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.
Robots are complex pieces of machinery. Robots are engineering marvels that enable different components and systems to help with higher functions and features. These components and systems are usually very complex and require much research and development with time, resources, and specific skills. Furthermore, integrating these components is difficult, and the robot programming platform conquers it well.
With the advancement of technology, various systems, including sensors, processing power, battery power, storage systems, motors, actuator systems, and digital systems, are getting more modern and efficient. With the constant evolution of these components, they are increasingly getting complex. However, increasing complexity also increases the ease of use, efficiency, and capability of these components. Nevertheless, the integration of these components is the hardest part.
Robots with specific use cases, more movement points, locomotion capabilities, and robots that perform specific tasks with great accuracy and repetition are even more complex. For example, a moving robot or robot capable of movement, which is almost always the case, has to be aware of its surroundings, at least on a functional level, i.e., to perform its functions or to operate. Industrial robots are similar.
Usually, industrial robots are movable hands/arms that extend out to perform specific tasks or robots that carries your stuff from one place to another or operate on niche needs of the industry. So naturally, with industrial robots, complexities are even higher since they have to be accurate and run without downtimes and be efficient in the production line.
Even a little downtime or failure can lead to huge losses and difficulties inside a production facility. Hence industrial robots are usually on the verge of sophistication and perform niche tasks.
Consequently, industries usually run production smoothly and efficiently, with the lowest downtimes ahead of the competition. Moreover, enterprises are also constantly evolving and optimizing themselves and often require upgrades and updates to keep themselves at the top of their game. Furthermore, since an industry production is continuously working, maintenance and repair operations should also be efficient and fast with minimum downtime.
With all this in mind, we can say that in an industry with a production base, the side that can optimize and efficiently run their production with minimum downtime and constant upgrades and evolutions become the winners. These sides can outperform the competition, yield the most profits, and come out at the top of their respective industries.
All this is possible if the robots used for production are efficient and require less downtime for installation during the show. Even maintenance and upgrade—the traditional method of using Teach Pendants brought revolution during its inception. But times have changed, and so has the technology around robot programming. Offline Robot Programming is the new pinnacle of robot programming and coding approach that has become so mature that it throws the old method of using teach pendants out of the competition.
Witnessing how the robot programming platform conquers, industries and industry experts consider it complex to integrate and challenge to learn. However, there still lies the misconception that only extensive production facilities of industries with deep pockets can afford to use Robot Programming Platforms. Unfortunately, that is not the case. Conversely, the Robot Programming Platform has come a long way in becoming the shiny new tool that is easy to use, adopt and base the industry upon rather than using Teach Pendants.
The Power of Robot Programming Platform
Robot Programming Platforms have their origin in simulation technology. Simulation, a technology introduced as early as 1947 by Thomas T. Goldsmith Jr. and Estle Ray Mann, enables a virtual platform to imitate an object or an environment, effectively retaining all their characteristics and behaviors with almost 100% accuracy. Thus, simulations can enact the subject (object or domain under imitation through simulation) properties and behavior even in different situations, conditions, and environments. Today, simulation technology has come so far that it can accurately simulate even complex mechanical and electric phenomena along with the capabilities to simulate real-world physics very accurately.
Real world-physics, mechanical and electrical interaction between objects is critical while developing and testing robots. Simulations today can simulate all these interactions very accurately. Simulation technology or Softwares can also simulate Electromagnetic phenomena along with fluid dynamics, air dynamics, gravity, collisions, etc., effectively with a high precision being virtually indistinguishable from the real world. It shows that simulating a whole robot with all its movements, behaviors, materials, processing, and other phenomena is possible. It’s very much possible and is already available. Companies like FS Studio are already providing Robotic Simulation Services with their deep knowledge and decades of experience to back it up.
We get the Robot Programming Platforms to pair this versatile and accurate simulation technology with robotic programming. Robot Programming Platforms not only enable virtual programming of robots without even taking it out of production, since the training process happens through software updates, but it is also possible to program robots while they are still operating in the production lines. Although, one may think this might invite huge problems and irregularities if the instructions are faulty. However, robotic programming platforms also provide features for testing and verification of these instructions virtually on a PC, even before uploading the education.
The offline robotic programming platform conquers a massive leap in robotic research and development, especially in industrial and production setups. However, traditional methods of using Teach Pendants to train and program robots are very time-consuming, resource-hungry, and require an operator's presence at all times. On top of that, the robots should also be out of production to even begin their training. Then add all the cost of taking that robot out of production, setting it up for training, and waiting for the robot until it completes its training and again putting it back for production. Furthermore, add the downtime it causes to the whole production. The cost is just too much more relative to offline robotic programming.
Robot Programming Platforms enable OLP (Offline Programming), which is an “offline” approach to robot programming, i.e., away from the “online” process of Teach Pendants. OLP enables faster, more efficient, and cost-effective robot teaching or programming with robotic programming platforms capable of testing and verifying these programs virtually in a simulation environment. It enables a much wider road of possibilities and opportunities with even fewer obstacles and trenches on the way.
The industries with Robot Programming Platforms can even develop programs/codes for robots in a PC with virtual/digital twin of the robots without even being present. It allows for tremendous flexibility and overall freedom to configure, test, update and upgrade robotic programming very frequently. And all this happens without even a second of downtime; it all occurs virtually; it all happens digitally.
It again opens the road towards a higher level of automation. Robot Programming Platforms with Artificial Intelligence at their core can analyze data, more efficient solution generation, and real-time optimization of existing solutions. With the power of deep learning, even potential errors cannot hinder the production line since AI with deep understanding enables the detection of possible errors and faults beforehand. Even self-diagnosis and self-real-time optimization are all within natural reach through the use of Robot Programming Platforms.
All these advantages and benefits help a production site or industry enhance their existing robots and production lines to be more efficient, cost-effective, and capable of yielding high Return on Investment (ROI) if they adopt Robot Programming Platforms. Furthermore, with fewer downtimes, more frequent upgrades, and seamless integration of digital technology, Robot Programming Platforms conquer complex robotic problems and help surpass and outperform the competition.
For a smoother transition towards Robot Programming Platforms, industries can seek collaborations and partnerships with FS Studio companies that provide OLP and robotic simulation services solutions. Even the companies currently using Robot Programming Platforms can look for improvements towards newer state-of-the-art solutions that are proven to be more efficient, robust, and intelligent. Not only this, technologies like Artificial Intelligence (AI), Virtual Reality (VR), and Augmented Reality (AR) will also be essential in the future, not only from a technological standpoint but also from an industrial standpoint. FS Studio excels in these types of bleeding-edge technologies. They can not only provide companies and industries with these types of innovative technologies. Still, They can also equip them with the power of these technologies to propel them ahead towards a more prosperous future of prosperity. Simulation technology grows more powerful and capable, which we can already see from the example of how robot programming platform conquers complex parts, outperforming the competition.
Companies and industries from different fields are moving towards this technology rather than old and traditional approaches. As a result, the industry’s future is looking more probable to reach the next industrial shift, the Fourth Industrial Revolution, sooner than later. With this in hindsight, we can be confident that industries that can adapt and adopt digital technologies like Robotic Programming Platforms quickly are the industries that are incredibly likely to outperform their competition and thrive in the future.