturing process will run more smoothly!
Simulation in the digital twin is reducing costs for industries.
For example, ASME reported, a 2020 study says that up to 89% of all IoT platforms will have a digital twin in 2025, while nearly 36% of executives across industries understand the benefits, with half planning for implementation within just five years from now.
If you're not already familiar with the concept of digital twins, then it's time to get up-to-date. A digital twin is a virtual representation that mirrors an existing physical system in real-time.
In other words, if your company has a manufacturing plant and wants to find ways to be more productive by reducing costs or improving product quality, implementing a digital twin may help!