artificial intelligence - Robotic Simulation Services

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.

Industries are rapidly advancing. With growing adaptation and accessibility of state-of-the-art technologies, various industries’ production innovation and R&D technology are becoming very advanced, albeit more complex. However, with technologies getting more complex, they are also getting easier to adapt. So laden with numerous possibilities and opportunities, industries are adopting digital technologies in their industrial application to reap these lucrative advantages as deep learning boosts robot picking flexibility.

The ultimate pursuit of automation in industries and production goes through the path of intelligent and smart robots. With more demanding industries, newer and better robots can perform various industrial applications more smoothly and efficiently. But as industries expand their reach into more fields/sectors, they need robots to achieve even more different tasks in different environments.

This broad spectrum of need for the usability of robots leads to robotic technology not being able to keep up with the demand. Hence, traditional methods and approaches to robotics must be let go to introduce new and better techniques to robotic technology. Within the advent of digital technology lies more possibilities for robotics that are even unseen before.

Digital technologies and platforms like Robotic Simulation Services, Offline Programming, Augmented Reality, Virtual Reality, and Artificial Intelligence take the world by storm. They are now in integration or development for almost every industry possible. The robotics industry also is not lagging in this aspect, with robotic manufacturers or various services providers already utilizing these technologies to propel robotics further. Deep learning is one of the technologies in use, with much anticipation and exciting possibilities, within the robotic industry.

Let's talk about Deep Learning

Deep learning is a type of Artificial Intelligence, or more so a kind of Machine Learning approach. In the broader AI paradigm, Machine Learning is a subset of AI that refers to an AI system that can learn with the help of data instead of developers having to code it. ML is an approach to AI that enables various algorithms to remember from data, i.e., training data consisting of input and output data, to infer a pattern or a “knowledge” in the input data about the output. With this knowledge, ML algorithms can effectively predict the outcomes with the analysis of input data.

Deep Learning is a similar approach. It's a family of algorithms in the machine learning paradigm based upon Artificial Neural Networks (ANNs). These ANNs in deep learning can perform representation learning. Representation learning is a method in which systems detect or infer a pattern or representation, i.e., features in the input data for feature detection or classification. Hence, computer science also defines it as feature learning since it detects features from raw data and uses them to perform some specific task.

How Deep Learning Boosts Robotic Picking Flexibility

Deep learning boosts robotic picking flexibility with its data by effectively imitating how intelligent creatures like humans gain knowledge and do certain things. In deep understanding, a system takes in input data and tries to infer a pattern or detect some specific feature in that data. This “learning” approach is known as deep learning. Furthermore, education can also be either supervised, unsupervised or semi-supervised.

These are various deep learning architectures that researchers combine up with various other computer techniques and technologies to enable different features and functions in robotics: deep neural networks, recurrent neural networks, convolutional neural networks. Deep reinforcement learning and deep belief networks are various architectures in deep learning—robotic technology pairs up these architectures with different hardware and technologies to build various robotic functions.

For instance, robotic researchers and developers use convolutional neural networks for computer vision with cameras and other sensors to give visual information like depth. Likewise, different architectures enable different computer application fields like speech recognition, natural language processing, image analysis, bioinformatics, etc. Moreover, these applications are often in use for various purposes within other industrial areas.

Why Deep Learning Boosts Robotic Picking Flexibility?

In robotics, one of the most complex things to perfect is its ability to pick things up. For human beings, picking items seems very easy. However, seemingly effortless things with biological creatures are not always similar to robotics and computer systems.

Thus, although it may seem that picking items up is easy, it is not the case. The complex interworking of different systems together to perform even a simple task is very hard for computers. For instance, to first pick things up, you need to know what you are picking.

This part is usually straightforward since, for example, you can tell a computer that the stuff it's gathering is in a specific location. But the hard part comes when it's doing the actual picking. For example, how is it even going to pick the object? Even in a single production environment, there are a variety of things with different shapes and sizes. In addition, objects have different textures, structures, and a specific suitable picking spot.

We can undoubtedly program a robot to utilize information about a particular object and a suitable method to pick the thing, but programming it to select it is challenging. Relatively, programming a robot to choose only a single type of object can be easy, but you would need other robots for different kinds of things/products. So this is certainly not an effective method to accomplish this.

Furthermore, products and objects may behave differently in different environments, creating complexities in ways deep learning boosts picking flexibilities. For instance, a product with a smooth surface can be slippery to grab or hold onto in a humid environment. Moreover, picking other objects in different backgrounds requires the robot developer to program the robot for various environments and various things. Along with this, considering the wide range of products, this problem quickly becomes substantially huge.

One of the enormous complexities we are not even exploring yet remains motor skills. Programming a robot to perform specific motor skills and functions is one of the vastest complexities of the robot development paradigm. Even to grant them specific motor functions is very hard. That's why it's a huge deal, even if a robot can perform simple tasks like holding a cup, walking, etc. However, now you can certainly deal with these problems through various means.

For instance, a robot that needs to move can have wheels. A robot that does not have to move but grab onto things can have arms on a fixed body. But these solutions are also tough to implement. Add this to the use case, such as a moving robot that has to move on an uneven surface or a wrong road or even locations where there are no roads, i.e., hills, rocky places, etc. Then this problem becomes substantially more challenging. Similarly, for industrial robots, picking different products and objects is also a complex problem due to different environments and types of things it has to deal with in a particular manner.

Apart from these problems, one primary concern is how deep learning boosts robotic picking flexibility, computer vision. A robot needs to see the object it's picking up. Recognizing a thing insight is a significant feat of computer vision that is currently possible with a massive range of solutions available. But simply recognizing an object is enough to interact with the thing. The robot has to know what object it's looking at and determine how it will pick it up. It again involves problems regarding the size, shape, texture, and structure of the object or product.

In hindsight of all these problems, an industrial robot capable of gripping and interacting with different types of objects or products with other characteristics and properties in different conditions or environments is tough to build. Consequently, it is one of the biggest problems in the industrial robotic plane. It is where deep learning comes into play.

We can use various deep learning techniques to teach a system to recognize and interact with an object. Using deep learning methods, we can use data from multiple production sites, companies, and industries of interaction and manipulation of various things and products for training the system. This data can effectively help a deep learning model to “learn” how to pick different objects in different environments in various particular ways.

The initial data can come from systems already proficient in picking and dealing with objects, which would help in how deep learning boosts robotic picking flexibility. For instance, there is data with humans picking up things. These specialized robots pick only a specific object or interact with them, or even human operators that operate machines to pick up different objects. After data collection of these types, a robot with a deep learning system can go through a training process to effectively learn how to replicate the task or perform it more efficiently.

With this, data collection is complete from a specific specialized robot and for different machines. Moreover, developers and researchers can share and augment such data for training there be used robots for broader use cases and even interact and manipulate objects they are yet to interact with. The possibilities are endless as deep learning boosts robot picking flexibility. As a result, developers can build with a wide range of picking flexibility that can help an industry drive itself towards the end goal of automation. It is why companies like FS Studio provide various services regarding robots and AI tools like deep learning. With decades of collective experience and knowledge with a wide range of expertise, FS Studio provides deep learning services for various robots and other innovative services like Robot Simulation Services, Offline Programming Solutions, and the integration of innovative technologies like AR and VR in different systems.

The landscape of Robotics technology is evolving, pushing industries forward for a 360-degree approach to robotics. More so than before, today, robotic technology is progressing at a swift speed alongside its integration with technologies like Artificial Intelligence (AI), Simulation technology, Augmented Reality (AR), and Virtual Reality (VR). Robotics was always at the center of a future where industries are digital with automation at its core. However, industries that fully integrate AI and digital technology to enable automation with robots are still far away.

In the current world, car production and manufacturing is probably the industry with the highest level of robotic usage. One of the most prevalent uses of robotics and automation even in this industry is the Tesla manufacturing facility. Even though this is the case, Elon Musk, the CEO of Tesla, admits that robots are tough to automate and efficiently run without advancing digital technologies like AI and more innovative technologies like the Offline Robot Programming Software Platform or Robotic Simulation Services.

However, with the advent of Industry 4.0, the next industrial revolution, we will see some industries take a 360-degree approach to robotics through digital technology. Robotics technology is a crucial part of this transformation. Hence, enterprises will have to change their traditional policy to robotics with a new innovative and modern digital strategy to keep up with the changing industry and competitors.

With that said, industrial robotics is complex, in fact, very hard. With industries and production, the site the robots will have to work in is susceptible to all kinds of risks. These risks are not only limited to humans but also to the industry itself. Production environments generally contain various types of materials and substances that can create many unforeseen circumstances and problems. For example, rusts or corrosion of machine parts or robots, leaks, noise pollution, etc., are issues that the production will have to deal with almost regularly. Pair this with unforeseen problems in machines since they run all the time; industrial environments are very tough for robots to survive, which is why the 360-degree approach to robots is so important.

Not just the risks and problems for the robots, but the aftermaths of these problems and faults are more expensive to a production site. For instance, when a robot fails, or an installation of a new robot occurs, the actual production environment will probably suffer from its downtime. And industries do certainly not like downtimes. Downtimes lead to the stopping of whole production facilities and bar the production, resulting in the loss. Furthermore, this loss becomes more substantial if the materials or products that are not complete can go wrong. It will add the loss of materials and incomplete products to lower numbers of outgoing products from the factories.

Robotics in industries possesses more importance when it comes to error detection. Since production sites and factories can be dangerous and harmful for humans since they have to approach the machines to detect errors, it can be hazardous and even fatal in some cases. Hence, the emergence of drones and locomotive robots is rising in this department. However, industries are still taking the old approaches to use robotics and digital technology.

Industries generally shape robots around the production and use cases in the production sites rather than the inverse. Although typically, enterprises approach robotics as only a medium to replace human resources either in potentially dangerous places or tasks that may not be possible for humans to perform, the 360-degree approach to robotics in the future would only develop the technology further. Instead of this, industries and production facilities should shape themselves around robotics. Of course, it does not mean changing the particular industries’ end goal towards robotics and its implementation. Instead, it means to shape the industry so that it embraces robotics and involves it in the actual process and communication of the production sites.

Usually, robots in industries are linear, i.e., they are put in place of a human to speed up a process/task with a set of inputs fed to them by the developers or operators. They only do or set out to do specific functions inside the production line.

For instance, we can use a robot to put a product inside a box, put product stickers in packages, and seal the box. However, these robots only perform one task, i.e., a robot for placing products in a box cannot close it or put product stickers on it. Therefore, it limits the opportunities and possibilities that robotics can unlock. For instance, with the integration of technologies like AI, robots can become more dynamic and a part of the actual production process rather than the production line.

With AI and technologies like simulation, innovations like Offline Robot Programming Software Platforms are possible. With this, robots become more helpful; they can even participate in production processes to make them brighter and effective. Moreover, With the possibilities of self real-time optimization and self-diagnosis possible, robots will become able to report errors or possible errors in the future and solve those problems faster than humans ever can. And the time essential for robots to process what went wrong and determine if a possible solution is tiny.

In comparison, humans must first come across the errors, either after the error has already happened or detect it beforehand. Then such errors have to go through actual experts and need proper analysis. Only after this, a solution can come up which can fix the problem. But, unfortunately, the developers or the debug team may misinterpret the answer due to insufficient data or enough time. Even during this time, though, the situation can escalate, sometimes even forcing a downtime in the production. But the upcoming 360-degree approach to robotics would change it all.

With the integration of robotics from the start, alongside the significant goals of the particular industry, the actual use cases of robotics with more comprehensive and newer possibilities can emerge. It will let the industries access the actual use case they want from robots and the robotic technology more appropriately instead of focusing on what robots can do afterward, limiting the robotic possibilities. Only after integrating robotics with the actual goal or vision can an industry properly access what they need from robotics and other complementary technologies.

Every industry has a different need. Along with this need, various production systems and methods emerge. Hence, every industry or company may need something different from robotic technology. Even without using the latest or bleeding-edge technology, a company may fulfill its actual needs, i.e., every company need not use them. Hence, every industry needs to use and approach robotics differently to achieve their needs.

For instance, in a data-driven industry, the static robots that cannot communicate or process does not make sense. Since it's a data-driven industry, utilizing such technology in their robots will provide them with numerous benefits.

In an industry where robots and humans have to work together, human-robot collaboration makes much sense for the upcoming 360-degree approach to robotics. For instance, to perform a task like inspection of a faulty machine, robots can collect data from the air or the ground, while humans can analyze them and provide their insight. It becomes even more efficient with technologies like digital twins, AR, or VR.

3D models with digital twins can be much more efficient if industries integrate them with robotics. Automation becomes much closer while remote operations can thrive. With simulation technology, the training and testing of robots will become a digital endeavor rather than an inefficient, risky and expensive physical approach. Digital technology for robotics can enable rapid prototyping, higher form of product innovation, more advanced Research and Development (R&D), all the while remaining inexpensive, safe, efficient, and fast.

The 360-degree approach to robotics would also impact how we teach the robots as well. Technologies like offline robot programming (OLP) will enable robotics to evolve more rapidly. Offline robot programming replaces the traditional approach to teaching robots with Teach Pendants. Teaching pendants can be very slow, inefficient, and resource-consuming on top of being a significant cause of downtimes when it comes to teaching a robot. Pendants require robots to be out of production and in teaching mode the whole time during their programming. It increases downtime during the installation of robots and brings downtimes if the production house wants to upgrade the programming or coding.

But OLP replaces all that with a software model of teaching. The generation, testing, and verification of the teaching programs are possible through software simulations through OLP. OLP effectively eliminates the need to take out robots during its teaching process, allowing production to continue and robots to work even when training. OLP even opens a path for rapid maintenance, repair, and continuous upgrading of robots, all due to its teaching possible through software updates. Along with this, adopting simulation technology is another major win in terms of robot research and development. Simulations with AI can enable whole new ways of robot development, testing, and deployment. Pair this with technologies like Machine Learning, deep learning, and digital twins, AR and VR. Robots will then indeed be able to thrive. Companies like FS Studio that thrive in product innovation and advanced R&D technology can provide the industry with a much-needed push to propel themselves towards Industry 4.0. With over a decade’s collective knowledge and experience, FS Studio delivers a plethora of solutions for robotic technology and helps companies take a 360-degree approach to robotics.

From everyday market consumers to innovative technologies like robotic simulation services, offline robot programming, AI, AR, and VR, one thing is for sure, the robotic technology in the future will reach places and fields that are unforeseen even today. So, researchers and market enthusiasts have already started to predict what the industry will be like in the future. Hovering over thousands of ideas and scenarios, they have come down to these top three predictions for the robotic industry.

The Robotics industry is continuously evolving and growing. Researchers estimate that the market for the robotic industry globally in 2020 was more than 27 Billion US Dollars. This figure, however, has high expectations to grow astronomically to more than 74 Billion US Dollars by 2026. Researchers also pair this expectation with an annual growth rate of 17.45%, which again believes it will grow more.

The mainstream market also reflects this growing influence of robotics. The demand for robots and robotic technology is increasing in industries and factories, and regular consumer space. It shows that the robotic industry will become more and more mainstream with its uses to be making places even in fields that we cannot foresee today.

With the COVID-19 pandemic, industry and consumer trends are shifting. During the pandemic, automation and remote operations experienced a boom that saw changing needs among manufacturers and consumers. In addition, people working from home, communication technology was on top of its game, with industries relating to remote communications increasing in value and influence.

It also brings together the sensing technology along it. With automation of tasks, even daily tasks being in demand, the robotic industry and the consumer industry focus on automation and sensing technology that enables it. Moreover, with automation comes data. Hence the data-driven industries like cloud technology are also increasing. Today’s data industry is so big that the tech giants of the current world are determinants of the amount of data they control and can process.

Another significant technology in communication, the 5G technology, is also a rave among consumers and industry alike. With this, the robotic industry is also taking advantage of 5G technology, with robots being more capable of high-speed communication and being more data-driven than ever.

We can compile all this information and trends of the current world into three things: Mainstream consumer space, Automation, and the data-driven industry and communication and sensing technology.

The demand for robotic and other state-of-the-art technology is increasing in the mainstream market. As a result, consumers are getting warier with these technologies and are willing to invest in them. It shows that the mainstream consumer market is undoubtedly aware that robotics technology is the future.

Furthermore, with or without the pandemic, communication and sensing technology is increasing in adoption and innovation, giving the green light to the predictions for the robotics industry. But due to the pandemic, it experienced a rapid increase in its adoption and development. Moreover, with people working from home and companies emphasizing remote working, communication technology is experiencing a high rise in demand. It is no different in robotic technology. Since robots integrate other technologies that are very advanced and highly complex, communication and networking will experience colossal development.

Consumers will expect their devices to be able to communicate with them more seamlessly. Furthermore, every use case of any robotic technology will want to fully utilize this advancement in communication technology to enable different possibilities. With high-speed communication possible, fleets of robots will communicate more efficiently and rapidly, creating even more use cases. Furthermore, Fleets of communicating robots capable of working together as a unit to complete specific tasks together will also be a high possibility with newer communication standards like 5G.

Along with communication comes sensor technology. With sensors getting smaller with more efficiency but less power, it will be possible to use them even in unforeseen places and use cases. Furthermore, with home security systems improving daily and technologies like computer vision and natural language progressing, sensors adept at these technologies will also enhance more. So naturally, the robotics industry will also take advantage of this.

Since the robotic industry is mainly based around sensors and their capabilities, with the increasing efficiency of sensors, it will be possible to include more significant, more capable sensors in any robot.

Predictions for the robotic industry are getting wilder; however, the accomplishments don’t fail to amaze us. Like the battery technology is improving further, and these sensors are getting more and more power-efficient, it is almost certain that we will use various kinds of sensors in different fields that are even seen as not possible today. For instance, take our phones, for example. Mobile technology is improving at such a fast pace that with each increasing year or two, people feel obliged to upgrade their phones to a newer model since they have started to feel old even if they are only a year or two old.

Since phones are getting smarter, so are the sensors inside them. A smartphone has numerous sensors, from cameras to accelerators to some phones even having LiDAR sensors in them. Compare this advancement to only a decade back, when phones with even a camera were tough to find. It acts as a testament to how far sensing technology has come and is improving at a fast pace. Of course, this also applies to robotic technology.

With sensors getting more efficient, smaller, more powerful while being more power-efficient, it will be possible for robot developers to pack more robust and accurate sensors in their robots. It will enable more probabilities. Furthermore, with sensors comes to their data. Sensors are devices that extract enormous amounts of data. However, to process and handle this, data-driven technologies are promptly evolving, if not even more.

The data-driven industry is evolving at a pace that exceeded the predictions for robotic industries made before the pandemic. With almost all kinds of technology now capable of dealing with data, manufacturers are constantly packing their products with more data-driven features, thanks to the efficiency of processing units getting better. The data industry is so important today that the top tech leaders of the current world are determinants of the efficient utilization of data technology; with devices capable of collecting large amounts of data, whether, through sensors or user interactions, data-driven applications are certainly thriving.

With data comes technologies like Machine Learning, Deep Learning, and Artificial Intelligence (AI) applications. With AI comes the automation of the industry. The Robotics industry is undoubtedly at the forefront of automation technology, with humans having a vision of automated robots way back. However, what’s even more exciting about this data-driven technology is that it helps a robot have practical and smart applications and even helps to develop and build robots.

Innovative technologies like Simulations, AR, and VR will thrive under the data-driven industry after all these technologies rely heavily upon data. But with data-driven technology developing at a rapid rate, these technologies are also improving very fast. Moreover, simulations are now capable of imitating real-world environments and phenomena with very accurate physics engines. Robotic development is also possible with these technologies, especially since the robotic industry is a costly industry due to its high risk for humans and economic benefits and resource consumption.

Robotic research and development usually require many resources and skills willing to take a risk with high-value components, and research is for waste. Furthermore, since simulations and digital technologies like Robotic Simulation Services or Offline Robot Programming Software Platforms are mainstream, the future robotic industry will depend on these technologies.

With various advantages like rapid prototyping, faster and efficient designing process, fewer resources, and fewer requirements of highly skilled personnel, simulation technology will thrive in the future for the robotic industry. The robotic industry will design, test, develop, and research robotics inside simulations with technologies like digital twins.

The predictions for the robotic industry also indicate that the industries and production sites will be using technologies like Offline Robot Programming Platforms for teaching and programming robots, resulting in fewer downtimes and progressing more smoothly. It is because the robotic industry will have its core lying in digital technologies like these.

Robots of the future will also focus more on the human-robot collaboration where robots will be more capable of working together with humans. For this, integrating technologies like AR and VR in robotics and AI will be crucial. AR and VR will allow the robotic industry to venture towards complete digital premises along with remote technology.

Compiling all this information and trends in the world today, we can be sure that the future of the robotic industry looks to be very promising. From everyday market consumers to innovative technologies like robotic simulation services, offline robot programming, AI, AR, VR, one thing is for sure, the robotic technology in the future will reach places and fields that are unforeseen even today. With this, the top 3 most significant predictions for the robotic industry are:

The general consumer market will adopt the robotic industry. With the increasing popularity of robotic products and machines among common consumers, the robotic industry will adopt this consumer space to produce robotic technology even for common usage. Enterprises will also adopt robotic technology more widely.
With the increasing evolution of sensing and communication technology, the robotic industry will utilize this development to improve its reach further. The robotic industry will use more efficient, robust, and capable sensors to power more use cases. Communication technology will also power the future of robotics. With this, data-driven robotic technology will be the future.
The predictions for the robotics industry also say that the future will see the adoption of digital technologies like Robotic Simulation Services, AR, VR, and Offline Programming Platforms. Thus, the robotic industry will move from the traditional approach towards a more digital approach while enabling rapid development and improvement in robotic technology. Furthermore, automation will be the top-of-the-line demand, and hence robotic industry will see a massive push towards automation.

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.

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.

Teaching robots is a time-consuming and laborious task, especially when you’re manually teaching robots. Particularly with robots of niche applications, use cases, and robots with complex movements or robots within specific environments like industries and production. Robotic technology is continuously evolving, and so is its complexity. However, robotic tech is also becoming easier to use, more accessible, and more adaptable with increasing complexity. Conversely, teaching robots through traditional approaches like Teach Pendants is getting more and more challenging and complex.

The Robotics industry is complex because of the sheer complexity of the technology and the cost of developing, building, and deploying a robot. Robot research and development and deploying robots are challenging tasks because of the sensitive nature of testing in robotics. Testing a robot is an expensive task. Consuming massive resources and time, testing robots along with training them is a very resource-intensive task.

However, due to the advancement of technology and the Fourth Industrial Revolution (FIR) inching closer and closer, industries are rushing towards digital technology and automation, which, in some scenarios like industries and production only possible with robots. Consequently, the importance of robotics in the production industry is increasing day by day. As a result, manufacturers and production sites are getting more eager to adopt their production line with robots with digital technology at its core. And manually teaching robots would only slow the production down and eventually leave you behind in the competition.

The Complexity in Robotics

With robotics comes its complexity. A robot is not a single entity but an integration of several different parts, components, and systems working together. These parts, components, and systems are usually various mechanical parts, motors, actuators, hydraulics, sensors, processing systems, networking interfaces, and many more. These components are very hard to build and even complex to perfect. Furthermore, integrating these parts to work together simultaneously with efficient cohesion to achieve a system that can perform specific tasks is complex on another level.

The integration may well be complete and the robot ready. But another major hurdle comes in the form of programming/coding the robot. Programming a simple robot with a particular function may be easy, but the robots that have to perform complex tasks while performing complex movements with precision are strenuous. This difficulty only scales up for industrial robots that have to accomplish tasks with accuracy and repeatability and perform various activities and functions within the production environment.

Why Manually Teaching Robots Will Hold You Back?

Programming a complex robot also requires a complex teaching process. The traditional approach to programming and coding robots is to use teaching pendants. Teaching pendants are a device that helps robot operators to control and program an industrial robot remotely. For example, these devices can code or teach a robot to follow a specific path or perform certain actions in a particular manner. With teaching pendants, robot operators or developers have to teach these robots manually.

Manual robot teaching may be easier on robots with low movement paths, simple actions, or singular axes. But industrial robots are a whole another story. They need to be constantly working in a usually adaptive and harsh environment of production. Such robots are complex and also very sensitive. Hence training the robots with teaching pendants is a difficult task. It is a very time-consuming task with the requirement of the teaching personnel to be present at all times. Furthermore, the robots have to be in teaching mode during all this time which means they cannot perform other tasks. Add this to the fact operators have to take them out of production during this long teaching process. All this makes manual teaching very cumbersome.

The downtime while teaching the robots is a massive issue to production. Moreover, this downtime is not only a one-time thing. Since industries have to be at the top of their game to thrive, they need to evolve and adapt over time. New changes and upgrades are necessary. Maintenance and repair works are inevitable. And even the failure of robots is not a common thing. All this requires teaching pendants, which is again very slow and a tedious approach to programming robots. It will add more delays, difficulties, costs and consume more resources. And this is a massive bottleneck for production.

Instead of wasting time in this slow and cumbersome manual approach, using new and better solutions with automation at its core is the way to go.

Learn About Offline Robot Programming

Offline Robot Programming is an “offline” approach to robot programming. Offline Programming (OLP) is a software solution to manually robot teaching by replacing the teaching pendants with simulation software. This “offline” solution teaches the robots virtually through software remotely. Thus, OLP takes leading away from the manual approach and takes out the requirement to remove the robots from production.

Although Offline Robot Programming is not a new technology, its evolution in recent years puts it in the spotlight in robot programming and the whole paradigm of robotics. It’s because of the advantages and benefits of using offline robot programming. Offline robot programming replaces the teach pendants with a more elegant solution. Furthermore, OLP allows for industries to train robots and their programming/coding through software updates. Robotic Programming Platforms also offer different software solutions to generate these instructions.

It means there is no need for the actual physical robot to be present in any generation phase or testing the training program/code. Instead, all this happens within the simulation technology inside the robotic programming platform itself. The evolution of simulation technology is so far ahead that it can now accurately simulate almost any object or environment with all the characteristics and behaviors of the original real-world object or environment.

Simulation technologies today can simulate every robot’s functionalities, features, and operations. Various behavior, states, and phenomena of robots and their components can simulate without manually teaching robots. Simulations can accurately simulate the mechanical elements of different parts with different materials and their operation in different environments and conditions. Along with this, fluid dynamics for air and water is also possible to simulate. Collisions, movements, etc., are also potential. It is due to the ability of simulations to accurately simulate and imitate the real-life physics of materials and the environment.

In addition to this, simulations can also imitate electronic components and processes. For example, it can accurately simulate the processing of CPUs and progressing units or even network interfaces and data exchange. Along with this, simulations can even test technologies like Artificial Intelligence (AI) with Machine Learning (ML) and deep learning. All these possibilities allow simulations to simulate all behavior, state, and properties of a robot along with its features and functionalities effectively.

Robotic simulation software solutions are already available, and different industries and companies are already leveraging their benefits. These simulations make innovative technologies like OLP possible to exist and thrive, creating manually teaching robots irrelevant. With offline robot programming, companies need not go back to the old approach of using teaching pendants. Such an old approach is very time-consuming while also requiring enormous resources, workforce, and investment. In contrast, OLP provides companies with elegant future-proof solutions that are effective and efficient.

OLP successfully reduces downtimes from production due to its ability to upload programming instructions in robots that they are working on without taking them out of the output. They can also enable new roads to generation and testing robot programs far from the manual testing method and age of robot codes or instructions. Simulations make it very easy to try these codes, while AI automation enables self-diagnosis and real-time optimization of production lines.

OLP is often seen as a technology that is very complex and requires high skills to utilize. There is a huge misconception that only the sides with deep pockets can afford to use OLP solutions, and there won’t be any demand for manually teaching robots anymore. But that is not the case. OLP solutions are pleasing on paper and easy to integrate and adapt even in existing production. Companies like FS Studio are working hard to bring out innovative solutions and state-of-the-art R&D technologies, including robotic OLPs, to make this transition of using OLP solutions smoother. With decades of experience and collective knowledge of various skillful people, FS Studio brings out solutions like Robotic Simulation Services for multiple companies and industries.

With the increasing pace of the industry’s move towards Industry 4.0, every industry is eagerly shifting towards digital technology while replacing old technologies like Teach Pendants with newer, more elegant, and efficient solutions like Offline Robot Programming platforms. Offline robot programming opens the road to newer possibilities and opportunities, enabling rapid prototyping, testing, training, and superior research and development, saving you from manually teaching your robots. In addition, it will help companies bring out efficient production and help them maximize their efficiency with a proven feat of achieving higher Return of Investment (ROI) in production lines and product innovation. Furthermore, this will help industries and companies innovate and remain at the top of their game to surpass and outperform their competitors.

Robot programming software is a software solution that helps program or code a robot for its use or operation. Offline Robot Programming Software is also the same.

With the advancement of technology, Industry 4.0 is inching swiftly closer towards us faster than ever. Industry 4.0, also known as the Fourth Industrial Revolution, is the age of digitization where every industry has digital technology at its core. Consequently, digital technology is continuously evolving. Today, it almost seems inevitable for industries to adopt digital technology instead of relying on the traditional approach to industry, manufacturing, and product innovation.

Robotic technology is also continuously evolving, with robots today more capable than ever in various fields and fronts, even unseen in the last decade. Moreover, with the complexity and sophistication of the robots increasing, they are constantly getting more and more complex to program, code, and even develop.

However, with increasing complexity in technology, it is also getting more and more adaptable, usable, accessible, and easy to use. It’s because newer bleeding-edge technological solutions help keep these complex problems and technology operable and functional with great ease of use and access. One of the similar problems regarding the increasing complexity is currently running alongside the robotic industry.

The Robotics industry is far more complex, risky, and resource-hungry than most technological undertakings out there. Due to the growing industry use cases for robot and their ability to fulfill these use cases. The nature of robotic technology is that numerous parts and systems converge together to form a single system unit that can perform various tasks and operations using these parts and systems. Due to this nature, alongside the already complex building blocks of robots, i.e., the components and different systems, integrating these building blocks to work in an efficient cohesion with each other is a huge undertaking.

For easing the difficulty of integrating different parts and systems, many state-of-the-art industries and companies are starting to use robotic computer simulations. Simulations are a great innovation of digital technology that can help develop robotics through research, design, development, and production. Moreover, even after production, robotic software can now help robot operations, maintenance, and programming with different robot programming software solutions.

What is Offline Robot Programming Software?

Offline Robot Programming software is an “offline” approach to programming or coding a robot. This “offline” approach takes the usual method of programming a robot, i.e., teaching pendants away while doing the “teaching” part through the software remotely. However, this remote programming of the robot takes away the need of taking the physical robot out of production; instead can program and code robots virtually through software.

Teach pendants the most common interface to program an industrial robot. The device helps control an industrial robot remotely and teaches them to move or act in a certain way. For example, these devices can program or code the robots to follow a specific path or perform a certain action in a particular manner. These devices also allow the operator to control and work with these robots without being physically present or in tether connection with the robot. It means robot programmers or operators get to control the robots and “teach” them remotely. Technicians usually use these devices for testing or programming, or coding of industrial robots. Hence, teaching pendants are a crucial part of industrial robotics.

Offline Robot Programming Software replaces this teaching pendant with a more elegant and efficient solution with the power of software and simulation technology. Due to the control over robots with software since robotic OLP or Robotic Offline Programming allows for uploading programs and codes through software updates. Furthermore, software developers and robot operators can generate these programs and codes through robotic simulation software in a PC rather than using the robot physically. OLP is, therefore, a more elegant, efficient, and more modern way to program industrial robots.

Why is Offline Robot Programming Software Important?

Even though these pendants are helpful and crucial to industrial robotic operations, they remain one of the bottlenecks of industrial robotics. Right off the bat, these devices are very slow and time-consuming. It's also very resource-consuming and requires personal at all times to operate. Furthermore, pendants also require the presence of the actual robot. They need the robot to be physically active in the teaching mode rather than doing other work during the teaching process, which is usually very long.

Hence, during teaching, the robot cannot be in production or be doing other functional tasks teaching process is very lengthy and tediously for the more complex robots with various joints or movement points and axes. The robot programmer has to program multiple joints and parts to code the robot manually, which is very time-consuming. The programmer will also have to take out the production robot during and until the teaching process. It will surely hamper the production line, and hence downtimes become longer.

In any industrial setup, the production line is the most vital part of it. So much so that the whole manufacturing or production plant usually is based around it. Holding such importance, the optimization of production lines is a very crucial task in any industry. Downtimes, irregularities, or faults in the production lines and components around it means it directly hampers the sector. Moreover, machines like robots, especially the ones with automation, are very crucial in production lines. Hence, production lines must not stop nor deter it due to the robots.

However, with robotic OLP, industries can remove and eliminate all these disadvantages and bottlenecks from production. Instead of teaching these industrial robots online, offline programming eliminates the downtime for programming these robots completely. With this power in their hands, production lines can now completely get rid of time for programming. Instead, industries can use all these times in the actual production and get better returns.

With OLP, automation comes one step closer in production setups. Offline Robot Programming software enables rapid prototyping to test programs and codes before uploading them to the robots through simulation software. Furthermore, simulations are now very technologically smart such that they can simulate all robot parts, mechanics, systems, and movements. With such capability in hand, robot programming and even robot development and the building will become very easy. Due to this, testing, training, and evaluating robots virtually become very easy through OLP. Furthermore, it allows for error detection and verification of programs and robot capability to perform tasks and operations even before they are physically present.

Apart from this, Offline Robot Programming software also increases the productivity of production lines and robot operators and developers. Furthermore, OLP also provides greater profitability and has a better Return On Investment (ROI). Moreover, with OLP, one can test and prove new and better project or concept ideas in their quotation phase without investing in physical resources.

OLP allows for not only training and testing but also helps in maintenance and repairs too. OLP can help to track down potential faults and errors even before they occur or after they occur. It further makes the production efficiency and without any downtimes possibly in future too.

Not only is OLP advantageous and beneficial for regular industrial robots, but it's also essential and can be a boon for some industries that involve high risk. For example, industries like aviation, nuclear, automotive are very high-risk industries. Testing robots in these industries is a sensitive matter. Hence OLP is a requirement in these industries to train and test robots efficiently. Furthermore, without, OLP it is likely not even possible and feasible for industries like the space industry able to undertake projects and accomplish them.

Offline Robot Programming software is generally seen as a technology with high complexity and requiring very skillful personals. But that is not the case. Various companies like FS Studio provide solutions when it comes to offline robot programming. Companies like these can help industries get started with OLP and thrive on enabling substantial new possibilities and opportunities. With decades of experience and expertise in fields like Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Simulation technology, FS Studio, can provide companies with proper and efficient OLP solutions to propel their industries with more efficient, safe and effective production lines. With the advent of Industry 4.0 upon us, companies and industries now must look for better alternatives and modern approaches to the industry. Digitization of industries is the future where digital technology will be at the core of all industries with efficient and smart solutions. OLP with simulation technology enables rapid prototyping, testing, development, and superior research and development (R&D) along with faster and efficient programming or coding of industrial robots. Furthermore, industries can collaborate with different OLP providers to determine the best solution for their particular industry and production and help them integrate their existing robots and production for a more smooth transition towards Offline Robot Programming Software.

The advent of next-generation technologies like Simulations, AR, VR, and AI continues to grow rapidly. With continuous evolution in their advancement and increasing accessibility, they can exponentially add value to manufacturers. Hence, influencing industries across the globe to adopt these technologies at an increasing rate. For example, artificial intelligence with immersive technologies like AR and VR swiftly transforms manufacturing processes and product development. But, on the other hand, robotic technology redefines the possibilities and opportunities in various fields and industries.

The increasing sophistication of robotic technology is visible due to giant leaps in the capabilities of current robotic systems. With technology evolving swiftly, the industry is also adopting newer technologies in its manufacturing and product development processes. One of these newer technologies the industry is moving towards is simulation technology.

With the dawn of Industry 4.0 upon us, industries undoubtedly need to advance towards digital transformation. In this advancement, simulation technology is a boon for manufacturers. Although simulation technology is not new due to its rapid evolution in recent years, it is expanding its horizon of possibilities and opportunities. Robotic technology is one of the unknown frontiers of simulation tech.

Simulation software has the power to enable rapid prototyping, testing, and development of product development processes and R&D technology. Computer simulation is one of the vital tools for industries like robotic development and manufacturing. With the crucial role of robots in the manufacturing industry, the development and advancement of robotic technology are significant for the whole manufacturing industry.

Why Simulation Softwares in Robot Development?

Robot research and development, along with its design and production, is very complex. It is not just because of the sophistication of the technologies in a robot. But also because of economic reasons and risks in robotic development. They also have to add value to the manufacturers as well.

Robots are usually expensive pieces of machinery. Industrial and manufacturing robots are costly due to the niche application following the niche research and development requirement. Moreover, even general robot design and development require massive resources, cost, time, and multidisciplinary skills. Furthermore, prototyping robots for testing, evaluation, and assessment need equally, if not more, resources, time, cost, and abilities. Add this with risks present in the real world, and robotic development truly becomes a huge undertaking.

Computer simulations for robotic development can solve all these problems. Computer simulations offer efficient and elegant solutions that are more cost-effective and less time-consuming. Any computer simulation software usually provides a 3D digital space to test and develop a product. Similarly, robot simulation software offers different environments and tools in a digital 3D area to test, run, research, evaluate and develop a robot.

Real robots in the real world consist of parts like motors, batteries, joints, arms, sensors, actuators, controllers, and other mechanical parts. Furthermore, robots also consist of networking, processing, and data handling components to analyze data and communicate. Apart from this, some robots also need to be smart and capable of making various decisions in real-time to add value to manufacturers. Consequently, due to these causes, robots in the real world are very complex and expensive.

However, robotic simulation software provides all these tools, components, and parts in its digital space. Due to the high advancement of simulation software, today, simulation software can simulate all these parts and subsequently a fully functional robot that can run/operate in different conditions and environments. One just has to bring these parts and models together digitally. The simulation software also supports the design and development of these parts and models digitally. Hence, developing or putting together a robot in a simulation environment is very quickly relative to the real world.

Moreover, just like in the real world, robotic simulation software also allows for the testing and evaluation robots in different environments and conditions. Simulation software can simulate fluid and air dynamics, collisions, and many more physical, real-world phenomena with very accurate and modern physics that reflect real-world physics. All this happens similarly to the real world, except the simulations are fast and easy to develop and do not have to suffer huge risks and significant economic setbacks.

With computer simulations on hand, the risks and costs in association with robot development become redundant. It also ensures that the developers do not exhaust their time worrying about resources and cost but instead focus on the actual robot development. It also provides the developers with flexibility and space to develop the best robot for their requirements without compromising developmental risks and costs.

How they Add Value to Manufacturers

With the vast advantages of using simulation software in robotic research and development, manufacturers are beginning to realize the potential it carries. Furthermore, minimizing risk in robot development in manufacturing and factories also means developing robots with better design that suits the requirements to a far greater degree. As a result, companies or factories using robots in various product manufacturing processes can undoubtedly reap the benefits of better and cost-effective robotic solutions, which is possible due to robotic simulation software.

Proper simulation software can ensure the best systems for different applications and use cases. With rapid design and development in the card, even if a system is not up to the mark, companies can simply re-design it in the digital form with much lower costs and resources. In addition, with computing systems becoming cheaper and efficient, simulations can now help manufacturers build their robotic solutions to stay competitive in the market with new and better solutions.

There are numerous ways the robotic simulation software can add value to manufacturers, for example, cohesion with better designs, processes, and efficient investment.

With manufacturers expanding their product spectra to a wide range, robotic systems in use are not always general robots but tailored with specific needs and requirements in play. For instance, a car manufacturing company cannot automate the assembly line process without the same robots. Development of robots enters completion with niche use cases in mind. One robot installs engines while another robot paints the car; another robot detects flaws in the painting. Another installs wheels, another lifts machines before installation, and so on. Each different use case requires another robot.

Hence in this scenario, designing different robots for different use cases in the real world is very expensive as well as being time and resources consuming. However, creating robots for other use cases is much simpler, more accessible, faster, and cost-effective through simulation software. Consequently, robotic simulation software can also help manufacturers to customize and fine-tune robots according to their needs. Moreover, such systems can undergo design and development to seamlessly fit into their existing facilities and systems quickly relative to traditional methods.

Automation also becomes much simpler with the capability of simulation software to test automation and smart technologies in a full-blown manner even before the final design is ready. Furthermore, simulation consisting of accurate and minute details add value to manufacturers, helping them configure their automation system so that the resultant robotic systems can meet their goals. However, manufacturers usually have to take significant risks for proof of design and automation process verification without simulation systems.

Due to all these advantages, simulation systems can return great results on the manufacturer’s investment. Furthermore, simulation software capable of self-diagnosis and automatic error reporting ensures that the finished designs and products are free of errors and potential flaws. It also ensures that the robotic simulation systems function with precision with known efficiencies in different environments and conditions. Thus, it helps manufacturers get maximum returns on their investment.

Moreover, the investment also becomes largely more safe and secure relative to the investment in traditional approaches. Furthermore, with the successful design and development of robots or systems meeting all requirements and needs beforehand, manufacturers can ensure further lucrative benefits and returns. Eventually, the end goal of manufacturers is to get returns from the end product. It largely depends upon the manufacturing process, which depends on the systems and procedures, including robotic systems used for manufacturing.

Hence, ultimately a successful result is a massive win for manufacturers. Robotic simulation software ensures that this result is successful and that the manufacturers get there with much lower costs, resources, time, and skills.

Conclusion

Industry 4.0 or the Fourth Industrial Revolution (FIR) is all about the digital transformation of enterprises. With Industry 4.0 approaching more closely than ever before, industries and manufacturers must keep up with advancements in technologies like simulation and artificial intelligence, AR, and VR. While it may seem that the transition to digital technology and simulations for product innovation, R&D, and robotic development is complex, the result in-store has enormous benefits with lucrative returns.

Hence, companies like FS Studio are working hard in these innovative technologies to ensure that manufacturers can experience a smooth transition to Industry 4.0. For example, ZeroSim, a technology in development and service by FS Studio, is a robotic simulation software technology built on Unity3D, a game engine, and ROS (Robotics Operating System). It provides a multitude of tools for building robots and simulation environments in Unity to interface with ROS.

Technologies like these add value to manufacturers, making robotic simulations faster, easier, and hassle-free to use for manufacturers. It also ensures that manufacturers can easily leverage the lofty benefits of robotic simulation software to transition themselves towards the next industrial revolution.

Building intelligent infrastructure with digital twins has helped several companies to collect, extract, and analyze data. Digital twin technology or virtual twin is overgrowing with increasing accessibility and adaptability. As Industry 4.0 comes closer, technologies surrounding digital twins are also maturing and continue to develop. With the incorporation of technologies like the Internet of Things (IoT), data analysis, and Artificial Intelligence (AI), digital twins enhance R&D innovation with intelligent services like automation, self-monitoring, and real-time optimization. It enables rapid design & development and smart solutions in production, sales, logistics, and overall supply chain.

With the ability to enhance current manufacturing & product development, industries worldwide are incorporating digital twin technology. We can already see this accelerating adoption of digital twins across the industry. Although the global twin market was currently at 5.4 billion US Dollars in 2020, much of its slump is due to the worldwide pandemic. In addition, several industries shut down due to lockdowns and social distancing being the new norm during 2020 because of COVID-19. Nevertheless, the digital twin market is slowly rising again, with a tremendous rise expected after 2021. As a result, the global digital twin market will likely reach 63 billion US Dollars by 2027 due to a high growth rate of 42.7% annually.

What is Digital Twin?

While the idea of building intelligent infrastructure with digital twins is not entirely a new concept, due to its current exponential rise and growth, digital twins are undoubtedly growing more and more prominent. Along with the advancement in IT and digital technology infrastructure, digital twins are also evolving rapidly. In general, the concept of digital twinning represents a physical object or environment in a digital form that possesses its accurate characteristics and behavior. While 3D models and simulations also can describe an object or environment, twins systems do more than that.

A digital twin generally represents a physical object or environment not just in a static manner but in a dynamic form. A digital twin represents every phase of the lifecycle of a physical object or environment. A digital twin represents a physical object or environment from its design phase to manufacturing and maintenance and changes due to re-resign, iteration, and refining the object.

Hence, a digital twin is less of a 3D model rather more like an information model. Unlike traditional 3D models, building intelligent infrastructure with digital twins needs a more dynamic and adaptive approach. They can evolve and change over time concerning changes and enhancement in information and data. Digital twins can communicate, update and even learn similarly to their physical counterparts through data exchange with Artificial Intelligence at its core.

Artificial Intelligence with technologies like Machine Learning and Deep Learning enables a digital twin to behave as accurately as possible in contrast with its physical counterpart. Due to this dynamic nature of digital twins, they are in use to explore solutions, detect and prevent problems even before they happen and essentially plan for the future. Armed with these intelligent and smart solutions, companies and organizations worldwide rapidly adopt these technologies in their operations and global supply chain.

Building Intelligent Infrastructure with Digital Twins

Digital twins have five levels of sophistication. Ranging from a level 1 twin that can describe and visualize the product to a level 5 twin model that can operate autonomously, different levels of digital twin require different levels of infrastructure. For instance, a level 1 twin does not require advanced Artificial Intelligence or Machine Learning systems, but a level 5 twin does need them. Level 2 digital twin is an informative twin that needs to incorporate additional operational and sensory data. Furthermore, level 3 is a predictive twin that can use these different data to infer and make predictions. On the other hand, the Level 4 digital twin is a comprehensive twin that can consider and simulate future scenarios to predict and learn from them.

Building digital twin technology includes converging technologies like IoT, data analysis, design & development of the twin either in 2D or 3D, and incorporating AI and technologies like machine learning and deep learning. The digital twin infrastructure is not only in a digital form but also in physical form. This is because a digital twin simulation model resides in a digital format and connects the physical world alongside it. This connection is the representation of both digital models and physical models such that they represent and replicate each other. Every change in the digital or physical model must be synchronized, and both should also respond to each other’s differences.

The actual connection is made through digital models. We can link the physical world with the virtual world by twins modeling and simulating the physical world to map and represent it in digital form. On the other hand, we can connect the virtual world with the physical one by replicating any changes and updates made in the virtual world in the physical world itself. It will ensure that neither the digital form nor the physical form is not synchronized.

In digital twin technology, synchronization must be in real-time when building intelligent infrastructure with digital twins. Real-time synchronization and simulation of the product is the following infrastructure for digital twins. Whenever a product is in the developmental phase of production, the status of the digital twin must also reflect that. The changes occurring in the digital twin must also be replicated in the physical product. Therefore, the changes in materials, processes, environmental, and every other change must be synchronized across physical and digital forms.

Apart from this, the digital twin infrastructure also requires data analysis for deep learning and intelligent systems. Artificial Intelligence generally powers these intelligent systems along with Machine Learning and Deep Learning capabilities. This is necessary for smart analytics and prediction. ML and deep learning systems must be capable of analyzing substantial amounts of data. This data must be representing the actual physical product in real-world environments. Such data are generated and collected by sensors placed in the physical world and physical development.

The data collection is a crucial metric for a system to detect anomalies or errors through analysis in the digital twins platform. Usually, ML systems process these data types and perform pattern recognition to make predictions or suggestions. Thus, these systems enable self-monitoring, predictive maintenance and diagnosis, alert systems for possible future errors, and detection of abnormalities or inconsistencies in the product.

Due to this, the data must be accurate and representative of the actual physical product and environment with great precision. These types of data also are helpful for the corporations or organizations for their product analysis and study.

These infrastructures together enable all the digital twin advantages. The convergence of these technologies is a complex task. Nevertheless, the resultant solution offers an intelligent system that can track past system analytics to predict future solutions and real-time product optimization. Companies are rapidly advancing towards implementing digital twin technologies in their platforms and systems to leverage such benefits.

Building the Infrastructures

Building digital twin infrastructures is a very complicated and complex process. Since digital twins incorporate various technologies together, it is tough to integrate these technologies to work together flawlessly. Only with such integration can one enable proper digital twin technology and can leverage its benefits.

Since the technologies part of the model twins infrastructures are different, companies must be willing to take on R&D for every technology when building intelligent infrastructure with digital twins. Moreover, if not for flawless integration, the technologies must at least be working together, which is a challenging task. However, technology is rapidly growing, and so is its accessibility and ease of use. Hence, integrating these technologies is increasingly easier to enable the tech stack for digital twins.

With the power of the cloud, technology today is dependent mainly upon real-time computing. With the help of the cloud, companies can leverage virtually endless amounts of computing to enable various services, including digital twins. Furthermore, cloud computing allows companies to build intelligent systems that are ideal for integrating multiple infrastructures of the digital twin technology.

One of the most prevalent uses of cloud computing is Artificial Intelligence. Due to the nature of Machine Learning and deep learning, immense computing power is necessary to develop these systems. Cloud computing shines brightly in this field due to its vast pre-built infrastructure and network of computer systems. In cloud computing, these computer systems are connected through an extensive network of servers and processing systems. Cloud computing service providers serve this network of different systems as a single system with enormous computing power.

Alongside this, a system for efficient and accurate modeling of the physical world with high-performance systems for real-time optimization and synchronization is mainly necessary. Moreover, deep learning and data analytics with intelligent AI systems to enable smart solutions with automation at its core is also imperative. Furthermore, a unified system integrating all these technologies is crucial while building an infrastructure for digital twins.

Companies like FS Studio pioneer product innovation and transformative R&D technology through already established and proven digital infrastructure. Since deploying and building intelligent infrastructure with digital twins is very complex and challenging for companies and organizations, FS Studio provides innovative and smart solutions for these problems. Consequently, companies can focus on their primary product innovation rather than shifting their resources towards building a digital infrastructure.

Challenges of creating digital twins are increasing exponentially, especially with the advancement of technologies like simulation, modeling, and data analysis, digital twins of objects and environments are increasingly becoming more accessible and adaptable across various industries. Furthermore, with the integration of Artificial Intelligence with Machine Learning & Deep Learning, digital twins will transform industries across different spectrums, including the manufacturing industry.

The Fourth Industrial Revolution, or FIR or Industry 4.0 in short, is the automation of traditional manufacturing, production & other related industries with the digital transformation of traditional practices through modern technologies. Thus, industry 4.0 will be the age of digital technologies. Machine to Machine communication (M2M) and the Internet of Things (IoT) will work together to enable automation, self-monitoring, real-time optimization, and the production industry’s revolution.

Digital twins will be at the forefront of Industry 4.0. With its power of rapid designing & development, iteration & optimization in almost every engineering process & practice, digital twins will enable new opportunities and possibilities. In addition, digital twins will transform various manufacturing & production processes, drastically reduce time & costs, optimize maintenance and reduce downtime.

While digital twin technology is not entirely new, its growth and adoption are skyrocketing across various industries in recent years, while the challenges of creating digital twins are also rising. As a result, the valuation of the global digital twin market was sitting at 5.4 billion US Dollars in 2020. Furthermore, although its market was experiencing a slump in 2020 due to the COVID-19 pandemic, it will undoubtedly recover and experience exponential growth again. Consequently, researchers expect that the global digital twin market will reach 63 billion US Dollars by 2027 while rising at the growth rate of 42.7% annually.

Over the last decade, the evolution of the manufacturing and production industry has been mainly focusing on reducing costs, increasing quality, becoming flexible, and reaching customer needs across the supply chain. The manufacturing industry is adopting different modern technologies to achieve these goals. Millennium digital technologies have also been part of this technology stack due to the innovation and opportunities it brings to the table.

Different companies and organizations are using twin tech accordingly in different scales and nature. Due to this, the technology in use varies across the industry, such that some industries use the latest bleeding-edge systems while others use legacy and proven techniques. Companies generally use the latest tech when it becomes available to use the latest features and functionalities. On the other hand, proven legacy systems are in use due to their stability and ease of use.

Likewise, different uses of twinning sims in various industries possess other challenges. Apart from this, integration technologies like the Internet of Things (IoT), cloud, big data, and different approaches to digital twin integration will only increase the challenges for digital twins in terms of the sheer complexity of implementation. However, this also presents an enormous opportunity for industries to adopt and align these technologies to suit different needs to solve these complexities and challenges. Subsequently, companies like FS Studio solve the challenges of creating digital twins, providing a platform for the manufacturers or companies to work on without dealing with complexities.

Generally, the goal of any twin manufacturing is to create a twin or model of a real-world object in digital form. Furthermore, the aim is to make indistinguishable virtual digital twins from the actual physical object. Therefore, from the perspective of a manufacturer or a product development company, a digital twin technology will create an actual physical product experience in digital form. Hence, a digital twin for a product, object, or environment will consistently provide information and expertise throughout the whole product cycle.

A virtual twin can also serve companies for feedback collection alignment, useful for the product or the design team. Results from various tests may provide results that can be useful too. The design/engineering/manufacturing team can compile this information, feedback, and results for multiple purposes from the digital twin model. Furthermore, this compilation can also provide additional insights into the product, which can be very useful to tweak, change or even redesign the product entirely. This digital approach will consume much fewer resources, effort, and costs than the traditional physical approach. Moreover, these changes will also be reflected on the twin's systems instantly as the teams make these changes. This will ultimately allow crews to perform true real-time optimization of a product or a manufacturing process.

It will drastically improve the efficiency of designing and developing a product or a process. In addition, digital twins also enable higher flexibility across the overall design and development process. Furthermore, this flexibility comes at a lower cost and additional agility in manufacturing or product development. Hence, digital twin technology becomes very appealing for manufacturers and product developers due to these advantages and benefits.

One of the main challenges of creating digital twins remains to be the convergence of existing data, processes, and products in the digital form to be easily accessible and usable for the current or future teams in involvement. Moreover, such convergence may also change a company’s complete organizational structure from their R&D technology and product innovation to sales and promotion. Furthermore, incorporating technologies like IoT, the actual development of 2D or 3D models & simulations, and data analysis for consistent process, quality & authentic experience of the product remains a very complex process.

Apart from this, the actual use of digital twins created is also another challenge. The infrastructure and platform needed to use such digital twins are also essential, albeit complex, things to build. For example, suppose a team can create a car’s digital twin for a car manufacturer company. But problems with digital twins are that there is no actual use of the digital twin except for visualizing the vehicle. Even for proper visualization of the car across teams, different platforms and tools are necessary to often serve niche use cases of the company.

For instance, a car company needs a motor, brake, acceleration, air dynamics, and other niche simulations for the digital twin of their car. The technology stack should be able to perform various maneuvers a vehicle performs on the road. Aerodynamics and gravity simulation is a massive deal for car manufacturers. Integrating these simulations is also a monumental task.

Along with this, for the actual process of testing and developing products, the platform has to simulate various objects, environments, and conditions necessary for such functions. Alongside this, the platform should also be able to report errors & statistical data on simulations running while constantly monitoring and diagnosing the product during its testing or development. Collaboration between team members on the platform is also necessary for a large-scale company. Integration of Artificial Intelligence and technologies like Machine Learning and Deep Learning is also a very challenging task to accomplish.

Digital twin technology is also often associating itself with complementary technologies like Virtual Reality (VR) and Augmented Reality (AR). The use of VR and AR in a digital twin platform will upgrade the realism and accuracy of the product experience. With realistic simulations and modeling in VR and AR’s capability to enhance a product experience, the 4.0 industry will incorporate these technologies at the forefront with digital twin technology, increasing the challenges of creating digital twins. Alongside this, integrating the digital twin with the actual physical manufacturing process is also a huge challenge.

Although companies will have to adopt this new industrial revolution 4.0 with digital twin-driven smart manufacturing, the overall process will not be that complex. The hard part is the convergence of different technologies to enable a platform for generating this digital twin and integrating it with the actual physical process in product development or manufacturing. However, since the digital twin simulation accurately represents the actual physical product, the product/manufacturing team will have almost no difficulty incorporating this digital twin tech in their physical process.

Therefore, companies like FS Studio help product developers and manufacturers to focus only on product development and design rather than the process of adoption of the digital twin. While different industries are transitioning towards Industry 4.0 technologies, various platforms and solutions establish themselves as leaders in cutting-edge technologies like the digital twin model with AR VR to eliminate the complexities present while the transition happens. It will help the companies and organizations focus on their primary and core goals instead of shifting their resources and concentrate on their growth to the next industrial revolution.

Realization of challenges for the convergence of technologies like IoT, design, and generation of 2D or 3D models & simulation and analysis of existing data remains. With this, the incorporation of Artificial Intelligence, Machine Learning, and data analysis also pose challenges regarding automation, self-monitoring, and real-time optimization. Subsequently, corporations and manufacturers moving towards Industry 4.0 must place digital twin technology at its core.

It will help companies and organizations transition smoothly towards the industry 4.0 revolution, which incorporates product development and digital transformation. With the power of rapid design and development, new production and R&D innovation will take over the industry, reducing the challenges of creating digital twins in the transition to industry 4.0. Subsequently, with digital twin technology, industries across the spectrum will be growing exponentially in their move towards the next industrial revolution.