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.
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.
Computer Simulation of Human Robots Collaboration in the industries is closer than we think. The current industry is moving towards the Fourth Industrial Revolution (FIR). FIR or Industry 4.0 is the digital transformation of the existing industries to enable new ways of manufacturing & production with automation at its core. The digital world will effectively meet the real world at this stage, integrating them on a level never seen before. Human Robots collaboration is one of the significant parts of this integration. With transformative technologies like computer simulations, AR, VR, and digital twins, cooperation among humans and robots is an absolute path that the next generation of technology will take.
Computer simulation is a very crucial tool for industries like robotic research and engineering. With the increasing adoption of computer simulation in various industries, simulations are rapidly becoming a vital part of product innovation and R&D technology. It is especially true for the robotic industry since collaboration between humans and robots is an essential part of the human robot paradigm.
Where Does Computer Simulation Come into Play?
Some factors influence the possibility for robots and humans to work together and collaborate efficiently. One of the top priorities or factors that affect this collaboration is human safety. During the operation, development, or testing of this concept of computer simulation of human robots collaboration, human safety is a top priority and should never be compromised. For this, various safeguards or failsafe mechanisms, power limiting restrictions, tools to monitor for possible errors, and proper fallback plans can be helpful.
Alongside this, robots that are in use must be aware of their surroundings and environment. At the very least, the use case of the robot must reflect its awareness and capabilities. Furthermore, robots also must control and change their actions as per real-time feedback and happenings in their surroundings. Thus, it presents the robot research and development industry with another challenge of autonomy and the ability of robots to perceive their surroundings or environments efficiently.
Conversely, bidirectional communication among robots and humans may open the door to fulfilling all the requirements necessary for a safe and effective human robot collaboration. But achieving such a feat is also not possible without proper testing and massive investments of time, resources, and money.
Computer Simulations can solve all these problems and complexities with efficient and elegant solutions. Computer simulation technology provides a modeling system to visualize any complex system, even 3D digital space. For example, a robot consists of joints, motors, arms, actuators, sensors, links, controllers, and other mechanical and electronic components like a battery, processing unit, and networking interfaces. All these components and elements can be costly when they reach the level of sophistication a robot requires. Alongside this, integrating these components into a complete robotic system in which these components work together efficiently as a whole system is also a very complex and expensive task to accomplish. Nevertheless, this is where computer simulations come into play.
The advancement in computer simulation technology now allows for the simulation of all these components and elements in a fully functional robot. Alongside this, computer simulation software can also simulate various environments and conditions under which a robot may operate. Much like a natural environment, a simulation environment allows for multiple experiments, tests, and evaluation of a robot, except it, is without all the costs and risks present when testing the robot in the real world. Computer simulations also enable monitoring and assessing robots with a very high level of sophistication in virtually any environment or condition possible.
Why is Computer Simulation of Human Robots Collaboration Important?
The human robot collaboration is essential for the factories of the future and all the possibilities that follow. In a space where robots and humans can work together efficiently to complete different tasks, endless opportunities emerge. For example, robots allow us to perform precarious and dangerous jobs that require massive strength or skill, along with repetitive or requiring extra precision. Meanwhile, some jobs require human intervention due to either being too expensive or complex to automate and jobs that require critical thinking and human intelligence. Thus, it constructively allows industries to utilize the best of both worlds efficiently.
For instance, risky jobs like mining, exploration of unknown borders and areas, repetitive assignments, lifting heavy loads, etc., have more practical industry use cases for robot in the field, but they also require human intervention. Similarly, jobs that require extra precision, like in surgery, may be more suited for robots. Still, due to a lack of intelligence and critical thinking, it is currently unable to do so. Likewise, human intervention is essential in search and rescue operations, but it also requires scanning large and potentially unsafe environments that are more suited for robots or drones. Alongside this, all factories and manufacturing industries cannot generally use robots due to either being too expensive to automate the job or too complex for robots to perform. Hence, human resources are used in various factories and manufacturing sites, albeit the factory and manufacturing sites are dangerous and unsafe.
These difficulties are easily removable if computer simulation of human robots collaboration becomes very efficient and easy to realize. Moreover, if such cooperation becomes possible to achieve, one can reap potential benefits from both worlds. For instance, robot developers in health care organizations can utilize the precision of a robot and the critical thinking of a surgeon to develop a surgical robot to perform complex surgeries on patients.
Consequently, a collaboration between humans and robots that enables an open environment where humans and robots can work together to complete works with integration of benefits from both worlds is a very lucrative goal to achieve. Computer simulation opens the door to such a goal. Due to the numerous advantages computer simulations possess, various industries develop human robot collaboration systems.
Generally, robot development in computer simulation software starts with designing and prototyping the robot. It requires a massive amount of resources, cost, time, and multidisciplinary skills in the real world. Then, each prototype comes to its testing, assessment, and redesign of the system according to the evaluations and results. It also requires equally if not more massive amounts of resources, cost, time, and skills in the real world. For a complete robot consisting of all its features and functionalities and compliance with all the factors discussed above, this process of prototyping, redesign, and testing has to be repeated numerous times until the evaluation and results are entirely within acceptable terms.
However, with the help of computer simulations, all these processes become redundant. When robot development with computer simulations occurs, developers/manufacturers get a digital platform to perform rapid prototyping with testing, modeling, redesigning, and programming all within the simulation. With the help of the computer simulation, developers can design a robot with all the parts and components right from the start to get a robot model. This model can go through various experiments, evaluations, and assessments to ensure formal requirements compliance. If not, developers can make changes or even redesign the robot entirely without much effort since it's in a digital form.
Not only this enables rapid prototyping and development, it ensures that developers do not exhaust all their time worrying about resources or costs but utilize that time for better ideas and models. It also opens the door for creative minds to flourish and experiment with various designs and configurations of robots. Furthermore, since the initial design process starts with a digital model, developers can tweak, organize and play with different formats. Finally, it will ensure that the design phase outputs the team's accurate designs with an efficient and agile developmental process.
Moreover, testing and evaluation of robots in different environments is also possible with error reporting and monitoring systems working together to gather essential data. It ensures that all unexpected problems or errors that the developers may encounter during the physical build of the robot are taken care of and solved. Testing with trajectory planning, verifying algorithm operation and efficiency, verifying the integrity of the design, and overall working of the robot can all be done in simulations. Testing various fluid mechanisms, aerodynamics, mechanical integrity, and kinetic forces with realistic physics engines is also possible.
One of the most vital computer simulation of human robots collaboration is human safety. Simulations enable testing for human safety and protection in numerous conditions and environments. We can quickly test and examine communications, control, and safety mechanics inside computer simulations without ever having to put a human at risk. With technologies like Augmented Reality (AR), Virtual Reality (VR), and intelligent AI systems, humans can test these robots with immersive experiences in realistic environments without taking risks.
It will rapidly evolve the development of human robot collaboration with the power of rapid prototyping, innovative product development systems, and efficient R&D technology. Furthermore, with Industry 4.0 gradually moving from embedded systems towards the digital transformation of the industries, simulations can open the door to new ways of development and enhance the much sought-perfect cyber-physical system (CPS).
With the advent of computer simulations, robot development and research is moving away from machines with no or low-level intelligence towards a more autonomous, adaptable, flexible, and re-configurable system that can work efficiently with humans. With computer simulations, human collaboration with intelligent robots will be possible across various industries where the whole collaborative system will be efficient, sustainable, effective, and safe. And our approach of creating the computer simulation of human robots collaboration will be completed.