robotics Archives - AUTOMATION INSIGHTS

The 5 Most Common Types of Fixed Industrial Robots

The International Federation of Robotics (IFR) defines five types of fixed industrial robots: Cartesian/Gantry, SCARA, Articulated, Parallel/Delta and Cylindrical (mobile robots are not included in the “fixed” robot category). These types are generally classified by their mechanical structure, which dictates the ways they can move.

Based on the current market situation and trends, we have modified this list by removing Cylindrical robots and adding Power & Force Limited Collaborative robots. Cylindrical robots have a small, declining share of the market and some industry analysts predict that they will be completely replaced by SCARA robots, which can cover similar applications at higher speed and performance. On the other hand, use of collaborative robots has grown rapidly since their first commercial sale by Universal Robots in 2008. This is why collaborative robots are on our list and cylindrical/spherical robots are not.

Therefore, our list of the top five industrial robot types includes:

- Articulated
- Cartesian/Gantry
- Parallel/Delta
- SCARA
- Power & Force Limited Collaborative robots

These five common types of robots have emerged to address different applications, though there is now some overlap in the applications they serve. And range of industries where they are used is now very wide. The IFR’s 2021 report ranks electronics/electrical, automotive, metal & machinery, plastic and chemical products and food as the industries most commonly using fixed industrial robots. And the top applications identified in the report are material/parts handling and machine loading/unloading, welding, assembling, cleanrooms, dispensing/painting and processing/machining.

Articulated robots

Articulated robots most closely resemble a human arm and have multiple rotary joints–the most common versions have six axes. These can be large, powerful robots, capable of moving heavy loads precisely at moderate speeds. Smaller versions are available for precise movement of lighter loads. These robots have the largest market share (≈60%) and are growing between 5–10% per year.

Articulated robots are used across many industries and applications. Automotive has the biggest user base, but they are also used in other industries such as packaging, metalworking, plastics and electronics. Applications include material & parts handling (including machine loading & unloading, picking & placing and palletizing), assembling (ranging from small to large parts), welding, painting, and processing (machining, grinding, polishing).

SCARA robots

A SCARA robot is a “Selective Compliance Assembly Robot Arm,” also known as a “Selective Compliance Articulated Robot Arm.” They are compliant in the X-Y direction but rigid in the Z direction. These robots are fairly common, with around 15% market share and a 5-10% per year growth rate.

SCARA robots are most often applied in the Life Sciences, Semiconductor and Electronics industries. They are used in applications requiring high speed and high accuracy such as assembling, handling or picking & placing of lightweight parts, but also in 3D printing and dispensing.

Cartesian/Gantry robots

Cartesian robots, also known as gantry or linear robots, move along multiple linear axes. Since these axes are very rigid, they can precisely move heavy payloads, though this also means they require a lot of space. They have about 15% market share and a 5-10% per year growth rate.

Cartesian robots are often used in handling, loading/unloading, sorting & storing and picking & placing applications, but also in welding, assembling and machining. Industries using these robots include automotive, packaging, food & beverage, aerospace, heavy engineering and semiconductor.

Delta/Parallel robots

Delta robots (also known as parallel robots) are lightweight, high-speed robots, usually for fast handling of small and lightweight products or parts. They have a unique configuration with three or four lightweight arms arranged in parallelograms. These robots have 5% market share and a 3–5% growth rate.

They are often used in food or small part handling and/or packaging. Typical applications are assembling, picking & placing and packaging. Industries include food & beverage, cosmetics, packaging, electronics/ semiconductor, consumer goods, pharmaceutical and medical.

Power & Force Limiting Collaborative robots

We add the term “Power & Force Limiting” to our Collaborative robot category because the standards actually define four collaborative robot application modes, and we want to focus on this, the most well-known mode. Click here to read a blog on the different collaborative modes. Power & Force Limiting robots include models from Universal Robots, the FANUC CR green robots and the YuMi from ABB. Collaborative robots have become popular due to their ease of use, flexibility and “built-in” safety and ability to be used in close proximity to humans. They are most often an articulated robot with special features to limit power and force exerted by the axes to allow close, safe operation near humans or other machines. Larger, faster and stronger robots can also be used in collaborative applications with the addition of safety sensors and special programming.

Power & Force Limiting Collaborative robots have about 5% market share and sales are growing rapidly at 20%+ per year. They are a big success with small and mid-size enterprises, but also with more traditional robot users in a very broad range of industries including automotive and electronics. Typical applications include machine loading/unloading, assembling, handling, dispensing, picking & placing, palletizing, and welding.

Summary

The robot market is one of the most rapidly growing segments of the industrial automation industry. The need for more automation and robots is driven by factors such as supply chain issues, changing workforce, cost pressures, digitalization and mass customization (highly flexible manufacturing). A broad range of robot types, capabilities and price points have emerged to address these factors and satisfy the needs of applications and industries ranging from automotive to food & beverage to life sciences.

Note: Market share and growth rate estimates in this blog are based on public data published by the International Federation of Robotics, Loup Ventures, NIST and Interact Analysis.

Picking Solutions: How Complex Must Your System Be?

Bin-picking, random picking, pick and place, pick and drop, palletization, depalletization—these are all part of the same project. You want a fully automated process that grabs the desired sample from one position and moves it somewhere else. Before you choose the right solution for your project, you should think about how the objects are arranged. There are three picking solutions: structured, semi-structured, and random.

As you can imagine, the basic differences between these solutions are in their complexity and their approach. The distribution and arrangement of the samples to be picked will set the requirements for a solution. Let’s have a look at the options:

Structured picking

From a technical point of view, this is the easiest type of picking application. Samples are well organized and very often in a single layer. Arranging the pieces in a highly organized way requires high-level preparation of the samples and more storage space to hold the pieces individually. Because the samples are in a single layer or are layered at a defined height, a traditional 2-dimensional camera is more than sufficient. There are even cases where the vision system isn’t necessary at all and can be replaced by a smart sensor or another type of sensor. Typical robot systems use SCARA or Delta models, which ensure maximum speed and a short cycle time.

Semi-structured picking

Greater flexibility in robotization is necessary since semi-structured bin picking requires some predictability in sample placement. A six-axis robot is used in most cases, and the demands on its grippers are more complex. However, it depends on the gripping requirements of the samples themselves. It is rarely sufficient to use a classic 2D area scan camera, and a 3D camera is required instead. Many picking applications also require a vision inspection step, which burdens the system and slows down the entire cycle time.

Random picking

Samples are randomly loaded in a carrier or pallet. On the one hand, this requires minimal preparation of samples for picking, but on the other hand, it significantly increases the demands on the process that will make a 3D vision system a requirement. You need to consider that there are very often collisions between selected samples. This is a factor not only when looking for the right gripper but also for the approach of the whole picking process.

Compared to structured picking, the cycle time is extended due to scanning evaluation, robot trajectory, and mounting accuracy. Some applications require the deployment of two picking stations to meet the required cycle time. It is often necessary to limit the gripping points used by the robot, which increases the demands on 3D image quality, grippers, and robot track guidance planning and can also require an intermediate step to place the same in the exact position needed for gripping.

In the end, the complexity of the picking solution is set primarily by the way the samples are arranged. The less structured their arrangement, the more complicated the system must be to meet the project’s demands. By considering how samples are organized before they are picked, as well as the picking process, you can design an overall process that meets your requirements the best.

Add Automation to Gain Safety and Control in Manufacturing

Industry automation not only has a positive effect on the improvement of production processes, it also significantly improves employee safety. New technologies can minimize the need for employees to work in dangerous situations by replacing them all together or by working cooperatively alongside them.

Overcoming fears of automation
Many workers fear technological progress due to the generally accepted view that robots will replace people in their workplaces. But their fears are conjecture. According to a study published in 2017 by scientists at the Universities of Oxford and Yale, AI experts predict a 50% chance of AI outperforming humans at all tasks within 45 years. But, instead of replacing all workers, there is a stronger chance AI will eliminate dangerous manual labor and evolve other roles. Following are a few examples.

- Automation in palletizing systems
  Before automation-based solutions entered factories, laborers had to do most work by hand. A work system based on the strength of the human body, however, does not bring good results. Workers tire quickly, causing a decrease in their productivity. And with time, health problems related to regularly carrying heavy daily loads also begin to appear. Until recently, employees of the palletizing departments struggled with these problems. But today, robots are carrying out the work of moving, stacking, and transporting products on pallets.

- Automation forging processes
  Also, until recently, forging processes in the metallurgical industry were performed with the help of human workers. There are still factories today in which blacksmiths are responsible for putting the hot metal element under the hammer to form the final shape of the product. Such a device hits with a force of several dozen tons, several times a minute. Being at the hammer is therefore extremely dangerous and may cause permanent damage to the worker’s health. Elevated temperatures in the workplace can also have negative effects on the body.
  
  At most businesses, forging processes are now fully automated. Robots specially prepared for such work feed the elements to the automatic hammer with their grippers. And sensory solutions help make the job safer by detecting the presence of people or undesirable elements within the working machine. The quality control of manufactured products is also extremely important and more easily controlled with an automated system.

- Automation in welding processes
  Welding processes are another dangerous activity in which automation is starting to play a key role. During welding work, toxic fumes are released from the gas lagging, which the welder regularly inhales. This can result in serious poisoning or chronic respiratory diseases. Welding also produces sparks which can lead to severe burns and worker blindness.
  
  Again, automation makes the process safer. High-class welding machines exist on the market that can work continuously, under human control. With such solutions, it is necessary to use appropriate protection systems to protect employees against possible contact with machines during work. Automation in this situation eliminates a dangerous role, and creates a new, safer, and, some would say, better work role.

Skillful design of automation systems
While factory automation eliminates some threats to workers, others often arise, creating the need for strict design plans prepared by specialists in this field. It is necessary to prepare the automation system in such a way that it not only ensures safety, it does so without reducing productivity or creating downtime which can cause the employee to bypass security systems. The systems blocking the working space of the machine should not interfere with the worker and the worker should not interfere with the system. Where possible, instead of a mechanical lock, an optical curtain at the feeding point should be used to stop the machine’s operation if a foreign object breaks the curtain’s beam of the light. Mechanical locks blocking access to the working space should be in places where it is not necessary to open the door frequently.

Successful human-machine collaboration
When designing automation systems in production companies, it is also necessary to remember that often a human is working alongside the robot. In palletizing systems, for example, a person is responsible for preparing the place for packing and cleaning the working area. For the work to go smoothly, it may be worth creating two positions next to each other. Mechanisms on the market today allow you to control the work of robots at a given position, assigning them to the workspace. Special security scanners prevent the robots from moving to positions where someone is working.

Add Depth to Your Processes With 3D Machine Vision

What comes to mind first when you think of 3D? Cheap red and blue glasses? Paying extra at a movie theater? Or maybe the awkward top screen on a Nintendo 3DS? Neither industrial machine vision nor robot guidance likely come to mind, but they should.

Advancements in 3D machine vision have taken the old method of 2D image processing and added literal depth. You become emerged into the application with true definition of the target—far from what you get looking at a flat image.

See For Yourself

Let’s do an exercise: Close one eye and try to pick up an object on your desk by pinching it. Did you miss it on the first try? Did things look foreign or off? This is because your depth perception is skewed with only one vision source. It takes both eyes to paint an accurate picture of your surroundings.

Now, imagine what you can do with two cameras side by side looking at an application. This is 3D machine vision; this is human.

How 3D Saves the Day

Robot guidance. The goal of robotics is to emulate human movements while allowing them to work more safely and reliably. So, why not give them the same vision we possess? When a robot is sent in to do a job it needs to know the x, y and z coordinates of its target to best control its approach and handle the item(s). 3D does this.

Part sorting. If you are anything like me, you have your favorite parts of Chex mix. Whether it’s the pretzels or the Chex pieces themselves, picking one out of the bowl takes coordination. Finding the right shape and the ideal place to grab it takes depth perception. You wouldn’t use a robot to sort your snacks, of course, but if you need to select specific parts in a bin of various shapes and sizes, 3D vision can give you the detail you need to select the right part every time.

Palletization and/or depalletization. Like in a game of Jenga, the careful and accurate stacking and removing of parts is paramount. Whether it’s for speed, quality or damage control, palletization/ depalletization of material needs 3D vision to position material accurately and efficiently.

I hope these 3D examples inspire you to seek more from your machine vision solution and look to the technology of the day to automate your processes. A picture is worth a thousand words, just imagine what a 3D image can tell you.

IO-Link Simplifies Connectivity on Robotic End-Effectors

In my last two blogs, Rise of the Robots: IO-Link… and Realize Productivity Gains with Smart Robotic Tooling , I shared how implementing IO-Link and incorporating pneumatic and electric smart grippers can help maximize your use of robotics in your applications. In this blog, I will discuss how you can get more from your robots through expanded use of end-effectors in your applications.

As pneumatic air and vacuum systems have been an integral part of automation projects of the past, these systems can also benefit from gains in intelligence moving forward. Smart vacuum generators can provide feedback on the operation of the system; for example, if cups are starting to wear or fail, the smart devices can be used to provide estimates on remaining service life through predictive maintenance calculations. Key components like process sensors, variable regulators, pneumatic grippers, and pneumatic valve manifolds are available with IO-Link technology at a reasonable price. More importantly, these devices dramatically simplify integration, installation, and maintenance with built-in diagnostics and parameterization tools. By utilizing smart pneumatics, we substantially reduce wiring complexity in new installations and expedite downtime repairs.

Easier I/O and Connectivity on Robotic End-Effectors

Figure 1 – An industrial robot with IO-Link I/O hubs and valve manifold control on the EoAT.

However, most people avoid adding these types of smart technologies to end-effectors due to cable management issues or the effort to put high-flex Ethernet or many conductors into the robot dress pack. With IO-Link and its use of standard conductors for communication, integrators and machine builders have been able to install already available conductors in the arm or use lower-cost high-flex sensor cables to communicate with IO-Link smart devices on the end of arm tooling (EoAT).

Smart I/O hubs allow for standard sensors to be used with simplified wiring and on large tooling, valve manifolds can be mounted and controlled on the EoAT (Figure 1). If tool change is needed for the application, non-contact wireless connectivity can send power and signal across an airgap, increasing application capabilities and functionality.

Manufacturers big and small have gained impressive intelligence at the robot’s end-effector using IO-Link electric grippers, smart pneumatics and tooling enabled with IO-Link sensors. As you look to your next robotic automation project, consider how you could reduce integration efforts, improve part quality, enhance production flexibility, gain more process visibility, and increase application capabilities of EoAT. To realize all the benefits of an industrial robot system and earn productivity gains in machine tending, assembly and material handling applications, smart grippers, smart sensors, and smart tooling (enabled by IO-Link) are a necessary part of your next smart factory project.