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.
Teaching robots is a time-consuming and laborious task, especially when you’re manually teaching robots. Particularly with robots of niche applications, use cases, and robots with complex movements or robots within specific environments like industries and production. Robotic technology is continuously evolving, and so is its complexity. However, robotic tech is also becoming easier to use, more accessible, and more adaptable with increasing complexity. Conversely, teaching robots through traditional approaches like Teach Pendants is getting more and more challenging and complex.
The Robotics industry is complex because of the sheer complexity of the technology and the cost of developing, building, and deploying a robot. Robot research and development and deploying robots are challenging tasks because of the sensitive nature of testing in robotics. Testing a robot is an expensive task. Consuming massive resources and time, testing robots along with training them is a very resource-intensive task.
However, due to the advancement of technology and the Fourth Industrial Revolution (FIR) inching closer and closer, industries are rushing towards digital technology and automation, which, in some scenarios like industries and production only possible with robots. Consequently, the importance of robotics in the production industry is increasing day by day. As a result, manufacturers and production sites are getting more eager to adopt their production line with robots with digital technology at its core. And manually teaching robots would only slow the production down and eventually leave you behind in the competition.
The Complexity in Robotics
With robotics comes its complexity. A robot is not a single entity but an integration of several different parts, components, and systems working together. These parts, components, and systems are usually various mechanical parts, motors, actuators, hydraulics, sensors, processing systems, networking interfaces, and many more. These components are very hard to build and even complex to perfect. Furthermore, integrating these parts to work together simultaneously with efficient cohesion to achieve a system that can perform specific tasks is complex on another level.
The integration may well be complete and the robot ready. But another major hurdle comes in the form of programming/coding the robot. Programming a simple robot with a particular function may be easy, but the robots that have to perform complex tasks while performing complex movements with precision are strenuous. This difficulty only scales up for industrial robots that have to accomplish tasks with accuracy and repeatability and perform various activities and functions within the production environment.
Why Manually Teaching Robots Will Hold You Back?
Programming a complex robot also requires a complex teaching process. The traditional approach to programming and coding robots is to use teaching pendants. Teaching pendants are a device that helps robot operators to control and program an industrial robot remotely. For example, these devices can code or teach a robot to follow a specific path or perform certain actions in a particular manner. With teaching pendants, robot operators or developers have to teach these robots manually.
Manual robot teaching may be easier on robots with low movement paths, simple actions, or singular axes. But industrial robots are a whole another story. They need to be constantly working in a usually adaptive and harsh environment of production. Such robots are complex and also very sensitive. Hence training the robots with teaching pendants is a difficult task. It is a very time-consuming task with the requirement of the teaching personnel to be present at all times. Furthermore, the robots have to be in teaching mode during all this time which means they cannot perform other tasks. Add this to the fact operators have to take them out of production during this long teaching process. All this makes manual teaching very cumbersome.
The downtime while teaching the robots is a massive issue to production. Moreover, this downtime is not only a one-time thing. Since industries have to be at the top of their game to thrive, they need to evolve and adapt over time. New changes and upgrades are necessary. Maintenance and repair works are inevitable. And even the failure of robots is not a common thing. All this requires teaching pendants, which is again very slow and a tedious approach to programming robots. It will add more delays, difficulties, costs and consume more resources. And this is a massive bottleneck for production.
Instead of wasting time in this slow and cumbersome manual approach, using new and better solutions with automation at its core is the way to go.
Learn About Offline Robot Programming
Offline Robot Programming is an “offline” approach to robot programming. Offline Programming (OLP) is a software solution to manually robot teaching by replacing the teaching pendants with simulation software. This “offline” solution teaches the robots virtually through software remotely. Thus, OLP takes leading away from the manual approach and takes out the requirement to remove the robots from production.
Although Offline Robot Programming is not a new technology, its evolution in recent years puts it in the spotlight in robot programming and the whole paradigm of robotics. It’s because of the advantages and benefits of using offline robot programming. Offline robot programming replaces the teach pendants with a more elegant solution. Furthermore, OLP allows for industries to train robots and their programming/coding through software updates. Robotic Programming Platforms also offer different software solutions to generate these instructions.
It means there is no need for the actual physical robot to be present in any generation phase or testing the training program/code. Instead, all this happens within the simulation technology inside the robotic programming platform itself. The evolution of simulation technology is so far ahead that it can now accurately simulate almost any object or environment with all the characteristics and behaviors of the original real-world object or environment.
Simulation technologies today can simulate every robot’s functionalities, features, and operations. Various behavior, states, and phenomena of robots and their components can simulate without manually teaching robots. Simulations can accurately simulate the mechanical elements of different parts with different materials and their operation in different environments and conditions. Along with this, fluid dynamics for air and water is also possible to simulate. Collisions, movements, etc., are also potential. It is due to the ability of simulations to accurately simulate and imitate the real-life physics of materials and the environment.
In addition to this, simulations can also imitate electronic components and processes. For example, it can accurately simulate the processing of CPUs and progressing units or even network interfaces and data exchange. Along with this, simulations can even test technologies like Artificial Intelligence (AI) with Machine Learning (ML) and deep learning. All these possibilities allow simulations to simulate all behavior, state, and properties of a robot along with its features and functionalities effectively.
Robotic simulation software solutions are already available, and different industries and companies are already leveraging their benefits. These simulations make innovative technologies like OLP possible to exist and thrive, creating manually teaching robots irrelevant. With offline robot programming, companies need not go back to the old approach of using teaching pendants. Such an old approach is very time-consuming while also requiring enormous resources, workforce, and investment. In contrast, OLP provides companies with elegant future-proof solutions that are effective and efficient.
OLP successfully reduces downtimes from production due to its ability to upload programming instructions in robots that they are working on without taking them out of the output. They can also enable new roads to generation and testing robot programs far from the manual testing method and age of robot codes or instructions. Simulations make it very easy to try these codes, while AI automation enables self-diagnosis and real-time optimization of production lines.
OLP is often seen as a technology that is very complex and requires high skills to utilize. There is a huge misconception that only the sides with deep pockets can afford to use OLP solutions, and there won’t be any demand for manually teaching robots anymore. But that is not the case. OLP solutions are pleasing on paper and easy to integrate and adapt even in existing production. Companies like FS Studio are working hard to bring out innovative solutions and state-of-the-art R&D technologies, including robotic OLPs, to make this transition of using OLP solutions smoother. With decades of experience and collective knowledge of various skillful people, FS Studio brings out solutions like Robotic Simulation Services for multiple companies and industries.
With the increasing pace of the industry’s move towards Industry 4.0, every industry is eagerly shifting towards digital technology while replacing old technologies like Teach Pendants with newer, more elegant, and efficient solutions like Offline Robot Programming platforms. Offline robot programming opens the road to newer possibilities and opportunities, enabling rapid prototyping, testing, training, and superior research and development, saving you from manually teaching your robots. In addition, it will help companies bring out efficient production and help them maximize their efficiency with a proven feat of achieving higher Return of Investment (ROI) in production lines and product innovation. Furthermore, this will help industries and companies innovate and remain at the top of their game to surpass and outperform their competitors.
Robot programming software is a software solution that helps program or code a robot for its use or operation. Offline Robot Programming Software is also the same.
With the advancement of technology, Industry 4.0 is inching swiftly closer towards us faster than ever. Industry 4.0, also known as the Fourth Industrial Revolution, is the age of digitization where every industry has digital technology at its core. Consequently, digital technology is continuously evolving. Today, it almost seems inevitable for industries to adopt digital technology instead of relying on the traditional approach to industry, manufacturing, and product innovation.
Robotic technology is also continuously evolving, with robots today more capable than ever in various fields and fronts, even unseen in the last decade. Moreover, with the complexity and sophistication of the robots increasing, they are constantly getting more and more complex to program, code, and even develop.
However, with increasing complexity in technology, it is also getting more and more adaptable, usable, accessible, and easy to use. It’s because newer bleeding-edge technological solutions help keep these complex problems and technology operable and functional with great ease of use and access. One of the similar problems regarding the increasing complexity is currently running alongside the robotic industry.
The Robotics industry is far more complex, risky, and resource-hungry than most technological undertakings out there. Due to the growing industry use cases for robot and their ability to fulfill these use cases. The nature of robotic technology is that numerous parts and systems converge together to form a single system unit that can perform various tasks and operations using these parts and systems. Due to this nature, alongside the already complex building blocks of robots, i.e., the components and different systems, integrating these building blocks to work in an efficient cohesion with each other is a huge undertaking.
For easing the difficulty of integrating different parts and systems, many state-of-the-art industries and companies are starting to use robotic computer simulations. Simulations are a great innovation of digital technology that can help develop robotics through research, design, development, and production. Moreover, even after production, robotic software can now help robot operations, maintenance, and programming with different robot programming software solutions.
What is Offline Robot Programming Software?
Offline Robot Programming software is an “offline” approach to programming or coding a robot. This “offline” approach takes the usual method of programming a robot, i.e., teaching pendants away while doing the “teaching” part through the software remotely. However, this remote programming of the robot takes away the need of taking the physical robot out of production; instead can program and code robots virtually through software.
Teach pendants the most common interface to program an industrial robot. The device helps control an industrial robot remotely and teaches them to move or act in a certain way. For example, these devices can program or code the robots to follow a specific path or perform a certain action in a particular manner. These devices also allow the operator to control and work with these robots without being physically present or in tether connection with the robot. It means robot programmers or operators get to control the robots and “teach” them remotely. Technicians usually use these devices for testing or programming, or coding of industrial robots. Hence, teaching pendants are a crucial part of industrial robotics.
Offline Robot Programming Software replaces this teaching pendant with a more elegant and efficient solution with the power of software and simulation technology. Due to the control over robots with software since robotic OLP or Robotic Offline Programming allows for uploading programs and codes through software updates. Furthermore, software developers and robot operators can generate these programs and codes through robotic simulation software in a PC rather than using the robot physically. OLP is, therefore, a more elegant, efficient, and more modern way to program industrial robots.
Why is Offline Robot Programming Software Important?
Even though these pendants are helpful and crucial to industrial robotic operations, they remain one of the bottlenecks of industrial robotics. Right off the bat, these devices are very slow and time-consuming. It's also very resource-consuming and requires personal at all times to operate. Furthermore, pendants also require the presence of the actual robot. They need the robot to be physically active in the teaching mode rather than doing other work during the teaching process, which is usually very long.
Hence, during teaching, the robot cannot be in production or be doing other functional tasks teaching process is very lengthy and tediously for the more complex robots with various joints or movement points and axes. The robot programmer has to program multiple joints and parts to code the robot manually, which is very time-consuming. The programmer will also have to take out the production robot during and until the teaching process. It will surely hamper the production line, and hence downtimes become longer.
In any industrial setup, the production line is the most vital part of it. So much so that the whole manufacturing or production plant usually is based around it. Holding such importance, the optimization of production lines is a very crucial task in any industry. Downtimes, irregularities, or faults in the production lines and components around it means it directly hampers the sector. Moreover, machines like robots, especially the ones with automation, are very crucial in production lines. Hence, production lines must not stop nor deter it due to the robots.
However, with robotic OLP, industries can remove and eliminate all these disadvantages and bottlenecks from production. Instead of teaching these industrial robots online, offline programming eliminates the downtime for programming these robots completely. With this power in their hands, production lines can now completely get rid of time for programming. Instead, industries can use all these times in the actual production and get better returns.
With OLP, automation comes one step closer in production setups. Offline Robot Programming software enables rapid prototyping to test programs and codes before uploading them to the robots through simulation software. Furthermore, simulations are now very technologically smart such that they can simulate all robot parts, mechanics, systems, and movements. With such capability in hand, robot programming and even robot development and the building will become very easy. Due to this, testing, training, and evaluating robots virtually become very easy through OLP. Furthermore, it allows for error detection and verification of programs and robot capability to perform tasks and operations even before they are physically present.
Apart from this, Offline Robot Programming software also increases the productivity of production lines and robot operators and developers. Furthermore, OLP also provides greater profitability and has a better Return On Investment (ROI). Moreover, with OLP, one can test and prove new and better project or concept ideas in their quotation phase without investing in physical resources.
OLP allows for not only training and testing but also helps in maintenance and repairs too. OLP can help to track down potential faults and errors even before they occur or after they occur. It further makes the production efficiency and without any downtimes possibly in future too.
Not only is OLP advantageous and beneficial for regular industrial robots, but it's also essential and can be a boon for some industries that involve high risk. For example, industries like aviation, nuclear, automotive are very high-risk industries. Testing robots in these industries is a sensitive matter. Hence OLP is a requirement in these industries to train and test robots efficiently. Furthermore, without, OLP it is likely not even possible and feasible for industries like the space industry able to undertake projects and accomplish them.
Offline Robot Programming software is generally seen as a technology with high complexity and requiring very skillful personals. But that is not the case. Various companies like FS Studio provide solutions when it comes to offline robot programming. Companies like these can help industries get started with OLP and thrive on enabling substantial new possibilities and opportunities. With decades of experience and expertise in fields like Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Simulation technology, FS Studio, can provide companies with proper and efficient OLP solutions to propel their industries with more efficient, safe and effective production lines. With the advent of Industry 4.0 upon us, companies and industries now must look for better alternatives and modern approaches to the industry. Digitization of industries is the future where digital technology will be at the core of all industries with efficient and smart solutions. OLP with simulation technology enables rapid prototyping, testing, development, and superior research and development (R&D) along with faster and efficient programming or coding of industrial robots. Furthermore, industries can collaborate with different OLP providers to determine the best solution for their particular industry and production and help them integrate their existing robots and production for a more smooth transition towards Offline Robot Programming Software.
The advent of next-generation technologies like Simulations, AR, VR, and AI continues to grow rapidly. With continuous evolution in their advancement and increasing accessibility, they can exponentially add value to manufacturers. Hence, influencing industries across the globe to adopt these technologies at an increasing rate. For example, artificial intelligence with immersive technologies like AR and VR swiftly transforms manufacturing processes and product development. But, on the other hand, robotic technology redefines the possibilities and opportunities in various fields and industries.
The increasing sophistication of robotic technology is visible due to giant leaps in the capabilities of current robotic systems. With technology evolving swiftly, the industry is also adopting newer technologies in its manufacturing and product development processes. One of these newer technologies the industry is moving towards is simulation technology.
With the dawn of Industry 4.0 upon us, industries undoubtedly need to advance towards digital transformation. In this advancement, simulation technology is a boon for manufacturers. Although simulation technology is not new due to its rapid evolution in recent years, it is expanding its horizon of possibilities and opportunities. Robotic technology is one of the unknown frontiers of simulation tech.
Simulation software has the power to enable rapid prototyping, testing, and development of product development processes and R&D technology. Computer simulation is one of the vital tools for industries like robotic development and manufacturing. With the crucial role of robots in the manufacturing industry, the development and advancement of robotic technology are significant for the whole manufacturing industry.
Why Simulation Softwares in Robot Development?
Robot research and development, along with its design and production, is very complex. It is not just because of the sophistication of the technologies in a robot. But also because of economic reasons and risks in robotic development. They also have to add value to the manufacturers as well.
Robots are usually expensive pieces of machinery. Industrial and manufacturing robots are costly due to the niche application following the niche research and development requirement. Moreover, even general robot design and development require massive resources, cost, time, and multidisciplinary skills. Furthermore, prototyping robots for testing, evaluation, and assessment need equally, if not more, resources, time, cost, and abilities. Add this with risks present in the real world, and robotic development truly becomes a huge undertaking.
Computer simulations for robotic development can solve all these problems. Computer simulations offer efficient and elegant solutions that are more cost-effective and less time-consuming. Any computer simulation software usually provides a 3D digital space to test and develop a product. Similarly, robot simulation software offers different environments and tools in a digital 3D area to test, run, research, evaluate and develop a robot.
Real robots in the real world consist of parts like motors, batteries, joints, arms, sensors, actuators, controllers, and other mechanical parts. Furthermore, robots also consist of networking, processing, and data handling components to analyze data and communicate. Apart from this, some robots also need to be smart and capable of making various decisions in real-time to add value to manufacturers. Consequently, due to these causes, robots in the real world are very complex and expensive.
However, robotic simulation software provides all these tools, components, and parts in its digital space. Due to the high advancement of simulation software, today, simulation software can simulate all these parts and subsequently a fully functional robot that can run/operate in different conditions and environments. One just has to bring these parts and models together digitally. The simulation software also supports the design and development of these parts and models digitally. Hence, developing or putting together a robot in a simulation environment is very quickly relative to the real world.
Moreover, just like in the real world, robotic simulation software also allows for the testing and evaluation robots in different environments and conditions. Simulation software can simulate fluid and air dynamics, collisions, and many more physical, real-world phenomena with very accurate and modern physics that reflect real-world physics. All this happens similarly to the real world, except the simulations are fast and easy to develop and do not have to suffer huge risks and significant economic setbacks.
With computer simulations on hand, the risks and costs in association with robot development become redundant. It also ensures that the developers do not exhaust their time worrying about resources and cost but instead focus on the actual robot development. It also provides the developers with flexibility and space to develop the best robot for their requirements without compromising developmental risks and costs.
How they Add Value to Manufacturers
With the vast advantages of using simulation software in robotic research and development, manufacturers are beginning to realize the potential it carries. Furthermore, minimizing risk in robot development in manufacturing and factories also means developing robots with better design that suits the requirements to a far greater degree. As a result, companies or factories using robots in various product manufacturing processes can undoubtedly reap the benefits of better and cost-effective robotic solutions, which is possible due to robotic simulation software.
Proper simulation software can ensure the best systems for different applications and use cases. With rapid design and development in the card, even if a system is not up to the mark, companies can simply re-design it in the digital form with much lower costs and resources. In addition, with computing systems becoming cheaper and efficient, simulations can now help manufacturers build their robotic solutions to stay competitive in the market with new and better solutions.
There are numerous ways the robotic simulation software can add value to manufacturers, for example, cohesion with better designs, processes, and efficient investment.
With manufacturers expanding their product spectra to a wide range, robotic systems in use are not always general robots but tailored with specific needs and requirements in play. For instance, a car manufacturing company cannot automate the assembly line process without the same robots. Development of robots enters completion with niche use cases in mind. One robot installs engines while another robot paints the car; another robot detects flaws in the painting. Another installs wheels, another lifts machines before installation, and so on. Each different use case requires another robot.
Hence in this scenario, designing different robots for different use cases in the real world is very expensive as well as being time and resources consuming. However, creating robots for other use cases is much simpler, more accessible, faster, and cost-effective through simulation software. Consequently, robotic simulation software can also help manufacturers to customize and fine-tune robots according to their needs. Moreover, such systems can undergo design and development to seamlessly fit into their existing facilities and systems quickly relative to traditional methods.
Automation also becomes much simpler with the capability of simulation software to test automation and smart technologies in a full-blown manner even before the final design is ready. Furthermore, simulation consisting of accurate and minute details add value to manufacturers, helping them configure their automation system so that the resultant robotic systems can meet their goals. However, manufacturers usually have to take significant risks for proof of design and automation process verification without simulation systems.
Due to all these advantages, simulation systems can return great results on the manufacturer’s investment. Furthermore, simulation software capable of self-diagnosis and automatic error reporting ensures that the finished designs and products are free of errors and potential flaws. It also ensures that the robotic simulation systems function with precision with known efficiencies in different environments and conditions. Thus, it helps manufacturers get maximum returns on their investment.
Moreover, the investment also becomes largely more safe and secure relative to the investment in traditional approaches. Furthermore, with the successful design and development of robots or systems meeting all requirements and needs beforehand, manufacturers can ensure further lucrative benefits and returns. Eventually, the end goal of manufacturers is to get returns from the end product. It largely depends upon the manufacturing process, which depends on the systems and procedures, including robotic systems used for manufacturing.
Hence, ultimately a successful result is a massive win for manufacturers. Robotic simulation software ensures that this result is successful and that the manufacturers get there with much lower costs, resources, time, and skills.
Industry 4.0 or the Fourth Industrial Revolution (FIR) is all about the digital transformation of enterprises. With Industry 4.0 approaching more closely than ever before, industries and manufacturers must keep up with advancements in technologies like simulation and artificial intelligence, AR, and VR. While it may seem that the transition to digital technology and simulations for product innovation, R&D, and robotic development is complex, the result in-store has enormous benefits with lucrative returns.
Hence, companies like FS Studio are working hard in these innovative technologies to ensure that manufacturers can experience a smooth transition to Industry 4.0. For example, ZeroSim, a technology in development and service by FS Studio, is a robotic simulation software technology built on Unity3D, a game engine, and ROS (Robotics Operating System). It provides a multitude of tools for building robots and simulation environments in Unity to interface with ROS.
Technologies like these add value to manufacturers, making robotic simulations faster, easier, and hassle-free to use for manufacturers. It also ensures that manufacturers can easily leverage the lofty benefits of robotic simulation software to transition themselves towards the next industrial revolution.