THE ROBOT REPORT_DESIGN WORLD_FEBRUARY 2022

A Supplement to Design World - February 2022 www.therobotreport.com

Swiss manufacturer automates CNC machine tending

INSIDE: • Why component makers should target cobots ............... 54 • A system for general in-hand object re-orientation ....... 64 • Tactile sensing provides advantages for cobots .............. 68

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The Robot Report

Why component makers should target cobots Collaborative robots are expected to see high growth in the years ahead, with end effectors representing the largest component market.

By Tim Dawson • Interact Analysis

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Recent years have been tough for the robotics and automation sector. Even pre-pandemic, the situation was challenging. That’s because, in 2019, automotive – a key consumer of industrial robots – saw weakened demand for traditional internal combustion vehicles.

Additionally, the electronics market was also stuttering, as was the machine builders’ market. And the expected 2020 rebound was stymied by the COVID-19 crisis, with the global industrial robot market falling by 11.1% in revenue terms, and by 5.9% in shipment terms.

But we are seeing a strong 2021 rebound as end customers resume much-delayed industrial automation projects. Beyond 2021, although activity will not be so enzied, we predict continued strong growth, not least because the experience of operating factories along social distancing lines has made even the most conservative production line managers think hard about the potential that automation offers. By 2024, the market for industrial and collaborative robots should achieve its previous 2018 revenue peak of $10.5 billion, with 410,000 units shipped.

Cobots expected to see 15%-20% annual growth Before we can understand the robot components sector, we need to look at the changing landscape where industrial and collaborative robots are concerned. In 2020, articulated robots accounted for nearly 75% of the combined $8.6 billion market value of industrial and collaborative robots. But by 2025, we A pin insertion predict that share will drop to 70%, while the combined market will exceed $11.1 billion. The reason for this application using an drop is that revenues om automotive, the biggest OnRobot RG2-FT gripper end-user of articulated robots, will fall om 33.4% and Universal Robots of the total market share in 2020 to 31.8% in 2024. collaborative robot.

Over the same period, the market for collaborative and SCARA robots will see faster than average growth, | Credit: OnRobot owing to the emergence of a range of new industries which will constitute strong new markets for these types of robots.

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The Robot Report Key Components In Industrial Robots - Revenues By Major Product 3000 2018

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While the overall industrial robot market is forecast to see annual growth of the order of 4%-5%, the collaborative robot sector is expected to enjoy 15%20% year-on-year growth. We are seeing a trend towards smaller, lower payload robots, as industries such as electronics continue to automate, and new industries such as battery manufacturing and PV manufacturing increasingly come on stream. SCARA robots with a payload usually of <10 kg, for example, are already the most widely used robot solution in the electronics sector, and have a broad range of applications, including material handling, assembly and inspection. This clear trend towards smaller robots will heavily impact the robot components sector.

Market for end effectors to top $2.5 billion by 2025 In our latest industrial robot component research, we consider all the key robot components: motors, drives, gearboxes, controllers, machine vision, sensors and end effectors. 2021 saw a strong double-digit rebound for components used in robots, and this year the market will exceed its 2018 size of just under $8 billion. This is due in no small part to prices being forced up by a number of factors including the semiconductor shortage and inflation. For 2022 and beyond we anticipate lower but still strong levels of growth, with a 4.1% CAGR out to 2025, and

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revenues projected to hit $9.3 billion that year.

As we can see on the chart above, end effectors represent the single largest component market. They will continue to do so for the foreseeable future, growing om a market value of $1.8 billion in 2020 to over $2.5 billion in 2025. The combined market for motors, drives and gearboxes is predicted to exceed the value of the end effector market.

There is a significant point to be made here: much of the motors/drives market is captive, with robot manufacturers usually producing these components for themselves. It’s the same story for robot controllers. But markets for other components, including end effectors, teach pendants, sensors and precision gearboxes are far more open to third party suppliers.

Machine vision and end effectors are seeing the highest growth as their penetration is increasing owing to the growing complexity of automation solutions being installed in manufacturing facilities. We are also seeing a slightly above average increase in market share for sensors as the more sophisticated and higher cost absolute encoders see increased use in applications requiring a high degree of accuracy.

The trend towards more compact robots with lighter payloads is creating a demand for smaller and lower power

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motors, drives and gearboxes which command lower market prices than their larger, more powerful counterparts. So, though these components will continue to dominate in terms of their combined market share, revenue growth will be at a slower pace than for other types of components. Meanwhile, the market for robot controllers is expected to keep pace with the overall industrial robot market.

Teach pendants, already well established in the cobot segment, are likely to see slightly increased growth as they are deployed in other robot applications.

Component manufacturers need to target ‘open’ market segments While the articulated robot sector, by virtue of its size, appears to represent the biggest single opportunity for component vendors, a significant part of the market is captive, with vendors enjoying strong, if not unbreakable, ties with their end customers. Instead, the eyes of component manufacturers hoping to grow their market share should be turning towards the more ‘open’ market opportunities offered by the burgeoning collaborative robot market where revenues are expected to exceed $1.1 billion by 2025, surpassing even the $865 million in revenues derived om SCARA robots. RR About the Author Tim Dawson is the senior research director and principal analyst for Interact Analysis’ industrial automation team. He uses his 20-plus year experience to develop best-inclass research for the manufacturing sector and is a equent speaker on all things research at conferences and industry trade shows across the country.

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The Robot Report

Swiss manufacturer automates CNC machine tending

OnRobot three-finger gripper and Doosan collaborative robot make production processes more efficient for family-owned business.

By The Robot Report Staff

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At the WEFAG production facility in Fällanden, Switzerland, production is supported by a collaborative application featuring a Doosan robot and the three-fingered 3FG15 gripper from OnRobot, loading and unloading a CNC machine. | Credit: OnRobot

WEFAG AG, a company based in Switzerland, has offered comprehensive CNC machining services for the last 35 years. It focuses on the CNC production of components and parts for the fluid technology sector.

Challenge: Shorter delivery times and smaller batches “We are increasingly dealing with extremely short delivery times and smaller batch sizes,” said Damian Hediger, managing director, WEFAG AG. “Besides, process reliability must be guaranteed – for all components.”

When machining workpieces with CNC machines, both absolute positioning accuracy and repeatability are crucial. This family-owned company focuses on precision and meeting the highest quality standards.

WEFAG is able to offer its clients a wide range of CNC machining services in an almost unlimited variety and combination of materials. The components go through various machining steps within the CNC machines, including turning, milling, drilling, vibratory finishing, assembly, refining and the final machining of the workpieces.

At WEFAG, an employee would spend the entire day placing workpieces in a machine and removing them. This made the company look for a way to make its production processes more efficient, without hindering employees’ tasks.

Solution A er much research and consideration, the company finally decided to automate. With modern technologies, many processes are already automated: loading and unloading, for example, which previously needed to be supervised by an employee. Today, WEFAG automates part of its process using a collaborative robotic arm om Doosan Robotics and an OnRobot 3FG15 gripper.

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OnRobot’s three-fingered 3FG15 enables precise gripping of objects of different shapes and sizes and was developed as a response to existing pneumatic three-finger grippers that are bulkier and less flexible. Its fingers can be mounted in three different positions, enabling different gripping forces and diameters with maximum stroke of 150 mm and a 15 kg (33 lb) payload, providing a strong, stable grip for both form fit (internal) or friction fit (external) gripping. | Credit: OnRobot

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Due to the limited space between CNC machines at the production facility, WEFAG was faced with another challenge: the process had to be automated in the most space-saving way possible.

Furthermore, the solution had to be

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accurate enough to precisely place a variety of components in the machine and remove them without damaging them.

Since the implementation of the automation project was not feasible for WEFAG alone, the company turned to cobot solutions AG, a Swiss company

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“WEFAG approached us with a request to develop a solution that would allow them to quickly and easily automate CNC machine tending,” explained Beni Zimmermann, managing director, cobot solutions AG. “Based on this request, we researched, tested and validated various grippers. The main challenge was the variety of workpieces, as it placed enormous requirements on the gripping range and flexibility of the gripper.”

WEFAG and cobot solutions decided on an application in which OnRobot’s 3FG15 gripper is mounted on a Doosan cobot arm. The electric, three-finger gripper stands out due to its high accuracy and flexible gripping range.

The 3FG15 enables precise gripping of objects of different shapes and sizes. Its fingers can be mounted in three different positions, enabling different gripping forces and diameters.

Thanks to its symmetrical rotational motion, the 3FG15 places objects precisely. It is also IP67 certified, which ensures that the device is dust tight and protected against temporary immersion.

This makes the 3FG15 the perfect solution for CNC machine operation.

Results in workpieces processed in less time.

Before the application was put into Employees now only need to load the operation, WEFAG and cobot solutions trays of the Easy Robotics Pro Feeder carried out various test runs. with the workpiece blanks and enter the “OnRobot let us use the gripper for necessary parameters in the software test purposes. This allowed us to test input screen. and validate the overall application The application then automatically beforehand,” said Jérôme Perdrizat, pulls out the trays of the Pro Feeder, development engineer, cobot solutions takes out the raw material and accurately AG. “The gripper’s firmware enables places it in the CNC machine. Once the various external interfaces. In this case, workpiece has been machined, the cobot we employ a serial communication arm removes the finished component interface, which allows us to use the from the machine with the help of the gripper in a very flexible manner. Since 3FG15 gripper and places it back in a free the gripper no longer needs to be set spot of the tray. manually, even employees with no prior “Thanks to automation, our processes knowledge can easily set up and operate are now much more efficient, and we the robot cell.” are able to reduce the workload of our To make the application even more employees. We expect the investment intuitive, cobot solutions developed to be paid off within a few months,” said software with input screens that can Hediger. be used to configure and automate a By employing the collaborative new task in just a few minutes. Thus, the application consisting of the Doosan application developed by cobot solutions robot arm and OnRobot 3FG15 offers a real added value for the robot gripper, WEFAG AG proved small and cell, including the initially mentioned medium-sized family businesses can small batch sizes. make production processes Once the application was up and substantially more running, it ran day and night to efficient using fully optimize the use of the automation. RR machines. This resulted in a significant increase The 3FG15 gripper is specifically

developed for machine-tending tasks and automatically centers workpieces, resulting in a strong, stable grip and precise placement in machine chucks. With a gripping force from 10 N to 240 N, the 3FG15 competes with much less flexible finger grippers. | Credit: OnRobot

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A system for general in-hand object re-orientation Model-free framework for robotic hand performs in-hand reorientation of 2,000-plus objects without prior knowledge of the object’s shape.

By Brianna Wessling • Associate Editor

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Most robotic end effectors don’t look or operate like human

hands. They’re designed for limited, specific purposes, and need to have intimate knowledge of the object they’re handling.

Researchers at the MIT Computer Science and Artificial Intelligence Lab (CSAIL) are working to create a model-free framework for a robotic hand that performs in-hand reorientation of 2,000-plus objects without prior knowledge of the object’s shape.

In-hand object reorientation has been a challenging problem in robotics due to high dimensional actuation space and the frequent change in contact state between the fingers and the objects. MIT’s learned policies show strong zero-shot transfer performance on new objects.

There are two core frameworks at play in CSAIL’s program: studentteacher learning and gravity curriculum.

Student-teacher learning is a training method in which researchers give a teacher network specific information about an object and its environment.

The teacher learns information that a robot wouldn’t easily be able to gather in the real world, like the specific velocity of an object.

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The teacher then gives this information to the student in the form of observations that a robot could make in the real world, like depth images of an object and joint positions of the robot. The student network then has the ability to learn from those observations and apply those techniques to a number of objects.

It was important to CSAIL’s team that a robot could handle objects with its hand facing upwards or downwards, which required extra training.

Robots struggle to handle objects when having to counteract gravity and without the support of a palm under an object.

Scientists taught the robot to counteract gravity gradually. First, they learned in a simulation without gravity. Then, researchers incrementally began to account for gravity, giving the simulation

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The Robot Report more time to learn how to hold objects even when gravity is at play.

With these frameworks, researchers found that their program was able to learn strategies for holding and manipulating objects that don’t rely on knowing the specific shape of the object.

“We initially thought that visual perception algorithms for inferring shape while the robot manipulates the object was going to be the primary challenge,” said MIT professor Pulkit Agrawal, an author on the paper about the research.

“To the contrary, our results show that one can learn robust control strategies that are shape agnostic. This suggests that visual perception may be far less important for manipulation than what we are used to thinking, and simpler perceptual processing strategies might suffice.”

CSAIL isn’t the first research lab to try to create anthropomorphic robot hands that operate like human ones. In 2019, OpenAI developed a program that trained

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a robot hand to solve a Rubik’s Cube.

While other developers had already trained robots to solve Rubik’s Cubes in seconds, OpenAI looked to train one to solve it without already knowing all possible orientations and combinations first. OpenAI researchers hoped teaching a robot to solve a Rubik’s Cube could help it to develop dexterity that could be used in handling a variety of objects. However, OpenAI recently disbanded its robotics research team due to the lack of large enough data sets to effectively generate reinforcement models.

CSAIL’s program was the most effective with simple, round objects, like marbles, with an almost 100% success rate. Not surprisingly, the program struggled the most with complex objects, like a spoon or scissors. The success rate for objects like these was 30%.

CSAIL’s program operated entirely within simulated scenarios, but the researchers are optimistic the work can

be applied to real robotic hands in the future.

“Our results show that model-free RL with simple deep learning architectures can be used to train policies to re-orient a large set of geometrically diverse objects.

Further, for learning with the hand facing downwards, we found that a good pose initialization obtained from a lifting policy was necessary, and the gravity curriculum substantially improved performance.”

“The most surprising observation is that information about shape is not required despite the fact that we train a single policy to manipulate multiple objects. Perhaps in hindsight, it is not as surprising – after all, humans can close their eyes and easily manipulate novel objects into a specific orientation.” RR

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DIGIT is a low-cost, compact, highresolution tactile sensor Meta (Facebook) designed for robotic inhand manipulation. | Credit: Meta

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Touch sensing technologies are quickly evolving to increase both the applicability and safety of cobots, especially for demanding applications that require handling delicate materials.

Tactile sensing provides advantages for cobots

Claudia Jarrett • EU Automation

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The first collaborative robot (cobot)

was manufactured in 1996. The system was designed for basic pick and place applications, and communicated with operators using motion resistance.

Cobots have come a long way since that time, working safely with humans. They are able to detect objects and people in their environment using vision sensors, and can even slow down, or stop functioning, in case of an unintended contact.

Modern cobots possess the ability to take corrective actions and minimize risks, but other sensing technologies, along with sophisticated so ware, allow them to do much more. For example, touch sensing technologies are quickly evolving to increase both the applicability and safety of cobots, especially for demanding applications that require February 2022

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The Robot Report handling delicate materials safely and precisely, such as healthcare.

Tactile sensors Currently, there are several types of tactile sensors used in cobots, including piezoelectric, piezoresistive, capacitive and elastoresistivity types. Piezoelectric technologies are used for gathering data from the cobot’s joints and transmitting it to the controller. In contrast, capacity sensors can act as proximity sensors, allowing the cobot to slow down when it detects the presence of an obstacle.

Although digital cameras have improved dramatically in the past decade, there has been remarkably little progress in tactile sensing since the 1980s. However, Meta (Facebook) recently launched a major initiative to develop tactile sensors using internal optics that are accurate, reliable, fast, and inexpensive. According to Ken Goldberg, William S. Floyd Jr. distinguished chair in engineering, UC Berkeley; co-founder & chief scientist, Ambi Robotics, the research community is excited to explore how Meta’s sensors could enhance robot manipulation.

Preventing collisions For some applications, detection sensors are placed outside of collaborative robots. These devices are used to recognize human workers when they enter the workspace, signaling to the system to slow down or stop on those occasions.

Although collisions can still happen, traditional cobot sensing modalities ensure that the impact is minimized. To improve the reliability (and hence safety) of collaborative robots, tactile sensors empowered with smart software can be embedded at the end of the cobot arm, which improves collision avoidance and increases movement efficiently.

Precise object handling and more Touch sensors are also useful for applications requiring precise object placement, such as loading parts into a fixture for machine tending. The sensing technology can find the exact part location and correct changes in the position or size of the raw stock material by measuring the insertion force.

Modern touch sensing systems use tactile sensors to capture information about an object in real time, such as its shape,

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The Robot Report size, and texture. The resulting data can then be used to produce a highly accurate description of objects, as well as the ability to recognize defects and changes. For example, early research from the USC Viterbi School of Engineering used embedded tactile sensors with conductive fluid to simulate human touch, resulting in a robot differentiating between the texture of wool and that of cotton.

With a more effective sense of touch, cobots can also be used in applications where they interact with more fragile or deformable objects. For example, tactile technology in surgical robots can be used to enhance precision and accuracy.

For this to be successful, multiple tactile sensors would have to be integrated using AI and machine learning.

increased productivity and efficiency for an increasing range of application types in an equally increasing number of markets.

About the Author Claudia Jarrett is the country manager for industrial automation components supplier EU Automation. In that role she oversees the operations of the company’s affiliation in the United States, while helping to develop new business and deliver growth via a multi-channel approach that has a significant positive impact on business.

Physical sensations With state-of-the-art sensors, actuators and software, cobots are now capable of experiencing physical sensations, allowing systems to ‘feel’ and identify many classes of objects – hard, soft, rigid, flexible, etc. – in the process.

Continuing tactile sensor advancements will allow cobots and humans to perform increasingly complex tasks while working in a collaborative manner. The result,

The DIGIT touch sensor is based on the GelSight style of sensor that was first conceptualized at MIT over a decade ago. | Credit: Meta

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3. Opt for fast delivery of your motor solution 4. Base the design on modularity - Not all AGVs do the same job and therefore having the flexibility to select a solution to match needed specifications is essential.

5. Prioritize safety – select motor options with integrated sensors.

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Flexible machine table fixtures – Robots are widely identified for their ability to handle workpieces. They also excel at automatically replacing fixtures and other peripheral tooling.

Automating multiple tasks – Use the robot to accomplish a secondary task. Instead of remaining idle during machining, the robot can automatically deburr workpieces.

Adjustable custom tooling – Use dove tails and adjustable mountings in EOATs to expand tools for changing conditions or reach requirements.

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