THE ROBOT REPORT-DESIGN WORLD-FEBRUARY 2019

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A Supplement to Design World - February 2019

• Inside NVIDIA’s Collaborative Robotics Lab_46 • Gripping Improvements_60 • Reinforcement Learning_66 • Automated Bin Picking_72 • Unique Cobot Case Studies_78

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A Supplement to Design World - February 2019

• Inside NVIDIA’s Collaborative Robotics Lab_46 • Gripping Improvements_60 • Reinforcement Learning_66 • Automated Bin Picking_72 • Unique Cobot Case Studies_78

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machine shop boosts capacity with cobot cell page 54

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The Robot Report

Inside NVIDIA’s collaborative robotics lab

NVIDIA opened its first full-scale robotics lab to drive breakthrough research in next-gen cobots.

The Robot Report named NVIDIA a must-watch robotics company in 2019 due to its new Jetson AGX Xavier Module that NVIDIA hopes will become the go-to brain for nextgeneration robots. Now there’s even more reason to keep an eye on NVIDIA’s robotics moves: the Santa Clara, Calif.-based chipmaker just opened its first full-blown robotics research lab.

Steve Crowe • Editor, The Robot Report

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The lab is in Seattle, just a short walk from the University of Washington. It’s tasked with researching the technologies for robots to operate safely and robustly around people.The robotics lab is led by Dieter Fox, senior director of robotics research at NVIDIA and professor in the UW Paul G. Allen School of Computer Science and Engineering.

“All of this is working toward enabling the next generation of smart manipulators that can also operate in open-ended environments where not everything is designed specifically for them,” said Fox. “By pulling together recent advances in perception, control, learning and simulation, we can help the research community solve some of the greatest challenges in robotics.”

The 13,000-square-foot lab will be home to 50 roboticists, consisting of 20 NVIDIA researchers plus visiting faculty and interns from around the world. NVIDIA wants robots to be able to naturally perform tasks alongside people in real-world, unstructured environments.

To do that, the robots need to be able to understand what a person wants to do

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and figure out how to help achieve a goal.

The idea for NVIDIA’s robotics lab came in the summer of 2017 in Hawaii.

Fox and NVIDIA CEO Jensen Huang met at CVPR, an annual computer vision conference, and discussed the exciting areas and difficult problems ongoing in robotics.

“NVIDIA dedicates itself to solving the very difficult challenges that computing can solve. And robotics is unquestionably one of the final frontiers of artificial intelligence. It requires the convergence of so many types of technologies,” Huang told The Robot Report. “We wanted to dedicate ourselves to make a contribution to the field of robotics. Along the way it’s going to spin off all kinds of great computer science and AI knowledge. We really hope the technology that will be created will allow industries from healthcare to manufacturing to transportation and logistics to make a great advance.”

NVIDIA said there are about a dozen projects currently under way. It will open source its research papers. Fox said NVIDIA is primarily interested, early on at DESIGN WORLD

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Tacile sensing, a missing element for commercialized robotic grippers, is a focus of NVIDIA’s robotics lab.

least, in sharing its software developments with the robotics community. “Some of the core techniques you see in the kitchen demo will be wrapped up into really robust components,” Fox said.

We attended the official opening of NVIDIA’s robotics research lab. Here’s a peek inside.

| NVIDIA

Mobile manipulator in the kitchen The main test area inside NVIDIA’s robotics lab is a kitchen the company purchased from IKEA. A mobile manipulator, consisting of a Franka Emika Panda cobot arm on a Segway RMP 210 UGV, will try its hand at increasingly difficult tasks, ranging from from retrieving objects from cabinets to learning how to clean the dining table to helping a person cook a meal.

During the open house, the mobile manipulator consistently fetched objects and put them in a drawer, opening and closing the drawer with its gripper. Fox admitted this first task is somewhat easy.

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The Robot Report Located in Seattle just a short walk from the University of Washington, NVIDIA’s robotics lab is tasked with driving breakthrough research to enable next-generation collaborative robots that operate robustly and safely among people.

The robot uses deep learning to detect specific objects solely based on its own simulation and doesn’t require any manual data labeling. The robot uses the NVIDIA Jetson platform for navigation and performs real-time inference for processing and manipulation on NVIDIA TITAN GPUs. The deep learning-based perception system was trained using the cuDNN-accelerated PyTorch deep learning framework.

Fox also made it clear why NVIDIA chose to test a mobile manipulator in a kitchen. “The idea to choose the kitchen was not because we think the kitchen is going to be the killer app in the home,” said Fox. “It was really just a stand in for these other domains.” A kitchen is a structured environment, but Fox said it is easy to introduce new variables to the robot in the form of more complex tasks, such as dealing with unknown objects or assisting a person who is cooking a meal.”

NVIDIA’s mobile manipulator includes a Franka Emika Panda cobot on a Segway RMP 210 UGV. | NVIDIA

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Deep object pose estimation NVIDIA introduced its Deep Object Pose Estimation (DOPE) system in October 2018 and it was on display in Seattle. With NVIDIA’s algorithm and a single image, a robot can infer the 3D pose of an object for the purpose of grasping and manipulation. DOPE was trained solely on synthetic data.

One of the key challenges of synthetic data is the ability to bridge the reality gap so that networks trained on synthetic data operate correctly with real-world data. NVIDIA said its one-shot deep neural network, albeit on a limited basis, has accomplished that. The system approaches its grasps in two steps. First, the deep neural network estimates belief maps of 2D keypoints of all the objects in the image coordinate system. Next, peaks from these belief maps are fed to a standard perspective-n-point (PnP) algorithm to estimate the 6-DoF pose of each object instance.

Tactile sensing NVIDIA had two demos showcasing tactile sensing, which is a missing element for commercialized robotic grippers.

One demo featured a ReFlex TakkTile 2 gripper from RightHand Robotics, which recently raised $23 million for its piecepicking technology. The ReFlex TakkTile 2 is a ROS-compatible robotic gripper with three fingers. The gripper has three bending DOFs and 1 coupled rotational DOF. Sensing capabilities include normal pressure sensors, rotational proximal joint encoders, and fingertip IMUs. The other demo, run by NVIDIA senior robotics researcher Karl Van Wyk, featured SynTouch tactile sensors retrofitted onto an Allegro robotic hand from South Korea-based Wonik Robotics and a KUKA LBR iiwa cobot. “It almost feels like a pet!” said Huang as he gently touched the robotic fingers, causing them to pull back. “It’s surprisingly therapeutic. Can I have one?”

Van Wyk said tactile sensors are starting to trickle out of research labs and into the real world. “There is a lot of hardening and integration that needs to happen to get them to hold up in the real world, but we’re making a lot of progress there. The world we live in is designed for us, not robots.”

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The Robot Report NVIDIA CEO Jensen Huang tests SynTouch tactile sensors on an Allegro robotic hand from Wonik Robotics and a KUKA LBR iiwa cobot. | NVIDIA

The KUKA LBR iiwa wasn’t using any vision to sense its environment. “The robot can’t see that we’re around it, but we want it be constantly sensing and reacting to its environment,” said Van Wyk. “The arm has torque sensing in all of the joints, so it can feel that I’m pushing on it and react to that. It doesn’t need to see me to react to me.

“We have a 16-motor hand over with three primary fingers and an opposable thumb, so it’s like our hands. The reason you want a more complicated gripper like this is you want to eventually be able to manipulate objects in your hands like we do on an daily basis. It is very useful and makes solving physical tasks more efficient. The SynTouch sensors measure

ReFlex TakkTile 2 gripper from RightHand Robotics.

| NVIDIA

Attendees test a control system developed by Dr. Madeline Gannon that enables a robots to mimic human movements. | NVIDIA

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The Robot Report

NVIDIA CEO Jensen Huang (left) and Senior Director of Robotics Research Dieter Fox at NVIDIA’s robotics lab. | NVIDIA what’s going on when we’re touching and manipulating something. Keying off those sensors is important for control. If we can feel the object, we can re-adjust the grip and the finger location.”

Human-robot interaction Another interesting demo was NVIDIA’s “Proprioception Robot,” which is the work of Dr. Madeline Gannon, a multidisciplinary designer nicknamed the “Robot Whisperer” who is inventing better ways to communicate with robots.

Using a two-armed ABB YuMi and a Microsoft Kinect on the floor underneath the robot, the system would mimic the movements of the human in front of it.

“With YuMi, you don’t need a roboticist to program a robot. Using NVIDIA’s motion generated algorithms, we can have engaging experiences with lifelike robots.”

You might have heard of Gannon’s recent work at the World Economic Forum in September 2018. She installed 10 industrial robot arms in a row, linking them to a single central controller. Using depth sensors at the bases of the robots, they tracked and responded to the movements of people passing by.

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“There are so many interesting things that we could spin off in our pursuit of a general AI robot,” said Huang. “For example, it’s very likely that in the near future you’ll have ‘exo-vehicles’ around you, whether it’s an exoskeleton or an exo-something that helps people who are disabled, or helps us be stronger than we are.” RR

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The Robot Report

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By Steve Crowe | Editor Robot Report

Universal Robots and OnRobot help FT Produktion save 500 hours when manufacturing 150,000 parts.

F

Founded in 1983 and based in the town of Åseda in Southern Sweden, FT Produktion is a machine shop that supplies metal parts measuring up to 7.5 meters in size to a variety of industries. The company has 25 employees that hail from Sweden, Norway, Lithuania, Syria, and Thailand who work in tandem with neighboring company Profilgruppen.

To keep up with an increase in orders, FT Produktion in 2017 made the largest investment in its history by renovating much of its machinery, which included the addition of a flexible collaborative robotics cell.

Challenge No machine shop would turn down global industry leaders like Volvo, Renault, and Scania when they come calling with orders – even if it means pushing capacity and productivity to the limit. With an annual revenue of just over $2 million, turning down new business would mean limiting FT Produktion’s growth opportunity.

As a result of the automation, FT Produktion saves 500 hours when manufacturing a series of 150,000 parts. | Universal Robots

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The Robot Report FT Production expects to add another two collaborative robots to its facility in the next couple years. | Universal Robots

To keep up with an increase in orders, FT Produktion in 2017 made the largest investment in its history by renovating much of its machinery, which included the addition of a flexible collaborative robotics cell.

“The demand for productivity continues to grow. But relevant manpower is hard to come by these days,” said Joakim Karlberg, who owns and operates the family-owned FT Produktion with his sister. “Thanks to automation, an operator can keep three or four machines running at once without stress. We try to eliminate monotonous manual tasks by roboticizing the high-volume production runs, giving employees more time to work on smaller runs with greater variation in their tasks.”

Solution FT Produktion manufactures 650 different aluminum profiles and fills 7,000 orders annually, with production runs ranging from five to 150,000 units. Given the wide range of tasks and volumes, flexible automation was essential. A single robot solution must be capable of handling many different parts with minimal downtime when reconfiguring from one production run to the next.

FT Produktion’s robot cell includes a UR5 cobot from Universal Robots, two RG2 robot grippers from OnRobot and a

ProFeeder from EasyRobotics. According to Universal Robots, this trio of Danish cobot solutions has now provided automated machine operation at FT Produktion for more than a year. Karlberg said the investment paid for itself in 9 months. “It’s one of the best business decisions we’ve ever made,” he said.

Results Since the collaborative robots cell has been up and running, a batch of 150,000 units can be manufactured in less than two months with the robots working up to 16 hours a day, five days a week. Once every 25 seconds, the RG2 gripper on the UR5 cobot removes a raw part from the ProFeeder’s parts tray in the mover and inserts it in a CNC machine. In the same robot arm movement, the other gripper takes a finished part out of the CNC machine and places it in a container. As a result, FT Produktion saves 500 hours when manufacturing a series of 150,000 parts.

In the past, an employee had to manually insert four parts at a time in a

“Now my job is less monotonous and much more interesting. It’s fun to watch a robot at work,” said Mikael Andersson, a machine technician and robot programmer at FT Produktion.

“I would like to see more of them here. This cobot solution is so easy to reconfigure that even small production runs are feasible in terms of costs.”

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The Robot Report

Case Study Breakdown Company: FT Produktion Location: Sweden Industry: Metal & machining Employees: 25 Challenges: Production and workforce limitations Robots: UR5 cobot arm, two OnRobot RG2 grippers; EasyRobotics ProFeeder Tasks: CNC machine tending; pick and place Value Drivers: Flexibility easy programming; plug-and-play accessories; collaborative and safe Results: 500 hours saved on 150,000 unit batches; increased job satisfaction ROI: 9 months

CNC machine, wait for 90 seconds, and then repeat the process for the duration of the workday. Now the solution requires just five minutes to manually fill one of the ProFeeder’s two movers. When the robot has emptied one of the mover’s parts trays, the operator removes the empty mover and clicks a filled mover into place in the ProFeeder, allowing the CNC to rapidly resume operation.

Karlberg expects to add another two cobots to FT Produktion’s facility in the next couple years. “It seems like there’s no end to this economic upswing,” he said, “and therefore no end to our bottleneck issues. So we can already see many more opportunities for improved optimization and automation.”

Another benefit of turning to automation, according to FT Produktion, is the increase in employee job satisfaction. Mikael Andersson is a machine technician at FT Produktion.

One year ago, he also became a robot programmer and robot operator following two days of theoretical and hands-on training at Edströms.

“It was actually easier to use robots than I had expected. If you’re standing there all day, feeding the same machine with parts, you can feel it in every joint

per

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of your body at the end of the day,” he said. “Now my job is less monotonous and much more interesting. It’s fun to watch a robot at work. I would like to see more of them here. This cobot solution is so easy to reconfigure that even small production runs are feasible in terms of costs.”

The new generation of cobot arms also requires high-performance accessories. For example, the functionality and user-friendliness of grippers ultimately determines the scope and variety of tasks and parts that can be automated, as well as the ROI time for automation investments.

“It only takes 10 seconds to change to a different part on the robot’s display,” said Anderson. “The robots are not taking any jobs. We can fill more orders and deliver more detailed parts with higher quality when we use the robots.

They have been easy to install and adjust, so we’ve been able to do it all ourselves, with just a little help over the phone from Edströms.” RR

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Once every 25 seconds, the OnRobot RG2 grippers simultaneously remove a part from the ProFeeder tray and take a finished part out of the CNC machine. | Universal Robots

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The Robot Report

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advances in machine manipulation

Robotic grasping is continuing to

evolve and diversify in response to

widening applications.

Robots have used multiple types of grippers for years, but the push for more manual dexterity has become more urgent because of rising demand from e-commerce order fulfillment and other applications. Human-level manipulation of various objects is a difficult technical challenge, and robotics developers and vendors have responded with a range of solutions.

Eugene Demaitre • senior editor The Robot Report

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From claw, parallel, and rotary grippers to bellows, magnetic, and vacuum grippers, robotic manipulation has evolved to meet the needs of industries including automotive and electronics manufacturing and food processing.

With the rise of collaborative robot arms, or cobots, machine manipulation has diversified to handle a widening variety of objects. Industrial robots could use parallel or rotary grippers for fast, repetitive handling of identical parts, but cobot users need flexibility and safety over throughput.

In addition to consumer packaged goods, markets for robotic grippers include machine tending, pharmaceuticals, plastics, and agriculture and food processing. Even more specialized, in terms of performance requirements, are end-of-arm tools (EOATs) for undersea exploration, telesurgery, and hazardous materials handling.

The makers of robotic grippers have designed their products to work with multiple models of industrial and collaborative robot arms. Advances

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in prosthetics have paralleled the development of electromechanical and pneumatic grippers. Better sensing and controls, lighter materials, and more efficient use of space and power continue to make grippers an area of robotics worth watching. Here are six advances to follow: 1 The right tool for the job Robot grippers must be appropriate for the size, weight, and rigidity of the objects they handle, as well as the required precision. Shoeboxes are much easier to move than screws, a large pallet, or shrink-wrapped shirts, for example.

As with robots in general, cobot EOAT designers and users should consider the competing demands for cycle time, torque, and shape and force limitations for safety. Environmental contaminants and patterns of contact and wear are other factors to note.

Several companies, such as Destaco, offer customizable end effectors to work with specific items or materials.

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The VG10 from OnRobot is a vacuum gripper that doesn’t use external compressed air to create suction. It creates an internal air supply with self-contained airflow and has two individual vacuum channels. | OnRobot

offering suites of solutions that include grippers, sensors, and software. For example, Quebec City-based Robotiq has grown not only by selling products, but also explaining the value of such combinations.

Much of the attention in the collaborative robotics space has been on developing end effectors that are as easy to install and use as the robot arms they’re mounted on. Rather than trying to use a single end

A choice of grippers helps this dual-arm YuMi robot pick up 300 unknown objects per hour with a 95 percent success rate. This is part of the Dex-Net 4.0 project from the University of California, Berkeley that lets the robot dynamically choose between two grippers.

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The Robot Report While humanlevel dexterity might not be the goal of developers and suppliers — surpassing it is — new materials and types of grippers are making robots more reliable and flexible than ever.

Schunk’s line of end-of-arm modular systems, which is exclusively for Universal Robots, includes this SHS 50 manual tool changer.

effector to do everything, tool changers are designed to make it easier to quickly swap out robotic grippers.

Major vendors of “automatic manual” tool changers include ATI Industrial Automation, Applied Robotics, SAS Automation, Schunk, and Stäubli.

2 Nimbler fingers While human-level dexterity might not be the goal of developers and suppliers — surpassing it is — new materials and types of grippers are making robots more reliable and flexible than ever.

Robotiq’s three-finger adaptive grippers are designed to minimize tool changes with a high degree of customizability, a patented mechanism for high precision, and plug-and-play kits for UR arms.

OnRobot’s RG-FT smart gripper, which debuted at last year’s IMTS, includes six-axis force and torque sensors. The company’s new Gecko gripper uses biologically inspired microscopic stalks to grasp flat objects more effectively than current electrostatic methods.

Not all robotic grippers use fingers or clamps. Researchers at the Rochester Institute of Technology have studied elephant trunks for grasping, and Festo has copied an octopus tentacle.

OnRobot’s Polyskin Tactile Gripper is an example of robot grasping with tactile sensors. Force sensing can improve manipulation of fragile items such as eggs or lightbulbs, as well as safety in processes where robots are collaborating with humans.

3 New approaches to suction A new generation of vacuum grippers don’t require an external air supply to create suction. This results in suction grippers that are less noisy, more energy efficient and have a smaller overall footprint. Researchers at Saarland University have developed “artificial muscles” to create suction.

The technology is based on the shape memory properties of nickel-titanium alloy, which is able to “remember” its shape and to return to that original shape after it has been deformed. By

| Schunk

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switching on and off an electrical current that flows to that muscle, it contracts to create a vacuum within the rubber membrane and relaxes to release it.

Odense, Denmark-based OnRobot offers a vacuum gripper that doesn’t use external compressed air with its VG10, which it acquired when it bought Purple Robotics in 2018. The VG10 features a payload up to 10kg, an internal air supply with self-contained airflow and two individual vacuum channels that can simultaneously use different suction power and lost grip detection.

“More electrical grippers are coming out and we’ll continue to see that,” said Esben Østergaard, co-founder and CTO of Universal Robots. “Five or 10 years ago everything was pneumatic, now everything is going electric.”

4 Combined approach In the past year, several companies have combined gripper types for more reliable pick-and-place operations. The gripper for RightHand Robotics’ RightPick combines a vacuum gripper and fingers and uses computer vision and machine learning in the cloud.

Soft Robotics’ SuperPick system is controlled by AI and adds a vacuum to its compliant grippers.

Researchers at the University of California, Berkeley, recently created Dexterity Network (Dex-Net) 4.0 for “ambidextrous robot grasping policies.”

In this approach, two-armed robots were trained for both parallel-jaw and vacuum gripper grasping on a library of 5 million synthetic images. With two arms and grippers, the robot could choose which gripper to use for a particular task.

“On a physical robot with two grippers, the Dex-Net 4.0 policy consistently clears bins of up to 25 novel objects with reliability greater than 95% at a rate of more than 300 mean picks per hour,” reported the UC Berkeley team.

OnRobot’s creation this past year from the merger of OnRobot, OptoForce, and Perception Robotics illustrates a different kind of consolidation. The number of tool providers is likely to change, even as EOATs proliferate and cobot makers challenge Universal Robots for dominance.

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The Robot Report

With a gripper that combines fingers and suction, RightHand Robotics’ RightPick recently achieved a pick rate of 800 to 1,000 units per hour at a DHL event.

5 Improved AI, sensors Among the recent developments for robotic manipulation is the addition of machine learning, fed by large data sets and connected via the Industrial Internet of Things. As with other forms of artificial intelligence, this depends on the amount of data available, whether processing for machine vision occurs at the edge or in the cloud, and the types of sensors used.

For instance, Vicarious AI is developing “artificial general intelligence for robots.”

The San Francisco-based company’s “virtual cognitive computer” software uses images from cameras to instruct robot grippers on how to approach an object.

In other words, the robot can teach itself through observation and comparison with existing models.

Researchers at Singapore’s Agency for Science, Technology, and Research (A*STAR) Institute and Japan’s CNRS-AIST Joint Robotics Laboratory have created an algorithm that allows a robot to compare a shape with its own hand to determine how to best move an object.

NVIDIA’s Deep Object Pose Estimation System enables robots to determine the position of an object using data from a single image.

IAM Robotics’ Swift can pick items from shelves and place them into bins, and it can be run in fleets.

The MOVO Beta from Kinova, which makes lightweight arms, grippers, and other robotics parts, is intended as a research platform. It runs on the Robot Operating System (ROS) and includes simultaneous localization and mapping (SLAM) capabilities.

Cobots getting a grip Software and hardware engineers at robotics developers, vendors, and user organizations have more options than building or picking a specialized tool for each task. The increasing breadth of EOAT, as well as more sophisticated sensors, data analysis, and connectivity, are driving a new generation of machine manipulation.

The use of EOAT in non-automotive applications increased by 20% in 2017, compared with 14% in automotive ones, according to the Robotic Industries Association.

The global collaborative robot market, which is a small but expanding portion of industrial automation, will experience a compound annual growth rate of 49.8%, predicted ABI Research. It will grow from $292 million in 2017 to $1.23 billion by 2025, said ABI. This growth will driven by small and midsize enterprises and demand for safer robots that can handle a variety of items. RR

6 Mobile manipulation Sometimes described as the “holy grail” of e-commerce automation, the combination of cobots with mobile platforms has been elusive until recently.

Fetch Robotics has been known more for its Freight autonomous mobile robots, but its Fetch mobile manipulator is available for researchers.

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The Robot Report - Case Study

Reinforcement learning shows promise for industrial robots Hype about reinforcement learning has a valid motivation, but it is not the main ingredient for success.

By Eugen Solowjow & Juan Aparicio Siemens

Industrial robots deployed today across various industries are mostly doing repetitive tasks. The overall task performance hinges on the accuracy of their

Figure 1: Reinforcement learning loop for robot control. | Siemens

controllers to track predefined trajectories. The ability of robots to handle unconstructed complex environments is limited in today’s manufacturing.

Two examples are flexible picking of previously not encountered objects or the insertion of novel parts in assembly tasks. There are numerous examples of spectacular robot demonstrators exhibiting dexterity and advanced control, e.g. robot Fanta challenge, or robots playing ping pong. However, these applications are hard to program and maintain, usually they are the output of a PhD thesis, and they haven’t made the leap into manufacturing.

Endowing machines with a greater level of intelligence to autonomously acquire skills is desirable. The main challenge is to design

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The Robot Report

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adaptable, yet robust, control algorithms in the face of inherent difficulties in modeling all possible system behaviors and the necessity of behavior generalization.

Reinforcement learning (RL) methods hold promise for solving such challenges, because they enable agents to learn behaviors through interaction with their surrounding environments and ideally generalize to new unseen scenarios.

RL is a principled framework that allows agents to learn behaviors through interactions with the environment. As opposed to traditional robot control methods, the core idea of RL is to provide robot controllers with a high-level specification of what to do instead of how to do it. Thereby, the agent interacts with the environment and collects observations and rewards.

The RL algorithm reinforces policies that yield high rewards. RL can be distinguished in value-function-based methods and policy search. In policy search, robots learn a direct mapping from states to actions. In valuefunction-based approaches, robots learn a value function, an intermediate structure that assesses the value of an explicit state, and derive actions from the value function.

Both policy search and value-functionbased approaches can either be model-based or model-free. Model-free methods do not consider the dynamics of the world. Modelbased methods incorporate a model of the world dynamics, which is learned from data as well.

Control methods

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As we can see, robot control methods can be grouped along a continuum where on one end we find “rigid� feedback control laws, which are hand-engineered, incorporate domain knowledge and the control structure is not adapted by data. On the other end of the spectrum we have RL methods, which allow learning control policies purely from observed data. Both methods have advantages and disadvantages.

Traditional feedback control methods can solve various types of robot control problems very efficiently, such as trajectory tracking in free space, by capturing the structure with explicit models, such as rigid body equations of motion. However, many control problems in modern manufacturing deal with contacts and friction, which are difficult to capture with first-order physical modeling. And if higher-level reasoning is required (where to pick in bin

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The Robot Report Figure 2: Insertion use cases solved with a combination of conventional control and RL. | Siemens

picking problems, for example) current robot controllers lack flexibility. Applying feedback control design methodologies to these kinds of problems often results in brittle and inaccurate controllers, which have to be manually tuned for deployment.

RL, on the other hand, can, in principle, learn any control structure. However, for real-world robots, the continuous exploration space is large and, hence, large amounts of data and, therefore, long training times are required.

Moreover, unlike conventional feedback control, convergence and stability statements are difficult to derive for RL methods.

Just to name two recently popularized use cases for both control methods: Boston Dynamics is known for deploying conventional feedback control laws (more precisely Funnel Control) for all its well-known demonstrations.

Google, on the other hand, has shown that RL is capable to arrive at a robot controller for bin picking simply through trial and error. However, several months of training on a robot farm were required to achieve the required control performance.

Flexible manufacturing After realizing that robot control methods comprise a continuum, where the underlying dimension is how much influence online data

Figure 3: Siemens Robot Learning Challenge. | Siemens

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has on shaping the control algorithm, it seems that best control performance for flexible manufacturing has to combine both traditional control theory and data-driven RL. Traditional control can provide guarantees in safety and performance, while RL can bring flexibility and adaptability, if tuned correctly. In a way, RL removes the specificity needed at the engineering stage, where controls are designed. It targets to achieve the same performance than a carefully engineered feedback control algorithm, but without the need of tedious programming and rules.

We suggest decomposing robot control pipelines, which consist of perception, state estimation, control etc, into sub-problems that can be explicitly solved with conventional methods and sub-problems, which are approached with RL. The final control policies are then superpositions of both datadriven components and control policies from first-order models. Our approach combines the benefits of traditional control theory (e.g. data-efficiency) with the flexibility of RL. For example, position control is taken care of by a PID controller, and RL contributes the control part that deals with friction and contacts. We have conducted studies on different industrially relevant use cases, which amongst others include robots to perform real-world assembly tasks involving contacts and unstable components.

Figure 2 illustrates two assembly use cases, where conventional feedback control was combined with RL to solve complex assembly tasks in a flexible manner. Subfigures (a) and (b) show how a gear wheel is placed on a shaft. The use case is part of the Siemens Robot Learning Challenge. The robot required less than seven iterations to learn the required control policy. Subfigures (c) and (d) show a different use case for which www.designworldonline.com

the same control algorithm was used as for (a) and (b). Again, after less than seven iterations, the robot learned the control policy.

A challenge persists in this approach.

Seven iterations may seem reasonable for lab setups, but they entail an inherent risk, as every iteration in a friction-rich environment has the danger to damage the part in contact with the gripper.

Accurate sensors and adequate constrain management can alleviate the problem.

Those are better handled in the pipelines that use traditional control, and can filter the output of the RL commands. Note that a certain amount of engineering is still needed to ensure that the robot is not in a lock position, unable to move because of the constrains. In these situations, calling a human for help may be the best course of action. In addition, in order to reduce the number of real world iterations, novel approaches in simulation to reality gap (sim2real) have been proven to accelerate the learning.

Conclusion As a conclusion, we believe the current hype of reinforcement learning around robotic applications has a valid motivation; however, it is not the main ingredient to guarantee success. Endto-end learning approaches have shown poor performance in tasks that require precision. In an analogy that we like to make, if you want to make a chocolate cake, chocolate (reinforcement learning in this case) is not the main ingredient. You still need eggs, flour, etc. These “less-sexy� ingredients are in our case traditional control approaches. They are the base to build a successful flexible robotics application.

We strongly believe that to accelerate robot learning research and its adaption in industry, we need a benchmark for the research community. We have seen that the ImageNet benchmark, which was introduced by Fei Fei Li in 2009, became the catalyst for image classification with deep learning. Machine performance for classification surpassed human capabilities in 2015. Benchmarks accelerate research because they facilitate reproducibility and allow comparison of research. RR

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The Robot Report

Is fully automated bin picking finally here?

A truly universal bin picking

solution needs to be usable

by non-experts, configurable

in a few hours, provide

sophisticated path planning that works with little or no tuning, and be cost-effective for SMEs.

W

With the lowest unemployment in nearly 50 years, the need for automated bin picking is urgent. In the U.S.,

where 38 percent of the manufacturing labor force moves parts between bins and manufacturing machines, 500,000 jobs remain unfilled. The automation industry is trying to meet that need: every automation trade show adds a few new companies that claim to have finally solved the puzzle of bin picking.

By Eric Truebenbach | director of corporate development | Teradyne

Automatica 2018 in Munich, for example, had no fewer than 14 bin-picking demonstrations. But even at large manufacturers, few bin-picking stations can be found. And at small and midsized enterprises (SMEs), the number is close to zero.

Why is the adoption rate of automated bin picking systems so low, when the need is so great and so many vendors claim to offer a solution?

Automated bin picking a complex problem The simple answer is that for the most part, automated bin picking is only a partially solved problem. Picking randomly positioned parts from a bin and placing them precisely into a machine is a simple task for a human but a daunting task for robots. Robots must be able to grasp parts in an infinite number of orientations and reach deep into the corners of the bin all while avoiding collisions with the bin, other parts, or the work cell itself.

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The Robot Report Add waypoints, revise algorithm on controller

Write robot program on controller Buy components

Assemble hardware

Configure software

Pick & place a few hundred parts

Pick a few parts

Configure bin picking program on processor

No Acceptable?

Yes

Program with acceptable reliability

(Repeat for many weeks) No

Picks are OK?

Yes

Typical installation and programming for a commercial bin picking system. (Credit: Energid)

Figure 1: Typical installation and programming for a commercial bin picking system

Currently, bin picking is able to be that runs on a separate computer. A Robots must be able fully automated only with a huge system robotics expert is expected to integrate integration project that requires multiple the sensor, computer, software, and robot to grasp parts in advanced technologies to work together. controller, and then write a program to Configure bin retrieve the location of each part and Pick & Pick & Yes picking program Buyan infinite Assemble number with These include: figure out how to get it toProgram the placement place a place on teach components hardware high reliability • A 3D model of few the parts part, the bin, the target.

Figure 1.)

OK? (Finish up(See in half a day) of orientations and pendant robot end effector, the placement Creating a general path-planning target, and any environmental No algorithm starting with infinite variation reach deep into the obstacles in part orientation is a near-impossible waypoints, revise • A robot model of one or moreAdd ways to pick up task. At best, weeks to months of Write corners of the bin algorithm on controller the part experimentation and tuning yields a program on with the end effector and No Figure 2: Installationcontroller and programming for an integrated with pathalgorithm planningwith unreliable

deposit it at the placement target system specialized all while avoiding Pick & place • A 3D sensor to map the bin performance.

Program with Yes Buy Assemble Configure Pick a few Because Accept • Image analysis software to locateaeach bin picking is so acceptable complex, collisions the components hardware with software few parts hundred able? part and potential obstacles in the bin specialists have attempted toreliability apply Configure • Path planning software to find a parts deep-learning techniques. So far, the bin bin, other parts, or picking collision-free route from the part’s results have been disappointing, at least program on point to the placement target pick-up for industrial use.

Artificial intelligence (Repeat for many weeks) processor the work cell itself.

• Robot control software to maneuver (AI) is effective for applications such

are and part along as image classification and voice the robot, endPicks effector, OK? the route recognition, where lower accuracy No Yes

is acceptable. But AI simply doesn’t There are commercial bin picking systems have the reliability and accuracy that that include some of these components manufacturers need to replace human Figure 1: Typical installation and aprogramming for a commercial bin picking system and address subset of bin picking operators for bin picking. challenges. Usually these systems combine With the large integration and a 3D sensor with image analysis software programming effort required, it’s no

Buy components

Assemble hardware

Configure bin picking program on teach pendant

Pick & place a few parts

Pick & place OK?

Yes (Finish up in half a day)

Program with high reliability

No

Installation and programming for an integrated system with path planning. (Credit: Energid)

Figure 2: Installation and programming for an integrated system with path planning

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The Robot Report

Energid’s Actin software, proven in mission-critical applications including NASA spacecraft, autonomous robots on oil drilling platforms, surgical applications, and industrial automation, works behind the scenes to automate the path planning for each part in real time, improving performance and reliability, without the need for user-defined waypoints and manual tuning. | Energid

per

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surprise that most real-world bin picking deployments are found at large, sophisticated manufacturers such as automotive OEMs. But most bins and manufacturing machines are found at SMEs, with 69% of the industrial labor force worldwide. SMEs have the greatest labor shortages but the least capital and expertise required to create a binpicking system.

Path planning key to ease of use and reliability A truly universal bin picking solution needs to be usable by non-experts, configurable in a few hours, provide sophisticated path planning that works with little or no tuning, and be costeffective for SMEs. This solution will truly democratize bin picking. (See Figure 2.)

The underlying software in a completely pre-integrated system manages all the complexity. All the system components (sensor, imaging and path planning software and coprocessor) are pre-configured and plug in to the robot controller with no setup required, reducing installation time to hours.

All programming is integrated with the robot’s teach pendant, enabling bin picking actions to be freely mixed with standard robot commands using the same programming interface. Training time is minimal.

Setup and programming are guided by a series of wizards that mimic the

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training a human operator would have to go through, e.g., how to pick up a part, where to put it down, obstacles to avoid, etc. (See Figure 3.)

Planning a unique, collision-free path for each part in the bin to the placement target is by far the trickiest challenge for bin picking, and is often left to the ingenuity of an automation expert. Path planning is the main determinant of system reliability, and if not done well will result in collisions, parts left in the bin or dropped, and missed targets.

Finally, a bin-picking solution for SMEs So, can we declare that the bin picking problem has been solved once and for all, and manual machine tending is a thing of the past? Not quite, since there will always be applications that are just too complex for automation and will require human operators for the foreseeable future.

However, advances in cost-effective cobots, 3D imaging, and intelligent motion control software have aligned to enable a new generation of bin picking solutions that can handle many more of today’s machine-tending applications, and enable manufacturers to shift their limited resources to higher-value tasks. More importantly, bin picking has become accessible and cost effective for SMEs. RR

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The Robot Report

Cobots bring automation to novel applications From greenhouses to the operating room, cobots are coming.

By definition, collaborative robots are designed to be safer to operate around humans. Thanks to their smaller scale, affordability, and flexibility, cobots offer the benefits of industrial automation to new markets, including at small and midsize enterprises.

Eugene Demaitre • senior editor The Robot Report

Cobot arms, compliant grippers, and mobile platforms equipped with collision-avoidance capabilities take robots from inside fenced work cells to production lines and other venues.

Better sensors and deep learning are also enabling new uses.

Here are some unconventional examples of how cobots can serve in areas beyond manufacturing, pick-andplace operations, and machine tending.

GROWBOT uses a Sawyer cobot arm to help greenhouse workers pick plants. | Rethink Robotics 78

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Robots to pick plants with machine learning The GROWBOT (Grower-Reprogrammable Robot for Ornamental Plant Production Tasks) project at King’s College London is using a Sawyer cobot arm to help greenhouse workers pick plants.

GROWBOT is intended to let nonexpert users work with robots for repetitive tasks and to help relieve shortages of seasonal labor. It uses machine learning to flexibly automate the handling of seedlings, herbs, and other plants.

“Handling plants involves a great amount of uncertainty in sensing, and there’s a reasonably high performance requirement in terms of throughput,” said Aran Sena, a postgraduate student at King’s College London. “Additionally, growers typically will have several varieties in production to meet consumer demands in relatively small batch sizes, and [they] will have different requirements for packaging and presentation for each consumer -- a challenge which can come up even in high-volume production -adding further challenges to automation.”

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The Robot Report

Cobot arms, compliant grippers, and mobile platforms equipped with collision-avoidance capabilities take robots from inside fenced work cells to production lines and other venues.

Sena said that Rethink’s software developer’s kit and foundation in the Robot Operating System made Sawyer a good fit for the project. Although Rethink Robotics shut down last year, HAHN Group is continuing support.

Do you want fries with that?

As robots move from food processing into restaurants, the equipment must still be clean, consistent, and efficient.

Miso Robotics’ Flippy uses a cobot arm with a variety of end-of-arm tools, its Miso AI platform, and cloud-based monitoring functions.

The burger-flipping robot can work on a grill or fryer, comply with OSHA and food-safety standards, and run for up to 100,000 hours of continuous uptime, according to the company.

“While our systems are designed to work with just about any six-axis robotic arm, we are currently using Fanuc North America arms,” said David Zito, co-founder and CEO of Miso Robotics. “They are made collaborative via our unique design made specifically for commercial kitchen work.”

Unlike most robots in factories, Flippy can work alongside people in any commercial kitchen. It was tested last year at CaliBurger, Walmart headquarters, and Dodger Stadium.

The major fast-food and fastcasual chains are evaluating such

robots to stay competitive, especially as they face escalating worker shortages.

Getting served by cobot bartenders, baristas Aside from demonstrations of industrial robots at trade shows such as CES, cobot arms mixing drinks was a novelty. Not anymore.

Royal Caribbean Cruises has robot bartenders on several ships, and several hotels and pubs worldwide have used robot bartenders. (To be fair, some are caged off to avoid broken glass or drunk patrons.)

Makr Shakr, which provides the robot bartenders to Royal Caribbean, uses KUKA arms.

Even if you’re not nursing a hangover, at a Café X kiosk in Hong Kong or San Francisco, you can get a coffee made by a Mitsubishi cobot. This is arguably the most public-facing collaborative robot application, but at $25,000, the arm won’t be on household kitchen counters anytime soon.

Surgery with cobots Robots for assisting or conducting surgical procedures are usually highly specialized. They also need to comply with strict regulations, meet high performance requirements, and often force surgeons to get more training.

Researchers at Nottingham Trent University (NTU) are developing

Flippy the burger-flipping robot was tested last year at CaliBurger, Walmart headquarters, and Dodger Stadium. | Miso Robotics

Café X has multiple robotic kiosks in San Francisco.

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| Café X

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ALIAS uses a UR3 cobot to help busy solo pilots.

“Scoliobot” for precise spinal surgery. They are currently working on 3D-printed models with two UR5 cobots from Universal Robots.

The team is also using augmented reality to provide surgeons with live feedback.

One robot arm, dubbed the Datum Robot, would follow a patient’s spine and collect data on how he or she moves. The other, called the Tooling Robot, would automatically adjust to drill holes itself for realignment rods to be placed in vertebrae.

“It is paramount that spinal procedures are carried out with total accuracy in order to minimize what can be substantial risks to a patient,” said Prof. Bronek Boszczyk, head of spinal surgery at Benedictus Krankenhaus Tutzing and visiting professor at NTU. “This technology has the potential to minimize those risks by performing a key part of the operation with accuracy which cannot be achieved by a human hand.”

ALIAS is intended to help pilots perform at peak ability, so that they’re not bored or overtaxed. Another design goal is that training it to a new aircraft would take only a month.

Where will cobots go next?

The global market for collaborative markets will grow from $710 million in 2018 to $12.3 billion in 2025, predicts Markets and Markets.

While much of that growth will continue in automotive, electronics, and other manufacturing, the safety and flexibility of cobots will bring robotics to an ever-wider range of users.

Just as machine shops today have lathes, drill presses, and grinders, it’s not too great a stretch to imagine them having cobot arms.

Kitchens, coffeeshops, and cockpits are only the beginning. RR

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WHAT DO YOU THINK?

Connect and discuss this and

other engineering design issues with thousands of professionals online

Prof. Philip Breedon with Scoliobot, a robot being developed at Nottingham Trent University that uses two UR5 cobots for precise spinal surgery. | NTU

Cobot co-pilot Aurora Flight Sciences, a Boeing company, uses a UR3 arm as part of its Robotic Copilot concept. The goal is “to create a portable and extensible hardware and software toolbox introducing of new levels of automation across a wide variety of military and civilian aircraft that ultimately reduce crew requirements.”

The Aircrew Labor In-Cockpit Automation System, or ALIAS, would combine sensors, a tablet interface for pilots, and flight-control and mission software. The cobot enables it to interact with cockpit controls designed for human pilots.

| Aurora Flight Sciences

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February 2019

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CGI Inc. Advanced Products for Robotics and Automation At CGI we serve a wide array of industries including medical, robotics, aerospace, defense, semiconductor, industrial automation, motion control, and many others. Our core business is manufacturing precision motion control solutions.

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FESTO Corporation Ready-to-Install 2D and 3D Cartesian Handling Systems Festo offers a range of Cartesian handling systems to fit the needs of your assembly and material handling operations. An economic and energyefficient alternative to conventional 4- to 6-axis industrial robots, you can easily adapt Festo’s 2D and 3D systems to linear and rotational applications. Each system is ready to install and comes with a matching motor and controller.

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Harmonic Drive Customizable supermini actuator Small enough to fit inside the finger of a robotic hand, these ultra-compact servo actuators utilize zero backlash Harmonic Drive® precision gears, a brushless servo motor and an incremental encoder. RSF supermini actuators are available in 2 sizes with ratios 30:1, 50:1 and 100:1. Peak torque .13~1.4 Nm, max speed 100~333 rpm. RSF Supermini actuators are remarkably reliable.

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Kollmorgen Kollmorgen TBM™ Offers Optimal Performance in a Compact Package As new applications for robots continue to emerge the market for high power density motors also grows.

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Want to learn more about Kollmorgen TBM frameless motors?

Contact info: Gene Matthews Kollmorgen 203A West Rock Road Radford, VA 24141 Phone: 1.540.633.3545 Email: www.kollmorgen.com

LAPP LAPP SKINTOP® Cable Glands Enable Safest Cleaning Possible Ideal for applications that require hygienic operation, LAPP’s SKINTOP® HYGIENIC and INOX series of edge-free, stainless steel cable glands include special design features that prevent microorganisms and bacteria from sticking to the surface— preventing contamination and enabling safe, easy cleaning.

SKINTOP® HYGIENIC • Certified to NSF/ANSI 169 • Stainless steel with FDA-approved sealing material • Protection ratings: IP68 & IP69 • Temperature range: -20 to +100°C • Wide clamping range • Available with NPT and metric threads SKINTOP® INOX • Certified to NSF/ANSI 169 • Highly corrosion-resistant stainless steel • Protection ratings: IP68 & IP69 • Temperature range: -40 to +100°C • Wide clamping range • Available with NPT and metric threads

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Corporate Headquarters 29 Hanover Road Florham Park, NJ 07932 Toll Free: 1-800-774-3539 Tel: 973-660-9700 Fax: 973-660-9330 www.lappusa.com Email: sales(at)lappusa.com

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maxon precision motors Drive Systems for Robotics Reliable, Powerful, Efficient A complete joint actuation unit. Includes a brushless DC motor, an internal high resolution encoder, planetary gearhead with absolute encoder and position controller with CAN and RS232 interface.

Exoskeleton Joint Actuator • Compact Housing • Integrated Controller • Reduced Weight and Cost • For Use in Hip and Knee Exoskeletons maxon is your single source for motion solutions. When you choose maxon, you can expect outstanding service, creative options and quality without question. Want to get your ideas moving? Contact maxon today.

Learn more about the maxon solutions and visit www.maxonmotorusa.com

101 Waldron Road Fall River, MA 02720 Phone: 508.677.0520 www.maxonmotorusa.com info@maxonmotorusa.com

Mitsubishi Electric FR Series Next Generation, High Performance Industrial Robots Integrate Seamlessly with iQ Platform Controllers for Advanced Cooperative Functions “FA-IT Integration Functions” with the full line of Mitsubishi Electric FA products such as PLCs, motion controllers, VFDs, HMIs, and CNC, as well as MES/SCADA packages provide a level of performance, functionality and ease of use unparalleled in the industry. In addition, the open platform architecture OS enables integration with 3rd party devices normally difficult or impossible to use on closed platforms. With such flexibility, capability and performance, increases to both productivity and maintainability can be achieved, resulting in a lower TCO (Total Cost of Ownership).

• With an extensive selection of arm sizes, configurations, protection ratings,

Website: us.MitsubishiElectric.com/fa/en Address: 500 Corporate Woods Pkwy Vernon Hills, IL 60061 Phone: 847.478.2100

backed with “Next-Generation” features, and options, the MELFA FR-Series line of robots are ready to handle all of your automation needs. Vertically articulated, horizontally articulated SCARA, ceiling mounted horizontal type, and dual arm high precision for micro-assembly • Industry’s best initial warranty - 3 year on-site for most models

February 2019 www.designworldonline.com

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maxon precision motors, inc.

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mk North America Enhance Your Automation with Pallet-Handling Conveyors Pallet-handling conveyors utilize pallets to transport workpieces asynchronously between stations. Pallets range in size from 10x10 inches to 48x48 inches, and are capable of carrying loads of nearly 1,000 lbs.

Pallet-handling Conveyor Benefits Are Delivered By: • Continuously moving materials from point to point • Handling every item in the same fashion with no variance • Removing production inefficiencies and wasteful delay by streamlining and standardizing the movement of material • Consistent material handling routines, ultimately reduces work in process and allows greater throughput • Eliminating bottlenecks and operations that require labor but, do not add value

Contact info: mk North America, Inc.

• Easily adapt to changing demands and production requirements

Website: www.mknorthamerica.com

• Efficiently handle changes to work flow by adding in new stops, locates or transfer components

Phone: 860.769.5500 Email: info@mknorthamerica.com

New England Wire Technologies Advancing innovation for over 100 years Why accept a standard product for your custom application?

NEWT is committed to being the premier manufacturer of choice for customers requiring specialty wire, cable and extruded tubing to meet existing and emerging worldwide markets. Our custom products and solutions are not only engineered to the exacting specifications of our customers, but designed to perform under the harsh conditions of today’s advanced manufacturing processes. Cables we specialize in are LITZ, multi-conductor cables, hybrid configurations, coaxial, twin axial, miniature and micro-miniature coaxial cables, ultra flexible, high flex life, low/high temperature cables, braids, and a variety of proprietary cable designs. Contact us today and let us help you dream beyond today’s technology and achieve the impossible.

NEW ENGLAND WIRE T E C H N O LO G I E S

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Contact info: New England Wire Technologies www.newenglandwire.com 603.838.6624

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NSK Precision For Maintenance Free Operation Choose NSK K1TM Lubrication Unit NSK’s K1TM is a uniquely designed system that not only lubricates but also helps minimize contamination. K1 material composition consists of 70% mineral

oil

and 30% polyolefin resin which ensures long-term, maintenancefree operation even under tough lubrication environments. K1TM offers no maintenance for up to 5 years or 10,000 km operational distance. K1 is ideal for environments where the lubricant is hard to replace or is easily washed away. Available in ball screws, linear guides, monocarriers and tough carriers.

NSK Americas www.nskamericas.com

Renishaw Inc. QUANTiC™ Encoder series Our QUANTiC encoder series features exceptionally wide installation and operating tolerance, in addition to enhanced diagnostics compatibility. QUANTiC read heads incorporate our wellestablished filtering optics that averages the contributions from many scale periods and effectively filter out on-periodic features such a dirt. Also, featuring a third layer of signal filtering from a new detector design, which helps to eliminate non-harmonic signal frequencies that can occur due to contamination on the scale. These signal conditioning features combine to ensure low Sub-Divisional Error (SDE) and minimal signal variation over contamination. QUANTiC encoders will help to increase the throughput of high-volume production lines and offer potentially significant time and cost savings for OEM customers.

Contact Info: 1001 Wesemann Drive West Dundee IL 60118 Website: www.renishaw.com Phone: 847.286.9953

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Email: usa@renishaw.com

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ROLLON Extend Your Robot With A Seventh Axis Adding a seventh linear axis to a robotic system provides all the benefits of six-axes plus another degree of freedom for maneuverability during complex tasks. This flexibility lets designers of a pick-and-place system, for instance, “shuttle” a robot — or multiple robots — and parts back and forth along a rail. A seventh axis provides a host of advantages, including: • Extended operating area. Multiple robots or heavy loads can travel over long distances and to several workstations.

• Greater robotic output. An additional linear axis expands the work envelope to the point that companies can use smaller, less- expensive robots to perform the same amount of work as larger robots.

• Flexibility. Seventh-axis robot systems can accommodate lightweight or heavy loads, even at high speeds.

• Efficiency gains. More machines and processes can be serviced with one robot.

• Reduced capital costs. The combination of increased range of motion, greater output from smaller, more economical robots and the ability to service more workstations means greater cost savings for automated operations.

Contact info: Rollon Corporation 101 Bilby Rd. Hackettstown, NJ 07840 www.rollon.com Phone: 973.300.5492

SICK, Inc. SICK Multi-Layer LiDAR for 3D Dimensioning Indoors & Outdoors The MRS1000 LiDAR from SICK takes standard planar scanning to a different level by offering: • 4 spread scanning layers at a 7.5° vertical aperture angle along with a 275° horizontal aperture angle • 0.2M - 64M range • IP67 housing with industrial grade M12 connectors • High definition distance measurement (HDDM+) with multi-echo capability provides consistent functionality in rain, snow, fog, dust or ambient light • Ethernet communication SICK offers first class industrial grade technology that’s been proven in thousands of applications worldwide in some of the toughest conditions imaginable. Contact us today to discover how to put SICK technology to work for you, providing the solutions you need while improving your overall

SICK, Inc. 6900 West 110th St. Minneapolis, MN 55438 USA www.sick.com info@sick.com

processes. We think that’s intelligent.

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