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.
Maintenance and Repair of Industrial Robots is an important issue that many companies may overlook sometimes.
Industrial robots are a great asset to any manufacturing plant, but they require maintenance and repair. You can save time and money by knowing the basics of how to handle issues that may arise with your industrial robot. Today, we'll go over some of the most common problems you might experience with your industrial robot, as well as what you should do when these problems occur.
You'll also learn about some best practices for improving the lifespan of your industrial robot to get more from it before having to replace or repurpose it.
Robots are fantastic tools to make your company more efficient. But like any other tool, they need regular care and maintenance to stay in good working order. So to ensure that your robots are running smoothly, you should establish a plan for regular maintenance.
Multiple problems can arise with robots, other than the complete failure of the equipment.
These include- electrical malfunctions, including damaged cords or cables and wires which could lead to an electrical fire. Dangerous behavior toward staff members due to unpredictable robot actions or malfunctioning behaviors can also occur.
Position deviation in robots is another common problem where a robot is no longer staying within its intended perimeters causing issues for your business processes. Repeatability in robots is a problem where the repetitive motions performed by a machine are not occurring consistently anymore resulting in data loss or software breakdown leading to significant disruptions.
Preventative maintenance is essential to ensure that your industrial robots will function correctly and without errors. Like any other machine, robots require routine checks for them to continue running efficiently and smoothly.
The Benefits of Preventative Maintenance for Industrial Robots:
Smooth Working Conditions:
A robot that's well-maintained will work better and more efficiently. Even if a machine looks like it's working fine without preventative maintenance, you may be spending extra money on operating costs because of this lack of care. We should make sure robots are running as smoothly as possible with minimal energy use. Upkeep is necessary!
By taking care of your industrial robots, you can save money and avoid costly repairs or replacements. Preventative maintenance tasks are generally less expensive than emergency ones in the case that a breakdown happens. You'll also be able to keep everything running smoothly by avoiding unplanned downtime altogether!
By taking preventative measures, you can prolong the life of robots. Even if nothing is wrong with a robot that isn't well-cared for, it could still be putting additional stress on its parts, leading to them breaking down earlier than expected. It will not have an immediate effect, but over time, these stresses could cause severe damage and shorten the lifetime of your machine before its due date arrives.
Preventative maintenance is more cost-effective than repairing issues that arise after they happen. When you do a preventive repair of an industrial robot, it saves your business time and money.
What is the Upfront Cost of an Industrial Robot?
Manufacturers need to consider several aspects when calculating the upfront cost of an industrial robot.
Firstly, while robots require someone with a hefty price tag (approximately $250k) to maintain them, they can help companies save money in other ways, such as increased productivity and reduced labor cost.
Secondly, humans incur many additional expenses that their robotic counterparts don't need to worry about; these include healthcare benefits ($10000 annually) training/recruitment fees ($2-$47 per hour depending on the country).
Human employees also require breaks now and then, which isn't necessary when utilizing advanced robotics technology! However, they also have operations and maintenance costs at around $10,000 annually compared to factory employee hourly wages that range from 2-47 dollars per hour, depending on the country.
Initially, the overall cost of an industrial robot (including systems engineering costs) is approximately $250,000. Furthermore, robots will also have operations and maintenance costs which we calculate at approximately $10,000 annually.
Typically, in the first 3-4 years the cost of industrial robot maintenance will be around $500 per year regarding preventive maintenance like lubrication and upgrading batteries. In the 5th year, the preventative maintenance will cost around $5,000 primarily for replacing wear items like internal wire harnesses.
In the next 6-8 years, there would be another 500 dollars spent on preventative maintenance. Still, this time it's mostly to do with greasing any moving parts and replacing the lithium-ion batteries, which should last up to about 8 or 10 years, depending on usage.
At first glance, it may seem like investing in a robotic workforce is very costly. If you consider all the expenses related to workers, such as recruiting expenditures, payroll hours loss due to accident hospitalization, etc.; they quickly add up over time.
The best robotic preventative maintenance intervals are not the same for all robots. Different mechanical companies will give you additional time frames for when your company should do maintenance on its machines, ranging from every 5,000 hours to 1 year or 3,850 operating hours. By enhancing operations, industrial robots can deliver an excellent return on investment (ROI). They include accuracy, efficiency, safety, and profitability.
Regular Maintenance Schedule for Robots:
Here are some everyday tasks that can be part of a maintenance schedule for robots, depending on the model and its usage:
If you notice any suspicious noise or vibration, don't assume it's nothing. Don't hesitate to talk with the manufacturer about what could be causing that weird sound. They may already know and can help prevent a more significant problem down the road before anything serious happens!
Data backup is essential if your business experiences equipment failure. You can back up data individually or use software to handle all robots on the same network.
However, when it comes down to data, you should always have a plan in place for when something goes wrong. In this case, having proper backups of robot's information will be helpful if there were ever an issue with their performance and function within the workplace.
Electrical Maintenance & Battery Replacement:
Minor and major electrical work is an expected part of industrial robot maintenance. This task should be left to the professionals, as it can become hazardous if not done correctly. Also, replace the batteries in your robot arm and controller if they are not working correctly.
Maintenance needs vary depending on how often the plant operates the machine, what materials have passed through the machinery, etc. Still, your company should consider hiring experienced technicians for these tasks at least twice per year to ensure safety when using their equipment.
To keep your equipment working correctly, you should investigate and clean the vents and filters weekly. Ensure that these areas are free of dust or other particles to avoid clogging. You'll also want to check for signs of wear, such as burned-out lights in light curtains. It indicates that the machine may have wiring issues.
Repairing robots can be difficult and costly, so it's essential to prepare for any eventuality. Industrial robot maintenance is a dynamic process that requires constant attention and understanding of the technology to keep your business running smoothly.
For more information or expert advice contact us anytime!