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:
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.
Simulation experts in industry 4.0 must have a passion for digital twin technology today for industry research. Digital Twin simulation is entering mainstream use as more industries are adopting this technology. According to Gartner's IoT implementation 2019 survey, 75% of organizations already use Digital Twin or plan to in a year. Notably, all the companies willing to adopt Digital Twin are implementing Internet-of-Things.
Digital Twinning is not a new technology. In 2002, Michael Grieves of the Florida Institute of Technology introduced the concept of Digital Twin publicly. In 2010, NASA showcased the first practical implementation of Digital Twinning to improve the physical model simulation of spacecraft.
We can see Digital Twin has been around for two decades now. Yet, many businesses are confused about the value of Digital Twin Technology. In addition, many companies don't know the use of Digital Twin in the modern energy, chemical, and process manufacturing industries.
Many companies still don't know that Digital Twin can enjoin the disconnected processes cutting out manual efforts that can be time-consuming.
For instance, interns or low-level employees will still follow the outdated method to gather engineering information. They would walk around one department to another to collect data required for engineering research.
Many engineers use CAD and PLM software and other sources to collect data to make informed design decisions. A few engineers are lucky enough to use enterprise search engines to pull information from various departments from hundreds of documents, folders, presentations, etc.
Now that we are entering the age of automation, companies must adapt to cultural change and access the right technology. They need to integrate technologies like Digital Twin to help teams gain information without any hassle. They also need to save employees from the pain of surfing through numerous record systems.
There are other reasons to consider Digital Twin to accelerate business innovation. Here we've discussed 3 of them below:
1) The Rapid build-up and expansion of data:
The business environment of the energy and chemical industries is already volatile. On top of that, the decision-making cycle is in an array across these industries due to piled-up data sources.
System Digital Twins made for entire plants, or factory systems can rescue energy and chemical industries. A massive amount of operational data can be collected, organized, and analyzed from various devices and products.
Human decisions are not rational, even if we make sound judgments after weighing evidence and assessing probabilities. It happens because the human brain tends to simplify information processing. So, cognitive biases, including memory and attention biases, influence human decision-making.
System digital twins can eliminate human bias for critical decision makings. System digital twins can also provide a single logical view of the actual situation based on evidence, probabilities, and analytics.
It is also essential that you know your needs before adopting Digital Twin Technology. Therefore, you must ask these three questions to ensure your success with Digital Twin:
2) What type of analytics should Industry players seek?
The factory systems and manufacturing plants involve complex processes today. So, measuring KPIs isn't easy now.
The digital twin can resolve this problem by providing deeper analytics from factory systems and plants, taking multi-dimensional factors and non-linear trade-offs into account.
The digital twin can build an accurate understanding of the future based on historical and present performances data. The digital twin can recommend the best strategies that can maximize profitability for the industries. Next, the experts will need to assess each recommendation and its impact to make the best decision for the businesses.
Therefore, industries can use the digital twin technology as a supporting tool to aid decisions enabling improved safety, reliability, and profitability.
3) Digital Twin Model Utility across the entire lifecycle of the asset:
Manufacturers use digital twins differently at each stage of the product development cycle.
Initially, manufacturers start working with Digital Twin Prototype or DTP. Then, manufacturers use DTP to design, analyze, and plan out the process to predict the future shape of the actual product.
In the next phase, manufacturers use Digital Twin Instance or DTI. DTI is the virtual twin of a physical asset. Developers will use DTI to run multiple tests and determine how the product will behave in different scenarios.
The DTI stays connected with the physical asset throughout its lifecycle. As a result, developers will add more operational data to improve it over time.
In the final phase, manufacturers will use Digital Twin Aggregate or DTA. Manufacturers use DTA to cross-examine the physical product, predictions, and learning based on the collected data from the previous phase.
People from engineering, operations, supply chain, shop floor even board room can look inside the assets and processes of the Digital Twin technology at every stage.
Companies integrate AI, machine learning, predictive analytics, etc., into the system with high hopes. They do it because they believe that digital transformation will cut out all the manual workload. However, when they realize that a lot of the work still depends on the human end, they get shocked.
Industries may have entered the automated age and have innovative IoT solutions at their disposal. However, automated systems cannot replace the human touch in many critical areas of business. For example, humans still need to implement and monitor automated systems in manufacturing plants.
Automation cannot replace other tasks like enhancing product design, building strategies, and growth roadmap, decision making, communicating with stakeholders, applying creativity to solve problems, etc. These areas will continue to need human intervention.
Companies need to set clear expectations when moving forward with the digital transformation of the assets.
The purpose of digital transformation and digital twin is to make the technical aspects of the job easier. In addition, Digital Twin technology will provide you the intelligence to help you focus your hard work on beneficial outputs.
Companies building Digital Twin Technology today are the pioneers of shaping the agile and intelligent industries of tomorrow. So, they need to develop the right digital twin platforms to leverage the full potential of digital transformation to create an exemplary model that others can follow.
The first steps will always be challenging. You can expect objections and hurdles to come your way. However, all these troubles are manageable if you know the proper ways to manage them.
Here is a brief guideline to follow for successful digital twin adoption in business:
To sum it up, digital twin adoption has the scope to attract more stakeholders' buy-in. Companies can show them the data-driven rewards based on concrete analysis instead of flawed predictions. So, the stakeholders will always have the know-how of the direction they are heeding with you.
"The true benefit of a digital twin: it gives you business intelligence to make better decisions in the future. It doesn't eliminate or minimize the work you're doing now, but it fundamentally changes what you're going to do next." - Former chief executive of Cambridge City Council, Andrew Grant
As we deduce the statement of Andrew Grant, it's a life lesson that industries have learned the most brutal way around the world after the Covid-19 shock. Thus, many enterprises are seriously considering the concept of Digital Twin and thinking big to expand it.
Companies are now interested in optimizing business operations based on the real-time insights gained from manufacturing plants and product use. As a result, they are more focused on satisfying orders, resolving root causes that are hindering growth, and maximizing factories' performance based on solid predictions.
We have already entered the age of automation as the 4.0 industrial revolution has begun. Today, companies maybe just interested in predictive maintenance. However, the use of Digital Twin Technology will expand where it will be integrated not for products but into manufacturing processes and entire factory systems.