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.
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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.
Read more: Computer Simulation of Human Robots Collaboration in the Industries
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.
Computer Simulation of Human Robots Collaboration in the industries is closer than we think. The current industry is moving towards the Fourth Industrial Revolution (FIR). FIR or Industry 4.0 is the digital transformation of the existing industries to enable new ways of manufacturing & production with automation at its core. The digital world will effectively meet the real world at this stage, integrating them on a level never seen before. Human Robots collaboration is one of the significant parts of this integration. With transformative technologies like computer simulations, AR, VR, and digital twins, cooperation among humans and robots is an absolute path that the next generation of technology will take.
Computer simulation is a very crucial tool for industries like robotic research and engineering. With the increasing adoption of computer simulation in various industries, simulations are rapidly becoming a vital part of product innovation and R&D technology. It is especially true for the robotic industry since collaboration between humans and robots is an essential part of the human robot paradigm.
Where Does Computer Simulation Come into Play?
Some factors influence the possibility for robots and humans to work together and collaborate efficiently. One of the top priorities or factors that affect this collaboration is human safety. During the operation, development, or testing of this concept of computer simulation of human robots collaboration, human safety is a top priority and should never be compromised. For this, various safeguards or failsafe mechanisms, power limiting restrictions, tools to monitor for possible errors, and proper fallback plans can be helpful.
Alongside this, robots that are in use must be aware of their surroundings and environment. At the very least, the use case of the robot must reflect its awareness and capabilities. Furthermore, robots also must control and change their actions as per real-time feedback and happenings in their surroundings. Thus, it presents the robot research and development industry with another challenge of autonomy and the ability of robots to perceive their surroundings or environments efficiently.
Read more: How Are Industries Creating New Opportunities By Combining Simulations and AI
Conversely, bidirectional communication among robots and humans may open the door to fulfilling all the requirements necessary for a safe and effective human robot collaboration. But achieving such a feat is also not possible without proper testing and massive investments of time, resources, and money.
Computer Simulations can solve all these problems and complexities with efficient and elegant solutions. Computer simulation technology provides a modeling system to visualize any complex system, even 3D digital space. For example, a robot consists of joints, motors, arms, actuators, sensors, links, controllers, and other mechanical and electronic components like a battery, processing unit, and networking interfaces. All these components and elements can be costly when they reach the level of sophistication a robot requires. Alongside this, integrating these components into a complete robotic system in which these components work together efficiently as a whole system is also a very complex and expensive task to accomplish. Nevertheless, this is where computer simulations come into play.
The advancement in computer simulation technology now allows for the simulation of all these components and elements in a fully functional robot. Alongside this, computer simulation software can also simulate various environments and conditions under which a robot may operate. Much like a natural environment, a simulation environment allows for multiple experiments, tests, and evaluation of a robot, except it, is without all the costs and risks present when testing the robot in the real world. Computer simulations also enable monitoring and assessing robots with a very high level of sophistication in virtually any environment or condition possible.
Why is Computer Simulation of Human Robots Collaboration Important?
The human robot collaboration is essential for the factories of the future and all the possibilities that follow. In a space where robots and humans can work together efficiently to complete different tasks, endless opportunities emerge. For example, robots allow us to perform precarious and dangerous jobs that require massive strength or skill, along with repetitive or requiring extra precision. Meanwhile, some jobs require human intervention due to either being too expensive or complex to automate and jobs that require critical thinking and human intelligence. Thus, it constructively allows industries to utilize the best of both worlds efficiently.
For instance, risky jobs like mining, exploration of unknown borders and areas, repetitive assignments, lifting heavy loads, etc., have more practical industry use cases for robot in the field, but they also require human intervention. Similarly, jobs that require extra precision, like in surgery, may be more suited for robots. Still, due to a lack of intelligence and critical thinking, it is currently unable to do so. Likewise, human intervention is essential in search and rescue operations, but it also requires scanning large and potentially unsafe environments that are more suited for robots or drones. Alongside this, all factories and manufacturing industries cannot generally use robots due to either being too expensive to automate the job or too complex for robots to perform. Hence, human resources are used in various factories and manufacturing sites, albeit the factory and manufacturing sites are dangerous and unsafe.
These difficulties are easily removable if computer simulation of human robots collaboration becomes very efficient and easy to realize. Moreover, if such cooperation becomes possible to achieve, one can reap potential benefits from both worlds. For instance, robot developers in health care organizations can utilize the precision of a robot and the critical thinking of a surgeon to develop a surgical robot to perform complex surgeries on patients.
Read more: Simulation in Digital Twin for Aerospace, Manufacturing, and Robotics
Consequently, a collaboration between humans and robots that enables an open environment where humans and robots can work together to complete works with integration of benefits from both worlds is a very lucrative goal to achieve. Computer simulation opens the door to such a goal. Due to the numerous advantages computer simulations possess, various industries develop human robot collaboration systems.
Generally, robot development in computer simulation software starts with designing and prototyping the robot. It requires a massive amount of resources, cost, time, and multidisciplinary skills in the real world. Then, each prototype comes to its testing, assessment, and redesign of the system according to the evaluations and results. It also requires equally if not more massive amounts of resources, cost, time, and skills in the real world. For a complete robot consisting of all its features and functionalities and compliance with all the factors discussed above, this process of prototyping, redesign, and testing has to be repeated numerous times until the evaluation and results are entirely within acceptable terms.
However, with the help of computer simulations, all these processes become redundant. When robot development with computer simulations occurs, developers/manufacturers get a digital platform to perform rapid prototyping with testing, modeling, redesigning, and programming all within the simulation. With the help of the computer simulation, developers can design a robot with all the parts and components right from the start to get a robot model. This model can go through various experiments, evaluations, and assessments to ensure formal requirements compliance. If not, developers can make changes or even redesign the robot entirely without much effort since it's in a digital form.
Not only this enables rapid prototyping and development, it ensures that developers do not exhaust all their time worrying about resources or costs but utilize that time for better ideas and models. It also opens the door for creative minds to flourish and experiment with various designs and configurations of robots. Furthermore, since the initial design process starts with a digital model, developers can tweak, organize and play with different formats. Finally, it will ensure that the design phase outputs the team's accurate designs with an efficient and agile developmental process.
Moreover, testing and evaluation of robots in different environments is also possible with error reporting and monitoring systems working together to gather essential data. It ensures that all unexpected problems or errors that the developers may encounter during the physical build of the robot are taken care of and solved. Testing with trajectory planning, verifying algorithm operation and efficiency, verifying the integrity of the design, and overall working of the robot can all be done in simulations. Testing various fluid mechanisms, aerodynamics, mechanical integrity, and kinetic forces with realistic physics engines is also possible.
One of the most vital computer simulation of human robots collaboration is human safety. Simulations enable testing for human safety and protection in numerous conditions and environments. We can quickly test and examine communications, control, and safety mechanics inside computer simulations without ever having to put a human at risk. With technologies like Augmented Reality (AR), Virtual Reality (VR), and intelligent AI systems, humans can test these robots with immersive experiences in realistic environments without taking risks.
It will rapidly evolve the development of human robot collaboration with the power of rapid prototyping, innovative product development systems, and efficient R&D technology. Furthermore, with Industry 4.0 gradually moving from embedded systems towards the digital transformation of the industries, simulations can open the door to new ways of development and enhance the much sought-perfect cyber-physical system (CPS).
With the advent of computer simulations, robot development and research is moving away from machines with no or low-level intelligence towards a more autonomous, adaptable, flexible, and re-configurable system that can work efficiently with humans. With computer simulations, human collaboration with intelligent robots will be possible across various industries where the whole collaborative system will be efficient, sustainable, effective, and safe. And our approach of creating the computer simulation of human robots collaboration will be completed.
Combining simulation and AI technologies like Machine Learning & Deep Learning unveils outstanding new possibilities and opportunities. Moreover, the use of AI on traditional approaches to simulation may even bring forth a paradigm shift in the industry regarding how we perceive and develop the simulation.
Although simulation and Artificial Intelligence (AI) are two different technology paradigms, these technologies are related to each other in their primary forms. In computer engineering, simulation imitates an environment or a machine, while AI effectively simulates human intelligence.
While they may be related, simulation and AI were being used very differently with different mathematical and engineering approaches. However, in recent years, the development of AI-based simulations has experienced rapid growth in various industries.
For instance, now infamous, Cyberpunk 2077 used AI to simulate facial expressions and lip-syncing in the gaming industry. On the other hand, Microsoft Flight Simulator 2020 used AI to generate realistic terrains and air traffic.
Read more: Simulation in Digital Twin for Aerospace, Manufacturing, and Robotics
The power of AI to enable rapid simulation development with faster, more optimized, and less resource-hungry simulations even on a large scale would empower more applications of simulation technology in far wider industries and platforms.
However, to understand the benefits of using AI in simulations and its development, we need to understand the traditional simulation development approach and its use in this scenario at first.
Traditional Simulation vs. AI-based Simulations
The basic idea behind simulation development is to gather data related to the machine, environment or anything for different inputs and conditions. These data would then be collected, analyzed, and studied to understand how the machine/environment/anything simulated functions and behaves under different conditions and situations.
This understanding would then be used to build a basic mathematical model that can govern and imitate the actual object in different conditions, then used to construct a simulation model that can replicate or simulate the real thing.
However, when AI is used to build these simulation models, the AI has to be fed with data related to the object/environment's behavior and how these subjects (object/environment to be simulated) function under different conditions and settings. During this process, the AI model requires relevant data that can be considered a sample of the simulation subject and represents the subject properly.
Generally, Neural Networks (NNs) would be used as the AI model to be trained. After the training, this would simulate the subject and its behavior.
Both approaches, either traditional approaches or AI for simulation, have their advantages and disadvantages. One of the significant advantages of the conventional simulation method is that the mathematical model defined after studying the simulation subject can be reused and reconstructed easily.
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This allows other development teams to verify or reuse the same mathematical principles or models to generate the simulation. A traditional approach would also enable the developers to expand the simulation based on their understanding of the subject without explicit testing or proof test.
One of the significant disadvantages of this traditional approach remains to be its complex and resource-hungry process to generate the simulation. This is because everything has to be done by the simulation developers, who would also have to be experts in respective domains such that they need to understand the subject very closely.
Meanwhile, in AI-based simulation development, data is one of the essential components. The subject's information needs to be in abundant amounts and deterministic such that the data can represent the subject very closely.
This type of data may not be available readily when the data needs to be either collected or generated. But after the collection of accurate and abundant data, an AI-based/aided approach is very advantageous since there is no need to understand the subject by developers themselves.
Another significant advantage of the AI-based simulation holds within the power of AI to discover patterns or behavior in subjects not even considered or found by the developers. Apart from this, training an AI model usually takes a lot of time, but it may not be as resource hungry, complex, and costly as the traditional approach.
One of the significant disadvantages of the AI approach is that the model builder cannot be recognized or understood by developers in any way, so it cannot be usually reconstructed unless similar data or input is fed again to train the model.
Apart from this, due to the data required to qualify the model, expanding the model will generally be impossible without sufficient data.
Combining Simulations and AI
Using AI in simulation generation or development would enable data-powered development with rapid changeability and minimal human involvement. Although the simulation traits would be considered too complex for humans to develop, AI may easily reconstruct such characteristics if sufficient data is provided.
Due to this, AI can be used to simulate something too hard, complex, or time-consuming for humans in a short time without too much effort. Thus, not only would the development of simulations be faster, more productive, and easy, but AI would also enable the rapid iteration and tweaking of simulations that would be far less feasible, especially on a large scale.
We can open new doors by combining the power of AI and simulation for product design and development. Generally, without AI simulations, developers have to design a product/model that must be intensively tested before production, and changes are needed after the story. Then, the same process would have to be repeated.
This process is very resource-intensive. But through AI, design changes and validation can be easily tested through simulation, enabling rapid iteration and development.
The development and adoption of AI for simulation are far more required in industries like Augmented Reality (AR) and VR (Virtual Reality), where the sheer complexity of building high scale models, environments, and graphics through the traditional methods would be infeasible compared to using AI to develop and deploy simulations with its data-driven approach of development. The opportunities in AR and VR could be far more explored and matured through the AI to generate and develop simulations.
Alongside this, simulation of subjects like fluids (air and water) is brutal to construct with only a traditional approach, the result of which would still not be good and very close to reality. But with the help of AI, such simulations would be closer to reality and more refined.
One of the significant advantages of AI-based simulation compared to the traditional approach is that the conventional system would be significantly resourced heavy since it usually calculates each simulation particle.
However, AI-based simulation would enable such complex simulations easily since AI can perform these calculations/predictions much faster and less resource hungry. Alongside this, generative simulations like the generation of models, terrains in games, and product designs would also be possible with AI.
For instance, take the game Microsoft Flight Simulator 2020 as an example. This game allows gamers to experience realistic flights worldwide without lagging in the quality of models, terrains, and environment.
By traditional approach, this would mean that the game developers would have to model and build all terrains used in 3D along with matching landscapes and backgrounds to give the simulation a realistic feeling.
This would have cost the game developers a massive amount of time, resources, and a considerable number of experts to deal with complex problems lying ahead in such an enormous project. Realistically, such a project would not be feasible or even practically be possible to complete.
But through the use of AI, the developers used massive amounts of data that are already available and combined them with vast amounts of computation through the power of the cloud to train an AI model that could build realistic 3D models of terrains, environments, along with grasses, trees, and water-based upon the real world.
The results produced were pretty spectacular and received substantial critical acclaim from game developers and gamers alike.
Conclusion
By combining simulations and AI, we can unfold new opportunities and endless possibilities in different industries.
Along with technologies like Machine Learning and Deep Learning, AI-enabled simulations will be propelled by the data-driven backend. Conquering the disadvantages of the traditional approach to simulation, AI-based simulations will be able to push the boundaries of what simulations can do.
Even the most complex simulations, which would be next to impossible when developed with traditional methods, will be attainable by combining simulations and AI.
Moreover, with AI enabling rapid development of more optimized and improved quality, the industry may experience a revolution empowering next-level simulations with realism and details never seen before.
Simulation in the digital twin can help the aerospace, manufacturing, and robotics industries in many ways.
How many times have you bought a new product only to find out it's defective? It is a huge problem that has affected many people in the manufacturing industry, and with simulation, we can help! A digital twin can be used for testing products before they are released. Let me show you how this works.
Imagine we have an airplane manufactured by company ABC Corp, and they want to make sure that their new plane design will work well without any defects. To know if their plane is safe enough, they need a simulator that displays the different flight conditions to test for safety. If the makers detect any problems during this simulation, they can fix them before production begins, so no one gets hurt.
This blog post explores the importance of simulation in digital twin technology for aerospace, manufacturing, and robotics. We will discuss why simulation is essential to these industries and how we can use it to improve efficiency.
Let's get started!
Simulation in digital twin for Aerospace:
Aerospace companies have become more competent. They are using digital twins to eliminate unplanned downtime for engines and other systems. Today, airlines can keep their aircraft in service longer due to digital twins' warnings.
A digital twin is the computer model of how an asset behaves in the aviation world. It accounts for variables like weather and performance to predict outcomes. The virtual model also provides actionable advice on what to do if things go wrong based on simulated scenarios. This strategy has been so effective at airlines that aircraft are flying more hours than ever before!
Digital twins are capable of recommending mission adjustments that will decrease wear on equipment, thus increasing longevity and success rate for a given operation.
Data analytics are a vital component of digital twins and can predict when an asset will fail. The sensors receive the data in real-time on specific failure points.
The models make predictions and help determine how long the running equipment has left before needing replacement or repair. It saves companies both money and valuable resources like human labor that would otherwise go towards maintenance efforts if they were done manually instead of digitally predicted.
Read more: How Are Industries Creating New Opportunities By Combining Simulations and AI
Creating a digital twin is challenging without the necessary data. However, data about calibration details, the geometry of components, and mechanical assemblies could be enough for creating an effective model that will help improve quality assurance testing.
According to Aviation Today, "Boeing has been able to achieve up to a 40% improvement in quality of parts and systems it uses to manufacture its planes with the "digital twin." Essentially this means that before any aircraft component enters production, they are analyzed digitally using high-powered computers.
Imagine if you could test out how your new car will perform in any weather. Well, with digital twin replication that's possible! This virtual 3D model can go through a range of simulated environments like being underwater or enduring freezing temperatures - all before it ever leaves the assembly line.
Alongside these simulations are data fusion techniques that help gather information on an asset by combining different datasets such as images from sensors embedded into machines. Data fusion evolves alongside technological advances, keeping up-to-date with the piled-up data in volume, velocity, and variety. It can be crucial for businesses who want their products ready for anything life throws at them!
Data is the driving force in our industry. We produce an unimaginable amount of data every day, and it has to be processed by machines so that we can make sense out of it.
The flow from raw data to high-level understanding requires a complex fusion process at different levels: sensor-to-sensor, sensor-to model, and model--model fusion.
Designing a digital twin for one or more critical systems like airframe, propulsion & energy storage, life support, avionics, and thermal protection is recommended for success.
Digital Twin Simulation for Robotics:
For example, let's say you're building a machine that picks up parts from its bin. You want it to know where the function is and how big it is so your robot can grab them correctly without any mistakes or hiccups in production.
We need an algorithm trained by images of the items on top of our bins - which would then tell us what size each item was. We will also need a video feed captured by cameras positioned overtop these bins, giving us that cameras above images
Read more: How Digital Twins Can Help In Saving The Environment
A great example is bin-picking; people must manually place parts in many different configurations for a machine-learning algorithm to learn how it should pick up a part automatically.
This method is an example of supervised learning. When training a supervised learning algorithm, the training data will consist of inputted images paired with their correct outputs like bounding rectangles and labels describing what objects are in each image (e.g., "box," "can," etc.).
There's a lot to consider when you're teaching robots how to complete tasks. In addition to training them on what the job looks like, it also takes repetition before being trusted with delicate and potentially dangerous materials.
The robot must have had multiple rounds of practice for every task for its skill sets not only get better but continue improving overtime without any hiccups or errors that could lead to injury accidents down the line
A robust automation solution can take weeks and even months to converge, depending on the task. For example, a complex system will require more time than one which has few components. Additionally, some of your parts might be unavailable or still in production already - this could limit you from accessing them for training purposes.
"Digital Twin" is making significant leaps forward in industrial robotics, assisting manufacturers by not only setting up systems but also validating them for robust reliability using machine learning and integrated vision techniques. As a result, it can shorten the time taken significantly from months or years down to days.
In a virtual environment, the avatar replaces the real robot. So instead of spending all day in front of video screens and keyboards, it's now easy to do everything from your couch: launch a simulation on your computer and let the machine work for you!
In addition, the costs go down by about 90% because there are no lab fees or equipment setup charges.
Next, you bring your robotics into the physical world from the virtual.
The machine learning algorithm helps to learn what everyday objects and scenes look like when viewed by this device so that its actions are more in line with our expectations for how we would behave if given these inputs.
You can teach an old robot new tricks using AI-based facial recognition software!
Digital Twin: The Future of Manufacturing:
Digital twins are the future of manufacturing. With a digital twin, you can test and simulate before any mistakes happen with physical prototypes—saving time and money from costly errors that could have occurred through experimentation on materials or manufacturing processes.
In addition, manufacturers will never again risk releasing a defective product to market because they know what works beforehand thanks to their virtual representation by way of a "digital twin."
It is getting to market faster than their competitors is a challenge for companies. However, it can be possible with a digital twin as it cuts long steps shorter and reduces changes in production.
The product life cycle happens in the virtual environment where we can make all improvements much easier and quicker- perfecting efficiency and development time.
Imagine you have created this beautiful virtual prototype that has all the potential features. But, instead of wasting time test
One of the best features of digital twin technology is that it can help you predict problems before they happen. So, for example, every time one broke down, its virtual copy would start to analyze data from sensors to pinpoint any potential troubles.
It can solve many potential issues without any intervention from an operator by using predictive maintenance software that collects various sources of data through sensor readings to identify likely future complications with machinery. As a result, if you replace worn-out parts sooner rather than later, your manufacturing process will run more smoothly!
Conclusion:
Simulation in the digital twin is reducing costs for industries.
For example, ASME reported, a 2020 study says that up to 89% of all IoT platforms will have a digital twin in 2025, while nearly 36% of executives across industries understand the benefits, with half planning for implementation within just five years from now.
If you're not already familiar with the concept of digital twins, then it's time to get up-to-date. A digital twin is a virtual representation that mirrors an existing physical system in real-time.
In other words, if your company has a manufacturing plant and wants to find ways to be more productive by reducing costs or improving product quality, implementing a digital twin may help!
Robotic simulation is a type of simulation that helps developers create physical robots without
physical limitations such as resources, machines, space, and so on.
In this way, robotic simulation saves a lot of time and money because it allows people to test how robots work without huge investments.
Developers and designers use these simulations to test concepts, processes and use machine learning to teach robots how to interact in real-world environments.
Real-world resources like robots are limited. That's why people turn to robotic simulations where you can test an unlimited number of robots.
As a business grows, there will come a limit as to how much humans can do for it.
These limits are very clearly visible in manufacturing industries, where robots can sometimes be as five times as fast as humans.
Robots also tend to perform better than humans at mundane tasks because they are very precise by nature, and they don't get tired of repetitive work.
If you're looking to scale your manufacturing business, you will need to implement robots sooner or later.
And since every environment is different, you need to use robotic simulations to see what concept works best for you.
With Robotic Simulation, you're able to virtually test an endless amount of environments and robots, which helps you pick the perfect one before implementing it in your business.
As mentioned before, there aren't one-size-fits-all solutions.
Robotic systems are very complex, and what your robots need to do will differ from your competitors.
There are many challenges in applying robot-based automation in highly individualized environments. That's why many developers turn to machine learning to face these challenges.
This is one of the reasons we've turned to game engines and developed our simulation system Zero SIM with Unity.
Using game engines, it's much easier to create and simulate 3d environments and have our robotic systems adapt to unique environments.
Think it of as Gazebo meets Unity.
If you'd like to learn more and test our robotic simulation software Zero SIM. Click here and apply for the beta.
Robotic simulation helps you save thousands of dollars by fleshing out all of the potential flaws your system has.
It ensures that your robots won't accidentally destroy your materials or, even worse, hurt your workers.
The end result should match the robotic simulation exactly, and if you've developed the right simulation, it should go as smooth as butter.
That's why we're focused on creating realistic 3D environments that help you test like you would in the real world.
If you're dealing with super expensive robots, mistakes tend to be very expensive too.
You don't want your robot to be wearing out your parts either.
Even if you do everything correctly, you will still need to service the robot much earlier if you're constantly testing it physically while developing it.
Even though physical space is limited, in a simulation, no such limits exist.
You can easily test 100, 300, 500, or even more than 1000 robots at a time. This endeavor would be almost impossible without a simulation unless you have access to an unlimited amount of money.
Short answer: Any industries working in robotics.
Long answer. Whenever a robot needs perception, you need to test its perception skills in a robotic simulation.
If your robot incorporates artificial intelligence in any way, you need to use robotic simulation to test and teach it how to do complex tasks.
Here are some example industries that can use robotic simulation:
When you're starting out with robotic simulation, you don't want to hire an in-house team of artists and designers.
More often than not, these developers don't have as much experience or knowledge as outside contractors within the field of robotics.
If development, design and environment creation aren’t your specialty, it's better to outsource this part of the process.
And the best part is that outsourcing tends to be much cheaper than hiring a team.
But if you go down the route of creating robotic systems yourself, make sure that you're familiar with ROS (Robotic Operating System).
The big thing about robotic simulation is that it's mostly focused on the visual aspect of it.
However, there are a lot of other ways that robots can sense the world.
Sometimes sensors like LIDAR tend to be a lot more reliable than visual input.
LIDAR is a method for measuring distances by illuminating the target with a laser light and measuring the reflection with a sensor.
Touch sensors are amazing as well, yet often unfocused part of robotics. These sensors allow robots to determine the size, shape, and texture of an object, which is incredibly useful when dealing with robots that operate on complex tasks.
High visual fidelity things are great for showing off, but you're going to build much better robots if you put focus and effort into other sensors as well.
This is a subject that is prone to much discussion.
The length of a project depends on the complexity of it. As mentioned above, not all robots and robotic systems are made equal.
In our experience, it takes us an average of 3 months to build a Robot using machine learning combined with robotic simulations.
At Robotic Simulation Services, we're focused on providing top-notch robotic services for clients.
We develop a simulation from scratch in Unity, create the environments, import, and rig robots.
For example: If you only have a robot arm file, once you import it into Unity, You need to define physics, add motors, add joints and so on.
We provide all of these services to our clients.
If you're a developer, you can do everything we do with our Zero SIM software.
Robots have helped thousands of businesses scale their operations and their businesses.
What's important is that you test out your ideas in a simulated environment before going out and spending thousands of dollars on building these robots.
Do you currently use robots in your business? What tasks do they do?
Let us know in the comments below.
