Companies are swiftly adopting digital technology, with the whole industry moving towards the Fourth Industrial Revolution (FIR) or Industry 4.0. The ultimate end goal of almost all industries is to be self-sustainable, with automation at its core eventually like the Skyscraper Window Washing Robots. Subsequently, the industry has to adapt and integrate robotic technology in its operational process to reach this goal.
Robotics technology is rapidly evolving in both accessibility and usability. With this evolution, the technology is getting better and more usable, but the robotics market is also more valuable. Consequently, the robotics industry is currently one of the biggest markets of the technology paradigm.
In hindsight, the market also reflects this favorable shift of the industry towards robotics technology. As a result, researchers forecast that the global robotic industry market was more than 27 Billion US Dollars in 2020, with the other estimation that it will cross 74 Billion US Dollars by 2026. This increase in the market value represents an annual increase of more than 17%.
Furthermore, these figures will only increase in rate and estimation in the future post-COVID-19 pandemic era where work-from-home and remote technology is experiencing an enormous boost in development, accessibility, and adoption.
Robotics especially shines in industries where the work is either reparative or dangerous. Industries see this in retrospect in which various industries and production use robotic technology to achieve automation in repetitive and potentially dangerous tasks. For example, in the cleaning industry, skyscraper window cleaning is a hazardous task. With skyscrapers towering very high, the workers usually have to climb onto a platform that is hanging potentially even a hundred floors above with the support of a couple of wires.
Cleaning windows properly is just one of the problems when you are dangling hundreds of floors above the ground. With such high risk, the window cleaning industry rides on workers with steel nerves and a good cleaning capability. Although this business is lucrative, the lack of such workers and even companies that perform such risky jobs. The industry is also declining due to the same reason. With over 40 Billion US Dollars worth of market revenue every year, the window cleaning industry faces a lack of young talents to replace the old and trusty workers. The fact that above 74% of workers with training for such jobs are over 40 years old reflects this problem. Consequently, although a lucrative business, it's a dangerous job facing a severe lack of replacement workers.
One of the primary solutions to this problem is to remove workers from the task altogether. Therefore, replacing the window cleaning workers with robots is one of the possible solutions to eliminate human risk and increase efficiency and potential. Hence, window cleaning robots are growing in popularity in this business.
But to know how this all works, we first have to know about robots.
What is a Robot?
A robot is a programmable machine that can automatically perform specific tasks or take particular actions without requiring human assistance.
People usually imagine robots as machines with humanoid shapes with high intelligence, at least that is the depiction of robots in media and science fiction. But unlike robots in popular media and science fiction cultures, robots come in different forms, sizes, and uses. Furthermore, a robot is any machine with some level of processing power and can perform specific tasks without needing human intervention.
Read more: How DeepMind Is Reinventing Robotics!
For instance, the disk-shaped machine/device that cleans the floor automatically while moving on its own and avoiding obstacles is a robot. Similarly, various toy robots and robotic kits are already available in the consumer market. Drones are also a type of robot that can fly autonomously, balance themselves and follow directions from human operators. Apart from the consumer market, robots are also widely in use in industrial settings. The most widespread use of robotic technology and robots is seen in industries and production sites.
A robot is not a singular device or a machine; instead, it combines various components, systems, and incoherence to perform multiple tasks. These components include sensors, processors, storage systems, power supplies, mechanical parts like wheels, arms, chains, cameras, actuators, rotors, motors, etc. These components, devices, and systems work together efficiently to behave like a singular unit and perform various tasks with collaboration and communication.
With the advancement of technology, various systems, including sensors, processing power, battery power, storage systems, motors, actuator systems, and digital systems, are getting more modern and efficient. With the constant evolution of these components, they are increasingly getting complex. However, increasing complexity also increases the ease of use, efficiency, and capability of these components. The whole robotic engineering paradigm reflects this increase with robots getting smarter, more capable, and more efficient in performing various tasks and jobs with increasing levels of autonomy.
The Case of Skyscraper Window Washing Robots
Skyscrapers are, as their name reflects, very tall and usually stand over 150 meters high. On the other hand, mega-skyscrapers are well over 600 meters in height with more than hundreds of floors. These skyscrapers also require a vast amount of maintenance, including cleaning their windows. But these skyscrapers are so tall that regular cleaners cannot clean them. So they need professional window cleaners.
Professional window cleaners usually stand atop a platform hanging beside skyscrapers and are controllable by a crane. This crane can take them downwards or upwards and sideways across the building. Although the media onto which the workers stand to have railings for safety, it still hangs above hundreds of floors above where one small mistake or mishap can end horribly. Besides these factors, the weather is also a significant factor that can increase the risk of window washing, especially if it is a windy season. So it is a pretty risky job from every corner possible.
Moreover, besides the danger of hanging beside such tall buildings, window cleaning is also a challenging job, with few workers even willing to climb onto the platform that's dangling hundreds of meters above the ground. This same case is why this business is so lucrative in the first place. Unfortunately, this is also becoming why new recruitments are getting complicated. With over 74% of the trained professional window washers being over 40 decades old, the replacement rate with young blood is thin.
Even if one does not fear heights, the job has a significant risk of losing their life, making it unattractive to many. The risk factor is very unfavorable with humans on the scale.
How Robots Make Skyscraper Window Washing More Safe?
Right off the bat, when robots are the ones cleaning the skyscraper windows, we can eliminate the risks of having humans on platforms besides the skyscrapers. When robots are replacing almost every human labor, it is essential to look into this factor where the risk of losing life is more than human labor. It will significantly reduce the risks along with having massive leverage if something does go wrong. Thus, we can remove the heavyweight of having potential dangers for humans.
With robots, maintenance along with cleaning is straightforward to perform. With the advancement in remote technology or even autonomous technology, window cleaning robots can leverage this by being controllable by humans or even independent at their tasks. With robots, workers can permanently bolt or fix the robots onto the lift mechanisms, significantly reducing the time consumption that would otherwise be used in checking harnesses or straps for human workers. It will reduce the turnaround times between jobs and save time significantly.
Not only will window cleaning become safer, but it will also become more efficient and fast while consuming fewer resources. Another significant advantage of using window cleaning robots is the economic benefit. These robots can work at almost any condition without stopping and even multiple robots for faster turnaround times between different jobs. As a result, it will undoubtedly bring more returns from the investment.
It will increase the work capacity of window cleaning companies and make the whole gig more economical for consumers. It will also be fascinating for the skyscraper owner to see the robot cleaning the window rather than being guilty of risking human life. Meaning it will attract more consumers and even less time between the cleaning cycles. It will increase the market value and revenue of the whole industry altogether.
FS Studio, therefore, provides robotic services like Offline Robotic Programming or even robot training and software development that can cater to the window cleaning business. FS Studio’s collective experience and knowledge from decades of research and development with solutions like Robotic Simulation Services alongside emerging technologies like AR and VR.
With expertise in Artificial Intelligence and technologies like Machine Learning (ML) and Big Data, FS Studio provides intuitive solutions for product development and innovative R&D technologies. FS Studio offers cutting-edge solutions for present problems and issues. It also empowers its clients with solid solutions that will also help them solve and tackle future challenges.
Skyscraper window washing robots are a massive step for window washing companies. They are safer, efficient, and cost-effective on top of enabling new opportunities and possibilities not only in end jobs but also on business fronts. With the Industry 4.0 approach, industries are transforming themselves towards digital technology to strive for automation. This goal relies heavily on robotic technology that enables intuitive solutions like skyscraper window washing robots.
With the evolution of simulations and 3D tech, innovative technologies are starting to emerge. Digital Twin is an emergent technology gaining massive momentum in the industry. As the Fourth Industrial Revolution comes closer, digital twins’ technologies are maturing and evolving rapidly, increasing the utilization of practical applications of digital twins.
Moreover, with the incorporation of technologies like Artificial Intelligence (AI), Machine Learning (ML), or Big Data, companies are converging digital twin technology with emerging technologies like Augmented Reality (AR) and Virtual Reality (VR). As a result, it enables rapid design and development and allows smart solutions in production, sales, logistics, and the global supply chain.
Digital twins are a massive boon for rapid prototyping during the design and development of a product. Furthermore, due to the ability to enhance current manufacturing & product development, industries worldwide are incorporating digital twin technology in their business, product development, and even consumer experience. The current global digital twin market sits at 5.4 Billion US Dollars, but this slump is due to the COVID-19 pandemic shutting down many industries and production along with it. As a result, the world was simply not ready to adopt it rapidly.
However, with adaptation, digital twin technology is rapidly rising in applicability and usability and increasing accessibility even at the end-user side. With this in hindsight, researchers predict that the global digital twin market will cross 63 Billion US dollars by 2027. This estimation shows a high annual growth rate of 42.7%. Furthermore, it shows that the market, industries, and even consumers are moving towards the much-awaited digital transformation of Industry 4.0.
Read more: Reduce Costs with IoT and Digital Twins
To understand the practical applications of digital twins, we first have to understand the technology itself.
Know Your Technology: Digital Twins
Digital twins technology is evolving in both its technological reach/sophistication and its meaning. While the idea of digital twins is not new, it is undoubtedly going through a massive revelation in the industry currently. Furthermore, with technologies like 3D models, simulations are rising. As a result, digital twins are also gaining momentum in the industry.
The digital twin accurately represents a real-world physical object or an environment in a digital form. Do not confuse digital twins with 3D models or simulations. It is much more than that. Digital twins represent a subject (any object in the real world) not just in a static manner but in a dynamic way. It means that the digital twin will always represent the product/object throughout its lifecycle. The twin always reflects any change or modification on the real-world object or vice versa, in which the real-world object demonstrates a shift in the digital twin.
While 3D models just simulate some properties and structure of an object, the digital twin represents and accurately reflects all properties and characteristics of the real world. From design, materials, behaviors, and properties, the digital twin represents them all. So it becomes easier to reflect changes of both the digital twin and the real object. Furthermore, it remains accurate throughout the whole design phase, developmental phase, prototyping, or even after production for maintenance or repair, effectively reflecting all stages of a product.
Furthermore, unlike a 3D model, which is just an informational model, digital twins react and behave in a certain way similar to the real object in different environments and conditions. Due to this, the digital model is more dynamic and adaptive. Moreover, with AI at its core, digital twin technology enables communication, updating, and even learnability similarly to its real-world counterpart through the exchange of data among each other.
With technologies like AI with ML or data analysis, digital twins are becoming more accurate and smart. It also enables more flexible product phases for the design and development of a product. They help product developers explore different solutions freely without concerns relating to physical material costs or loss. Companies worldwide are rapidly adopting digital twin technology, enabling various applications and use cases to arm themselves with this type of revolutionary technology.
Here, we list some of these potential uses and practical applications of digital twins technology as shared by 13 different tech experts of the Forbes Technology Council.
1. To calculate product performance statistics and measures
Michael Campbell from PTC shares that with innovations enabling digital twins to be a comprehensive digital equivalent of a product or process in the real world, product developers or manufacturers can understand how the product is in use or performing. They can even track if the product or supply line may break down or is low in supplies. Campbell remarks that all this can lead to a better experience for the end consumer.
2. Simulating complex manufacturing scenarios
Eugene Khazin from Prime TSR remarks that digital twins have great use in the form of a precise virtual representation of a production supply chain. It will use advanced analytics and machine learning systems to predict and simulate different complex “what-if” scenarios without running these in actual production. As a result, manufacturers and production sites will utilize resources more efficiently and accurately to increase product quality.
3. Removing risks from different experimentations and analysis
Kathleen Brunner from Acumen Analytics Inc states that digital twin technology is a game-changer saying that it can eliminate the need to perform various experiments and studies with actual equipment or processes. Digital twins offline can enable multiple investigations of various complex and what-if analyses of different scenarios. Practical applications of digital twins allow optimization of other parameters and outputs with a digital representation or replica interface that responds to human and environmental inputs. These digital experiments significantly de-risks these physical experimentations by deeming them unnecessary.
4. Improving software products
Vince Padua from Axway explains that one way for the practical application of digital twins is to leverage actual customer usage data. This data can improve enterprise software products through its analysis. The data collection can include whether users are using a particular feature and how they receive notifications or collaborate with other users. Developers can create a digital twin of the customer experience using this data, while Artificial Intelligence can determine and predict the fastest and most efficient ways to solve various issues.
5. Real-Time information sharing and analysis
Gerald Rousselle from One Concern shares that digital twins can produce new functionalities since they represent the physical world in a form that computers can understand. He says that a GPS in mobile can be a digital twin of the natural world to provide accurate and real-time direction and navigations to your destinations.
6. Creating valuable digital assets
Ghufran Shah from Metsi Technologies Ltd explains that there is a lot of hype around cryptocurrency and non-fungible assets/tokens or NFTs. He clarifies that NFTs are a way to represent a physical asset such as a picture, video, or even a music clip in a digital format. Once a physical object is mapped into an NFT, a unique identity of this asset can now live forever within the blockchain. These assets can even gain monetary value and become valuable collectible.
7. Facilitating hybrid teaching methods
Zeng Fan from the University of Miami Herbert Business School says that the schools and universities are equipping classrooms to accommodate virtual conferencing tech for virtual teaching due to the pandemic. This technology is similar to one of the practical applications of digital twins, face-to-face and digital/virtual class deliveries. This technology can also be in use for recording asynchronous digital course content.
8. Improving vehicle safety
Stefan Kalb from Self Engine explains that it's costly to use real cars and crash test dummies to get actual life data about car crashes, potentially saving lives. If digital twins technology is used, it can collect sensor data from inside a car as in the real world. This data, over time, can go through analysis and study and perform numerous cost-effective and efficient car crash simulations. These simulations can provide data that can improve the safety of real-live cars.
9. Supporting sustainable clothing practices
Julia Dietmar from Vue.ai explains that an excellent example of digital twin technology can be a “digital passport” for different pieces of clothes that are manufactured. Such “passports” can contain various information such as product attributes, raw materials, factory information, and even previous owner information. It can prove to be very useful for sustainable clothing practices.
10. Collecting and providing input for databases
Vitaly Kleban from Everynet says that the lack of ML and data analytics data is a genuine concern, even putting multimillion-dollar investments at risk. But digital twins can serve as an interface between real-world hardware and sensors to collect data from the physical world. The practical applications of digital twins can even prove to be a key to providing enough data for ML systems.
11. Preventing sports injuries and enhancing athletic performance
Laurie McGraw from AMA explains that the NFL has a digital twin for every player through field cameras and sensors. It can recreate every move or body posture of the players. This level of sophistication has huge potential regarding injury prevention and even improving player and game performances. These types of data and information can prove to be very useful for more than just elite athletes.
12. Providing personal assistance
Kerrie Hoffman from getting Digital Velocity and Focal Point Business Coaching state that smartphones are already digital twins of every person. Smartphones are already acting as our digital twins since they provide various functionalities like “Swipe to Pay '' when entering a coffee joint or providing alternate routes when there is a traffic jam ahead.
13. Optimizing traffic flows
Joaquin Lippincott from Metal Toad explains that practical applications of digital twins in the transportation sector are enormous. With smart vehicles and smart cities, planning and real-time adjustments to traffic are possible, optimizing traffic flows and saving time. Such technology may be dangerous, but we can test, optimize, and later implement such technology much more safely with digital twins.
Artificial Intelligence (AI) is a transformative technology. Not only can it enable autonomy and machines that can make intelligent decisions, but it can also even reinvent the technological wheels of various industries. Robotics, being an emergent technology to enable autonomy, AI is a beautiful tool that can help flourish the true capability of robotics technology. And Google's AI partner, DeepMind is reinventing robotics once again.
Today, AI is around us everywhere. From different apps to different devices/gadgets and various services we use, AI mainly integrates with these apps, devices/gadgets, or services. With this, AI provides us a superior experience of use with devices capable of making intelligent decisions and predictions. Moreover, AI is very persistent in modern life, with AI in various voice assistants, recommendation systems in services from e-commerce sites to media consumption platforms, and intelligent solutions to make predictions or autonomous decisions.
With these services and devices, AI has already become an integral part of our lives. Therefore, it is only natural that industries and companies use AI to boost their company performance on the consumer and product development and innovation front in such a scenario. One of these industries where AI has much potential is the robotics industry.
The robotics industry in itself is revolutionary, with capabilities to enable autonomy in industries. However, the endeavors of enterprises and various industries pose a massive challenge for robotics to fulfill them alone. So developers and researchers worldwide are trying to embed AI into robotics technology to usher the robotic industry to a new level.
With the help of AI, robots will not only be intelligent, but they will also be more capable and efficient. They will be able to form elegant solutions and make intelligent decisions. Moreover, they will be able to control and move a physical body which is very hard to program and build from the ground up. Furthermore, with the decision-making and prediction prowess of the system with convergence of robotics and AI, revolutionary and even unseen developments are possible.
DeepMind is reinventing robotics, and its developers have certainly caught up with this revolutionary possibility. The search giant Google's AI partner, DeepMind, is now working on this problem of convergence of AI with robotics. Raia Hadsell, the head of robotics at DeepMind, said, "I would say those robotics as a field is probably ten years behind where computer vision is." It demonstrates the lack of distinct development in robotics even when tech-like computer vision embedded in robots is already very far ahead.
The problem lying here is, though, more complex. Alphabet Inc, the parent company of Google and DeepMind, understands this daunting AI incorporation with robotics. More daunting challenges and longstanding problems remain in the Robotics-AI paradigm alongside challenges of gathering adequate and proper data for various AI algorithms to train and test them.
For instance, problems like training an AI system to learn new tasks without forgetting the old one? How to prepare an AI to apply the skills it knows for a new task? These problems remain primarily unsolved, but DeepMind is reinventing robotics to tackle the issues.
DeepMind is mainly successful with its previous endeavors with AlphaGO, WaveRNN, AlphaStar, and AlphaFold. However, with various breakthroughs and revolutionary developments, DeepMind is now turning towards these more complex problems with AI and Robotics.
However, a more fundamental problem remains in robotics. With their AlphaGO AI, DeepMind is reinventing robotics and successfully trained it through the data from hundreds of thousands of games of Go among humans. Apart from this, additional data with millions of games of AlphaGO AI playing with itself was also in use for its training.
However, to train a robot, such an abundance of data is not available. Hadsell remarks that this is a huge problem and notes that for AI like AlphaGO, AI can simulate thousands of games in a few minutes with parallel jobs in numerous CPUs. However, for training a robot, for instance, if picking up a cup takes 3 seconds to perform, it will take a whole minute to just train 20 cases of this action.
Pair this problem with other problems like the use of bipedal robots to accomplish the same task. You will be dealing with a whole lot more than just picking up the cup. This problem is enormous, even unsolvable, in the physical world. However, OpenAI, an AI research and development company in San Francisco, has found a way out with robotic simulations.
Since physically training a robot is rigid, slow, and expensive, OpenAI solves this problem using simulation technology. For example, the researchers at OpenAI built a 3D simulation environment to train a robot hand to solve a Rubik's cube. This strategy to train robots in a simulation environment proved fruitful when they installed this AI in a real-world robot hand, and it worked.
Despite the success of OpenAI, Hudsell notes that the simulations are too perfect. She goes on to explain, "Imagine two robot hands in simulation, trying to put a cellphone together." The robot might eventually succeed with millions of training iterations but with other "hacks" of the perfect simulation environment.
"They might eventually discover that by throwing all the pieces up in the air with exactly the right amount of force. With exactly the right amount of spin, that they can build the cellphone in a few seconds," Hudshell says. The cellphone pieces will fall precisely where the robot wants them, eventually building a phone with this method. It might work in a perfect simulation environment, but this will never work in a complex and messy reality. Hence, the technology still has its limitations.
For now, however, you can settle with random noise and imperfections in the simulations. However, Hudsell explains that "You can add noise and randomness artificially. But no contemporary simulation is good enough to recreate even a small slice of reality truly."
Furthermore, another more profound problem with AI remains. Hadsell says that catastrophic forgetting, an AI problem, is what interests him the most. It is not only a problem in robotics but a complexity in the whole AI paradigm. Simply put, catastrophic forgetting is when an AI learns to perfect some task. It tends to forget it when you train the same AI to perform another task. For instance, an AI that learns to walk perfectly fails when training to pick a cup.
This problem is a major persistent problem in the Robot-AI paradigm. The whole AI paradigm suffers from this complexity. For instance, you train an AI to distinguish a dog and a cat through computer vision using a picture. However, when you use this same AI to prepare it for classification between a bus and car, all its previous training becomes useless. So now it will train and adjust its "learning" to differentiate between a bus and a car. When it becomes adept in doing so, it may even gain great accuracy. However, at this point, it will lose its previous ability to distinguish between a dog and a cat. Hence, effectively "forgetting" is training.
To work around this problem, Hadsell prefers an approach of elastic weight consolidation. In this approach, you task the AI to assess some essential nodes or weights (in a neural network). Or "learnings" and freeze this "knowledge" to make it interchangeable even if it is training for some other task. For instance, after training an AI to its maximum accuracy for distinguishing between cats, dogs, and you, task the AI to freeze its most important "learnings" or weights that it uses to determine these animals. Hadsell notes that you can even freeze a small number of consequences, say only 5%, and then train the AI for another classification task. This time says for classification of car and a dog.
With this, the AI can effectively learn to perform multiple tasks. Although it may not be perfect, it will still do remarkably better than completely "forgetting," as in the previous case.
However, this also presents another problem: as the AI learns multiple tasks, more and more of its neurons will freeze. As a result, it would create less and less flexibility for the AI to learn something new. Nevertheless, Hudsell this problem is also mitigable by a technique of "progress and compress."
After learning new tasks, a neural network AI can freeze its neural network and store it in memory/storage to get ready to learn new jobs in a completely new neural network. Thus, it will enable an AI to utilize knowledge from previous tasks to understand and solve new tasks but will not use knowledge from new functions in its primary operations.
However, another fundamental problem remains. Suppose you want a robot that can perform multiple tasks and works. In that case, you will have to train the AI inside the robot in each of these tasks separately in a broad range of scenarios, conditions, and environments. However, a general intelligence AI robot that can perform multiple tasks and continuously learn new things is complex and challenging. DeepMind is reinventing robotics and now working continuously to solve these AI-Robot problems. Like DeepMind, FS Studio is also hard at work with its collective experience and knowledge over decades. FS Studio is also improving its services like Robotic Simulation Services, Offline Programming, and Digital Twins for reinventing the paradigm of robotic research and development with AI at its center.
The industry swiftly moves towards the Future Of Industrial Robotics, i.e., the Fourth Industrial Revolution (FIR). With this, industries and production plants are moving towards digital technology and probably reaching an efficient automation solution. To pursue this goal, industries are marching to develop their enterprises and production sites with robotic technology.
Robotic technology is increasing in sophistication and complexities. It is also going through a vast evolution of its use case and efficiency. In earlier times, where robots were much slower, inefficient, and less capable. While with modern technology, robotics now progresses much faster, with more efficient and competent robots. The robotic market is blooming with the advancement of sensors, communications technology, processing technology, storage systems, battery technology, and electronic components’ general efficiency and power.
This boom of the robotic market is not only for the industrial robotic paradigm, but even the common consumer market is experiencing this boom, its in pair with the increasing availability, accessibility, and ease of use of robotic technology at all sides. Consequently, today robots are not only available for industries and production plants but also in the general consumer space. Hence, the robotic industry itself is rising as a capacity market.
The robotic industry is growing at an unprecedented rate. With demands soaring through the sky, the robotic industry swiftly rises as one of the biggest markets. One of the most significant demands of robotic technology and robots is the industrial market with various industries and production sites. And since several types of industries currently “run” the world and robotic technology are at the forefront of these industries, it's one of the essential cornerstones of our future world.
With various robotic companies researching and developing innovative solutions and technologies within robotics, they are also propelling numerous industries towards success, efficiency, and even the future of the industrial robotics market, Industry 4.0. Furthermore, with robots simplifying and increasing the capabilities of industries, the robotic industry is also experiencing rapid growth. Consequently, researchers expect that the industrial robotic services alone will cross 4 Billion US Dollars in market value.
FS Studio is an innovative company that provides various state-of-the-art digital technology services like Robotic Simulation Services, Offline Programming, AI, AR, and VR. It also monitors the industry closely to prepare itself and its solutions to excel more in the future and essentially be future-proof. Reports from this monitoring help our clients and partners to identify various companies and opportunities lying within them. Furthermore, it will help them plan out different strategies regarding the robotic services they are planning to get and optimize their market position and plans.
Following are the ten companies that will dominate the industrial market in robotics.
With its establishment of robotic systems in 1980, Mitsubishi Electric has possibly been the leader in industrial robotics with automation since then. The company provides its services with a wide range of robotic systems and automation solutions that help to improve productivity and efficiency around the industry. It also specializes in high-speed and precision performance systems in the industry. Mitsubishi Electric provides RH-CH compact Selective Compliance Assembly Robot Arm (SCARA) and articulated arm robotic systems and provides delta style robots from leader innovator or pick and place robots, Codian Robotics. Mitsubishi Electric generally provides lightweight and value-for-money robot systems with reasonable costs and a warranty of about three years in a robot purchase.
(ABB) ASEA Brown Bover
ABB, or ASEA Brown Bover, is a robotic company with an international reach in over 100 countries. With its establishment in 1883, ABB remains to be a leader in robotic technology innovation. ABB also holds the prestige to be a company to pioneer the first electric microprocessor-controlled robot and be the world’s first company to produce an industrial paint robot. It has sold over 300 thousand robot units worldwide by 2019 and remains a multinational robot company with expertise in the automation and motion department. Some of its essential products include FlexPainter robots (IRB 5500-22), Pre-machining robots (IRB 6660), Press tending robots (IRB 6660), Dual-arm robot YuMi (IRB 14000), and SCARA robots (IRB 910SC).
B+M Surface Systems GmbH
In 1992, B+M Surface Systems had a high reputation for automation systems and remained the leader in high-quality painting plants with automation and different surface application systems. As its name implies, it's a world-leading robotic company in surface design and painting, all from design and installation to maintenance and support. They help their customers in all fronts of robot technology usage with high customization for their customers. It is a leader in surface painting robots with products like Painting Robots with its T1 X5 Series robots and Adhesive Dosing Systems with its T2 X5 series robots.
In 1915, Yaskawa led its journey towards the robotic industry by releasing the all-electric industrial robot, Motoman, in 1977. Since its release, Yaskawa has sold over 300 thousand units of Motoman, which is an all-electric industrial robot. Yaskawa remains the leader in applications like welding, packaging, assembly and material removal, material cutting, and dispensing. It has sold vast numbers of products, including over 18 million inverters and 10 million servos. Its essential products include Arc Welding Robot with VA1400, Assembly Robots with HP20F, Pick and Pack robot with its G series robots, and Spot Welding with MH255.
Omron Adept Technologies
In 1948, Omron Adept Technologies was the leader in guidance systems with computer vision systems. It excels in designing and manufacturing these robots. It is also the largest robotics company that is based out of the USA. It usually provides cost-effective robotic solutions with the integration of various use-cases and automation.
Omron Adept Technologies also includes application software solutions with automation systems/equipment and mobile robots. Some of the critical robot products from Omron include Hornet, Cobra, eCobra, and Delta robot systems.
FANUC is a leader in integrating Artificial Intelligence systems in robots and providing a wide range of industrial applications with robots of over 100 different models. It works on dynamic and smart solutions with AI integration and maintains its competitive edge with great flexibility. The FANUC robots are generally easy to operate, smart, and provide dynamic solutions. Some of the best FANUC robotic solutions include articulated robots of M-20B/25 series, collaborative robots with FANUC CR series, robots of R2000iC series, SCARA robots, and delta-style robots with M-1/2/3 series.
Kuka is a German company that leads the robotic industry with its automated range of fully customizable software solutions with integrated robots with control technology and embedded automation systems. With its foundation going back to 1898, its focus and dedication to automation with robotics began in 2004 when the company either sold or closed other non-core departments to shift its primary focus towards automation and robotics. Some of the critical robotic systems of Kuka include Press-to-Press robots, Palletizing robots with its QUANTEC robots, some shelf-mounted robots, AGILUS robot system, which is a hygiene machine variant, and its KR AGILUS series, including KR 30 and 60 F series robots.
EPSON is probably prominently known for its printing solutions in small printers. But the company was initially known for its automation systems and later became a major company dealing with various manufacturing sites with its different machinery solutions. Robotic technology from Epson excels in automation with several compact SCARA robots, PC-based and controlled robots. Its main products include G-series robots, SCARA with T-Series robots, and LS and RS series robots.
In 1896, Kawasaki became a leading robotic technology company with over 160 thousand robot systems sold and installed. Kawasaki was thought to be the future of industrial robotics in Japan as it was the first company to commercialize industrial robots in Japan. It also pioneered and highly contributed to industry robot popularity and integration of various labor-saving systems and solutions. One of the most prominent products of Kawasaki has been its SCARA robot, duAro, which is a dual-arm robot with human collaboration capabilities. Some of its critical robotic products include Painting solutions with its K Series robots, Pick-and-Place robotic solutions with its Y-series robots, B-series robots for Spot welding, M series robots for medical and pharmaceutical solutions, and duAro SCARA robots.
Though with a wide range of solutions, Staubli excels in Robots, Textiles, and Connectors. From its inception in 1892 in Switzerland with the textile business, Staubli began its industrial robotic journey devoted to quality engineering and factory floor solutions. Currently, Staubli also provides connector solutions with its expertise in both fluid and electrical connectors. With its accession of Unimation, Staubli is firm in its position towards being a most innovative industrial robotic solution provider company. It also provides various software solutions and various collaborative robots. Some of the significant products of Staubli include its RX series robots, TX2 series robots, CS series robots, TS80 robots, and TP90 robots. Various industries and production facilities are looking to invest in robotic technology. The whole industry is marching towards automation and its digital transformation to prepare itself for the future of industrial robotics with Industry 4.0. The robotic industry sets the path for these industries with its innovative robotic solutions and automation solutions. And these companies will undoubtedly be at the forefront when the industry sets its foot into this new landscape of Industry 4.0.
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.
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.
Read more: Are You Still Manually Teaching Robots?
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 chip giant NVIDIA and Open Robotics partnership may mark a significant stride in the robotics and Artificial Intelligence industry.
NVIDIA is one of the most potent entities for chips manufacturing and computer systems, along with Open Robotics being a giant in the robotics space. This partnership brings these two giants together to develop and enhance Robot Operating System 2 (ROS 2).
As put forth by Chief Executive of Open Robotics, Brian Gerkey, users of the ROS platform were using NVIDIA hardware for years for both building and simulating robots. So the partnership aims to ensure that ROS2 and Ignition will work perfectly with these devices and platforms.
ROS is not a new technology. From its inception in 2010, ROS has been a vital source of the developmental platform for the robotics industry. Also supported by various big names like DARPA and NASA, ROS is an open-source technology that combines a set of software libraries, tools, and utilities for building and testing robot applications. ROS2 is the new version with many improvements upon the old ROS and was announced back in 2014.
However, Open Robots’ Ignition simulation environment primarily focused and targeted the traditional CPU computing modes over these years. Conversely, on the other hand, NVIDIA was pioneering and developing AI computing and IoT technology with edge applications in their Jetson Platform and SDKs (Software Development Kits) like Isaac for robotics, NVIDIA toolkits like Train, Adapt, and Optimize (TAO). All this simplifies AI development and deployment of AI models drastically.
Read more: Are You Still Manually Teaching Robots?
NVIDIA was also working on Omniverse Isaac Sim for synthetic generation of virtual data and simulation of robots. Jetson platforms are open source and are available to developers. But now, with its combination with the Omniverse Issac Sim, developers will be able to develop physical robots and train them using the synthetic data simultaneously.
The NVIDIA and Open Robotics partnership majorly focus on the ROS2 platform, and it’s boosting its performance on the NVIDIA Jetson edge AI and its GPU-based platforms. The partnership primarily aims to reduce development time and performance on various platforms for developers looking to integrate technologies like computer vision and Artificial Intelligence (AI) and Machine Learning (ML), and deep learning into their various ROS applications.
Open Robotics will improve data flow, management, efficiency, and shared memory usage across GPUs and other processing units through this partnership. This improvement will primarily happen on the Jetson edge AI platform from NVIDIA.
This Jetson Edge platform is an AI computing platform and is mainly a supercomputer-based platform. Furthermore, Isaac Sim, a scalable simulation application for robotics, will also be interoperable with ROS1 and ROS2 from Open Robotics.
The NVIDIA and Open Robotics partnership will work on ROS to improve data flow in various NVIDIA processing units like CPU, GPU, Tensor Cores, and NVDLA present in the Jetson AI hardware from NVIDIA. It will also focus on improving the developer experience for the robotics community by extending the already available open-source software.
This partnership will also aim that the developers on the ROS platform will be able to shift their robotic simulation technology between Isaac Sim from NVIDIA and Ignition Gazebo from Open Robotics. It will enable these developers to run even more large-scale simulations with the enablement of even more possibilities. As put by the CEO of Open Robotics, Operian Gerkey, “As more ROS developers leverage hardware platforms that contain additional compute capabilities designed to offload the host CPU, ROS is evolving to make it easier to take advantage of these advanced hardware resources efficiently.”
It implies that developers will openly leverage processing power from different hardware platforms with more powerful, low-power, and efficient hardware resources. So, for example, ROS can now directly interface with NVIDIA hardware and take its maximum advantage, which was hard to do before.
The NVIDIA and Open Robotics partnership also put forward possibilities of results to come out around 2022. With a heavy investment of NVIDIA towards computer hardware, modern robotics can now utilize this hardware for enhanced capabilities and more heavy AI workloads. Furthermore, with NVIDIA's expertise in inefficient data flow in hardware like GPU, the robotics industry can now utilize this efficiency to flow large amounts of data from its sensors and process them more effectively.
Gerkey further explained that the reason for working with NVIDIA and their Jetson Platform specifically was due to NVIDIA’s rich experience with modern hardware relevant to modern robotic applications and efficient AI workloads. The head of Product Management, Murali Gopal Krishna, also explained that NVIDIA’s GPU accelerated platform is at the core of AI development and robot applications. However, most of these applications and development are happening due to ROS. Hence it’s very logical to work directly with Open Robotics to improve this.
This NVIDIA and Open Robotics partnership also brought some new hardware-accelerated packages for ROS 2, aiming to replace code that would otherwise run on the CPU, with Isaac GEM from NVIDIA. These latest Issac GEM packages will handle stereo imaging and color space conversion, correction for lens distortion, and processing of AprilTags and their detection. These new Issac GEMs are already available on the GitHub repository of Nvidia. But it will not include interoperability between Isaac Sim from NVIDIA and Ignition Gazebo from Open Robotics as per expectations of it arriving in 2022.
Meanwhile, though, the developers can explore and experiment with what's already available. The simulator on GitHub already has a bridge for ROS version 1 and ROS version 2. It also has examples of using popular ROS packages for navigation and manipulation through boxes nav2 and MoveIT. While many of these developers are already using Isaac Sim to generate synthetic data for training perception stacks in their robots.
This latest version of the Isaac Sim brings significant support for the ROS developers. Along with Nav2 and MoveIT support, the new Isaac Sim includes support for ROS in ROS April Tag, Stereo camera, TurtleBot3 Sample, ROS services, Native Python ROS support and usage, and even the ROS manipulation and camera sample.
This wide range of support will enable developers from different domains and fields to work efficiently in robotics. For example, developers will quickly work on domain-specific data from hospitals, agriculture, or stores. The resultant tools and support released from the Nvidia and Open Robotics partnership will enable developers to use these data and augment them in the real world for training robots. As Gopala Krishna put it, ”they can use that data, our tools and supplement that with real-world data to build robust, scalable models in photo-realistic environments that obey the laws of physics.” He claimed with the remark that Nvidia would also release pre-trained models.
On the remark about performance uplift in these perception stacks, Gopala Krishna said, “The amount of performance gain will vary depending on how much inherent parallelism exists in a given workload. But we can say that we see an order of magnitude increase in performance for perception and AI-related workloads.” Nvidia’s Gopala Krishna also remarked that the program would increase performance and much better power efficiency with appropriate processor use for an acceleration of different tasks.
Gopala Krishna also noted that Nvidia is working closely with Open Robotics to streamline the ROS framework for hard accelerations. The framework will also see multiple new releases of its hardware-accelerated software package, Isaac GEM. Some of these releases will focus on robotics perception, while further support for more sensors and hardware will arrive on the simulation technology side. The release will also contain samples that are relevant to the ROS community.
This development will aid the growing market of robotics. Especially after the COVID, the growth of the robotic market seems to skyrocket, with more and more industries and companies lining up to use and adopt robotics, from manufacturing and production lines to health care and agriculture usage.
Nvidia and Open Robotics partnership will see the advancement of AI and technologies like Machine Learning and Deep Learning at a rapid pace now with the support of NVIDIA hardware in robotics. Researchers estimate that the global robotics market will cross 210 Billion US Dollars. This estimate is likely to increase with the rapid development of AI and technologies like semiconductor technology, sensors, networking technology with 5G.
This collaboration between Nvidia and Open Robotics will only add valuation to this market with innovative platforms like Nvidia Isaac and ROC, helping developers develop more efficient, robust, and innovative robots and robotic applications.
It will also help the open-source community of robot development since this partnership brings together two of the most significant robotics development communities with ROC and Nvidia Isaac. Furthermore, FS Studio collaborates with this growing community to release its robotic simulation solution, ZeroSim, alongside the Nvidia and Open Robotics partnership. Thus, it will help the development bring together with collaboration and push the robotic development further. Now with the dawn of Industry 4.0, companies are moving towards digital technology. This movement can be seen with industries adopting digital solutions with robotics in different fields from production and manufacturing to the board paradigm of human-robot collaboration possibilities.
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.
Robots are complex pieces of machinery. Robots are engineering marvels that enable different components and systems to help with higher functions and features. These components and systems are usually very complex and require much research and development with time, resources, and specific skills. Furthermore, integrating these components is difficult, and the robot programming platform conquers it well.
With the advancement of technology, various systems, including sensors, processing power, battery power, storage systems, motors, actuator systems, and digital systems, are getting more modern and efficient. With the constant evolution of these components, they are increasingly getting complex. However, increasing complexity also increases the ease of use, efficiency, and capability of these components. Nevertheless, the integration of these components is the hardest part.
Robots with specific use cases, more movement points, locomotion capabilities, and robots that perform specific tasks with great accuracy and repetition are even more complex. For example, a moving robot or robot capable of movement, which is almost always the case, has to be aware of its surroundings, at least on a functional level, i.e., to perform its functions or to operate. Industrial robots are similar.
Usually, industrial robots are movable hands/arms that extend out to perform specific tasks or robots that carries your stuff from one place to another or operate on niche needs of the industry. So naturally, with industrial robots, complexities are even higher since they have to be accurate and run without downtimes and be efficient in the production line.
Even a little downtime or failure can lead to huge losses and difficulties inside a production facility. Hence industrial robots are usually on the verge of sophistication and perform niche tasks.
Consequently, industries usually run production smoothly and efficiently, with the lowest downtimes ahead of the competition. Moreover, enterprises are also constantly evolving and optimizing themselves and often require upgrades and updates to keep themselves at the top of their game. Furthermore, since an industry production is continuously working, maintenance and repair operations should also be efficient and fast with minimum downtime.
With all this in mind, we can say that in an industry with a production base, the side that can optimize and efficiently run their production with minimum downtime and constant upgrades and evolutions become the winners. These sides can outperform the competition, yield the most profits, and come out at the top of their respective industries.
All this is possible if the robots used for production are efficient and require less downtime for installation during the show. Even maintenance and upgrade—the traditional method of using Teach Pendants brought revolution during its inception. But times have changed, and so has the technology around robot programming. Offline Robot Programming is the new pinnacle of robot programming and coding approach that has become so mature that it throws the old method of using teach pendants out of the competition.
Witnessing how the robot programming platform conquers, industries and industry experts consider it complex to integrate and challenge to learn. However, there still lies the misconception that only extensive production facilities of industries with deep pockets can afford to use Robot Programming Platforms. Unfortunately, that is not the case. Conversely, the Robot Programming Platform has come a long way in becoming the shiny new tool that is easy to use, adopt and base the industry upon rather than using Teach Pendants.
The Power of Robot Programming Platform
Robot Programming Platforms have their origin in simulation technology. Simulation, a technology introduced as early as 1947 by Thomas T. Goldsmith Jr. and Estle Ray Mann, enables a virtual platform to imitate an object or an environment, effectively retaining all their characteristics and behaviors with almost 100% accuracy. Thus, simulations can enact the subject (object or domain under imitation through simulation) properties and behavior even in different situations, conditions, and environments. Today, simulation technology has come so far that it can accurately simulate even complex mechanical and electric phenomena along with the capabilities to simulate real-world physics very accurately.
Real world-physics, mechanical and electrical interaction between objects is critical while developing and testing robots. Simulations today can simulate all these interactions very accurately. Simulation technology or Softwares can also simulate Electromagnetic phenomena along with fluid dynamics, air dynamics, gravity, collisions, etc., effectively with a high precision being virtually indistinguishable from the real world. It shows that simulating a whole robot with all its movements, behaviors, materials, processing, and other phenomena is possible. It’s very much possible and is already available. Companies like FS Studio are already providing Robotic Simulation Services with their deep knowledge and decades of experience to back it up.
We get the Robot Programming Platforms to pair this versatile and accurate simulation technology with robotic programming. Robot Programming Platforms not only enable virtual programming of robots without even taking it out of production, since the training process happens through software updates, but it is also possible to program robots while they are still operating in the production lines. Although, one may think this might invite huge problems and irregularities if the instructions are faulty. However, robotic programming platforms also provide features for testing and verification of these instructions virtually on a PC, even before uploading the education.
The offline robotic programming platform conquers a massive leap in robotic research and development, especially in industrial and production setups. However, traditional methods of using Teach Pendants to train and program robots are very time-consuming, resource-hungry, and require an operator's presence at all times. On top of that, the robots should also be out of production to even begin their training. Then add all the cost of taking that robot out of production, setting it up for training, and waiting for the robot until it completes its training and again putting it back for production. Furthermore, add the downtime it causes to the whole production. The cost is just too much more relative to offline robotic programming.
Robot Programming Platforms enable OLP (Offline Programming), which is an “offline” approach to robot programming, i.e., away from the “online” process of Teach Pendants. OLP enables faster, more efficient, and cost-effective robot teaching or programming with robotic programming platforms capable of testing and verifying these programs virtually in a simulation environment. It enables a much wider road of possibilities and opportunities with even fewer obstacles and trenches on the way.
The industries with Robot Programming Platforms can even develop programs/codes for robots in a PC with virtual/digital twin of the robots without even being present. It allows for tremendous flexibility and overall freedom to configure, test, update and upgrade robotic programming very frequently. And all this happens without even a second of downtime; it all occurs virtually; it all happens digitally.
It again opens the road towards a higher level of automation. Robot Programming Platforms with Artificial Intelligence at their core can analyze data, more efficient solution generation, and real-time optimization of existing solutions. With the power of deep learning, even potential errors cannot hinder the production line since AI with deep understanding enables the detection of possible errors and faults beforehand. Even self-diagnosis and self-real-time optimization are all within natural reach through the use of Robot Programming Platforms.
All these advantages and benefits help a production site or industry enhance their existing robots and production lines to be more efficient, cost-effective, and capable of yielding high Return on Investment (ROI) if they adopt Robot Programming Platforms. Furthermore, with fewer downtimes, more frequent upgrades, and seamless integration of digital technology, Robot Programming Platforms conquer complex robotic problems and help surpass and outperform the competition.
For a smoother transition towards Robot Programming Platforms, industries can seek collaborations and partnerships with FS Studio companies that provide OLP and robotic simulation services solutions. Even the companies currently using Robot Programming Platforms can look for improvements towards newer state-of-the-art solutions that are proven to be more efficient, robust, and intelligent. Not only this, technologies like Artificial Intelligence (AI), Virtual Reality (VR), and Augmented Reality (AR) will also be essential in the future, not only from a technological standpoint but also from an industrial standpoint. FS Studio excels in these types of bleeding-edge technologies. They can not only provide companies and industries with these types of innovative technologies. Still, They can also equip them with the power of these technologies to propel them ahead towards a more prosperous future of prosperity. Simulation technology grows more powerful and capable, which we can already see from the example of how robot programming platform conquers complex parts, outperforming the competition.
Companies and industries from different fields are moving towards this technology rather than old and traditional approaches. As a result, the industry’s future is looking more probable to reach the next industrial shift, the Fourth Industrial Revolution, sooner than later. With this in hindsight, we can be confident that industries that can adapt and adopt digital technologies like Robotic Programming Platforms quickly are the industries that are incredibly likely to outperform their competition and thrive in the future.