The developments that took industrial robotics from automation to collaboration.

Industry 4.0 Deep Dive. Part 4: Evolving Industrial Robots

Automated systems governing smart manufacturing, and enabled by IoT devices, give us the IIoT (Industrial Internet of Things), which allows for the expansion and streamlining of all aspects of business. In addition, the deployment of AI and robots is particularly useful in industry as they increase product quality, production efficiency and overall speed.The introduction of collaborative robots (or cobots) is becoming increasingly common, in industry as well as in laboratory work and commerce. Cobots can take instructions from humans in order to work more productively with them, including instructions that were not originally anticipated in the initial programming. Robots don&#39;t get bored, hungry or tired. The <a href="https://www.rowse.co.uk/blog/post/the-rise-of-artificial-intelligence" target="_blank" title="The Rise of Artificial Intelligence">capabilities of Artificial Intelligence</a> are far beyond human capacity when it comes to such things as miniaturisation and precision measurements, and they deliver vastly superior quality assurance.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1584368176061-1584368176061.png" style="width: 1080px;" class="fr-fic fr-dib">So what are the benefits we can expect to see as industry moves forward into the 21st century world of cobots and AI?<h2>1. Direct Automation</h2>IIoT connects all IoT enabled devices to the factory floor, integrating manufacturing processes with big data and making them programmable via a logic controller. Increased use of precision sensory equipment means that information can be generated, recorded and analysed for all aspects of the production process, covering anything from temperature to item picking and packaging. Programmable logic controllers with AI capacity for deep learning can then respond automatically to the seamlessly generated information, and make alterations to the minutest function without recourse to human intervention. The big data analytics processed by AI can substantially improve performance across the entire production process, and can be operated remotely.<h2>2. 24/7 Production</h2>Human beings are biological organisms and need regular maintenance, e.g. food and sleep. For any production facility to continue working round the clock it is necessary to introduce shifts, using three human workers for every 24 hour period. Robots don&#39;t get tired or hungry, and are capable of working on the production line 24/7. This allows the expansion of production capabilities, which is increasingly necessary to meet the demands of worldwide customers. Furthermore, robots are more efficient in many areas such as the assembly line, and picking and packing departments. They can greatly reduce turn-round times in many areas of the business operation.<h2>3. Safety</h2>Human beings are fallible and prone to making mistakes, especially if they are tired or distracted. Errors and accidents do occur on the factory floor and in any construction or processing environment; a tendency which can be all but eradicated by AI and robotic assistance. Remote access control means a reduction in human resources, especially when the work is dangerous or requires superhuman effort. Even regular working environments will cut down on the incidence of industrial accidents and lead to an overall improvement in safety. In addition, more advanced sensory equipment integrated with IIoT devices make the installation of safety guards and barriers a simpler and more effective measure to protect human lives.<h2>4. Lower Operational Costs</h2>Many companies are viewing the introduction of AI into the manufacturing industry with trepidation, as it requires a huge capital investment. On the other hand, the ROI is significant and increases as time goes on. Once intelligent machines begin to take over the daily activities of a factory floor, businesses will benefit through considerably reduced operating costs, with predictive maintenance helping additionally to reduce machine downtime.These days, consumers are increasing their demand for unique, personalised or customised products, while continuing to expect the best value. With such advances as 3D printing and IIoT connected devices, it is becoming simpler and cheaper to meet these needs, and using virtual or augmented reality design techniques means that the whole production process will be more cost-effective. Integrating machine learning and CAD means that systems can be designed and tested in a virtual model before they are put into production, thus reducing the cost of trial-and-error machine testing.<h2>5. Greater Efficiency</h2>IIoT enables the collection of vast amounts of data and advanced analytics which can be used to gain insights into consumer behaviour. Trends can be predicted, patterns recognised and market developments forecast across time, socioeconomic sectors and geographic markets, as well as taking into account political developments, macroeconomic cycles and even weather patterns. AI has the capacity with machine learning to anticipate information, to refine processes, and to track incongruities, all the way down the supply chain from source to finished product. This is particularly aided by such technologies as&nbsp;<a href="https://www.rowse.co.uk/blog/post/rfid-in-manufacturing" target="_blank" title="RFID In Manufacturing">RFID tracking</a>, which enable materials to be tracked without having to go through a physical process such as a bar code reader.<h2>6. Quality Control</h2>AI is also extremely useful for carrying out predictive maintenance on machinery and equipment. Using sensors to track performance and operating conditions, machines can learn to predict malfunctions and failures, and take action to remedy them before they occur. This can result in faster feedback, helping companies to eradicate unplanned downtimes.Sensors can also detect the mot microscopic defects, scanning them at resolutions far beyond the capacity of human vision, thus improving productivity and increasing the percentage of items that will pass quality control. AI helps to speed up many routine processes and improves accuracy to an enormous extent. This obviates the requirement for quality control and in-process inspection by human beings, which is time-consuming and often fallible.<h2>7. Quick Decision Making</h2>When IIoT is coupled with cloud computing and virtual or augmented reality, companies can share simulations, confer on production activities and exchange critical or important information in real-time, irrespective of geographical location. The data gathered from sensors and beacons helps to determine consumer activity, allowing companies to anticipate future needs and make rapid decisions on production, as well as speeding up the exchange between manufacturers and suppliers.<hr>This post was originally posted on <a data-fr-linked="true" href="https://www.rowse.co.uk/blog/post/7-manufacturing-ai-benefits" rel="noopener noreferrer" target="_blank">www.rowse.co.uk</a>.

As the world becomes increasingly automated, the IoT (Internet of Things) is already transforming our domestic and business lives. Nowhere is this more apparent than in the use of AI and robotics in the manufacturing industry, with all the benefits offered by Industry 4.0.

7 Benefits of AI In Manufacturing

One key element of the future production landscape is highly flexible Human-Robot Collaboration (HRC). This article describes 3 case studies which illustrate how collaborative robots change the way people will manufacture. KUKA, one of the world&#39;s largest suppliers of industrial robots, has written a free, downloadable white paper called &quot;<a href="http://bit.ly/kuka-whitepaper-US" rel="noopener noreferrer" target="_blank">Shaping future production landscapes</a>.&quot; The white paper describes &#39;megatrends&#39; that impact manufacturing and 3 key factors to designing production infrastructures that can meet the requirements of the future.&nbsp;<hr><h1>Intro</h1><h2>Human-robot collaboration&nbsp;</h2>In human-robot collaboration, the robot assists the human operator. This means: The machine does not replace the human, but complements his capabilities and relieves him of arduous tasks. These can include overhead work, for example, or the lifting of heavy loads. Autonomous, collaborative robots are also used to supply production workstations.In the ideal &#39;factory of the future&#39; there is no separation between automated and manual workstations. Humans and robots collaborate optimally &ndash; without separation and without safety fencing.<h2>Human-robot collaboration: the advantages</h2>HRC-capable robots (often labeled &#39;Collaborative Robots&#39; or &#39;Cobots&#39;) are rendered mobile and capable of performing different tasks flexibly using mobile platforms. They can thus be individually deployed at whatever location and for whatever purpose corresponds to production requirements &ndash; dependent on the required batch size, for example. Furthermore, these types of robots offer maximum flexibility by means of so-called spontaneous automation: They support a user as an assistant in the case of workload peaks and resource bottlenecks in production operations.Human-robot collaboration changes industrial production and manufacturing and can bring a number of advantages over traditional automation or manual labor:<ul><li>Increased flexibility in production</li><li>Relief of employees by performing ergonomically unfavorable work steps that could not previously be automated</li><li>Reduced risk of injuries and infections</li><li>High-quality performance of reproducible processes &ndash; without requiring type-specific or component-relevant investment.</li><li>Increased productivity and improved system complexity thanks to integrated sensors</li></ul> <h1>HRC Case Studies</h1>To give you an idea of the impact of collaborative robots in the workspace we&#39;ve gathered 3 case studies to show you the impact of these robots and the challenges they overcome. <h2>Case Study 1: Lightweight robots supporting operators in adhesive bonding processes.</h2><h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1566292124494-KUKA+LBR+iiwa+Duerr.jpg" class="fr-fic fr-dib" style="width: 766px;"></h2>All images courtesy KUKASystem and machine builder <a href="https://www.durr.com/en/" rel="noopener noreferrer" target="_blank">D&uuml;rr</a> uses two sensitive LBR iiwa robots in final assembly. Without a safety fence, they perform key tasks in the application of adhesive beads. This improves quality, saves time and lowers unit costs.<div data-ignore="true">At the company, humans and robots work together in final assembly without the need for physical safeguards. Sensitive lightweight robots are used for the adhesive bonding of GPS antenna covers and tanks in the vehicle body. The solutions improve the quality of the adhesive bonding result, save time and lower unit costs.&nbsp;</div>The LBR iiwa detects any fault that arises when the GPS antenna cover is loadedFor application of adhesive to the GPS antenna cover, the assembly worker places the workpiece into the robot gripper manually. The gripper sucks onto it and moves it to the adhesive nozzle on the application tower. The robot slowly moves the GPS antenna cover upwards to the application nozzle. If it encounters an obstacle, it moves back a little thanks to its collision detection ability and starts the motion over from the beginning. Only after three attempts does it move back into the starting position. Otherwise, the adhesive application process starts and the adhesive bead is applied carefully while the robot executes the path. Finally, back at the starting point, the assembly worker removes the GPS antenna cover and mounts it on the vehicle. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<iframe src="https://www.youtube.com/embed/PdAXCNtXabc?&wmode=opaque" allowfullscreen="" class="fr-draggable" style="width: 767px; height: 429px;" frameborder="0"></iframe>Precise adhesive beads thanks to the seventh axis of the LBR iiwaFor the automated process of gluing the tank into place in final assembly, the skilled worker guides the tank to a turntable with the aid of a manipulator. There he cleans it, moves it into the correct position and transfers it directly to the robot for further processing. When the starting point is reached on the stationary tank, the nozzle opens under pressure so that no air bubbles are created. The robot then applies the adhesive bead onto the tank very evenly. Sensors on the application head monitor the accurate height of the bead. Thanks to its seventh rotating axis, the robot glues in a radius of 360 degrees &ndash; without re-orienting or having to interrupt the bead. As soon as the robot has completed its task, the skilled worker fits the tank into the vehicle body at the specified position.<a href="http://www.durr.com/" target="_blank">D&uuml;rr</a> has already sold ten systems in the field of adhesive bonding, and is working on HRC solutions for the robot-based adhesive bonding of small washers and other components. <h2>Case Study 2: Human-robot collaboration during the adjustment of headlights.</h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1566292345359-Fahrassistenzsystemprfstnden_Drr.jpg" style="width: 766px;" class="fr-fic fr-dib">To reduce the strain on the workers, two KUKA LBR iiwa cobots perform the unergonomic task of adjusting fog lights at Ford. Parallel to this, human colleagues can adjust the main headlights.<article data-ignore="true">The initial situationIn the automotive industry, vehicle fog lights have previously been adjusted manually by human operators. While stooped over, the human operator searches for the hard-to-reach opening for the adjusting screws in the bumper area. Once he has found the adjusting screw, he positions the tool on it extremely carefully without damaging the adjustment opening on the bumper.</article><article data-ignore="true">The solutionTo provide relief for the human worker during this ergonomically unfavorable work and simultaneously achieve improved of the adjustment, Ford VOME (Vehicle Operation Manufacturing Engineering), D&uuml;rr Assembly Products and KUKA have jointly developed an innovative alternative concept for the conventional process as part of a feasibility study. The solution called for the use of the KUKA LBR iiwa cobot. The sensitive robot adjusts the fog lights fully automatically. In doing so, humans and robots work on the same vehicle without additional safety equipment.</article><iframe src="https://www.youtube.com/embed/9HWY-CLR3Co?&wmode=opaque" allowfullscreen="" class="fr-draggable" style="width: 766px; height: 419px;" frameborder="0"></iframe>TeamworkWhile the human operator adjusts the conventional headlights, the cobot adjusts the hard-to-reach fog lights. Two lightweight robots are already in use in four headlight/driver assistance system test stands for the new Ford Focus C519 at the Ford plant in Saarlouis. &nbsp; &nbsp; &nbsp; &nbsp;The secret of the HRC conceptWith its size and joint torque sensors, the robot detects contact immediately and reduces its level of force and speed instantly. These characteristics make it possible to work with human operators in confined spaces. Furthermore, with its high-performance servo control, the cobot detects contours quickly under force control. In combination with an end effector specially developed by D&uuml;rr (consisting of a camera system and an adjustment screwdriver), the robot positions the adjustment screwdriver according to the hole coordinates acquired by the camera system. The adjusting screw itself is located approximately 80 mm behind the hole in the bumper. Thanks to sensitive movements, the cobot optimally positions the adjustment tool into the screw head.Redundant safety is provided by the HRC-capable, heavy-duty aluminum gantry from D&uuml;rr Assembly Products &ndash; on which the KUKA LBR iiwa is positioned in a ride-on installation above the light collecting box directly in front of the headlight to be adjusted. The advantage here: additional robot positioning can be dispensed with. <h2>Case Study 3: Using a cobot to install pump wells. </h2><h2><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1566292578317-KUKAflexFellow_Platzierung_im_Arbeitsbereich_IMG_3628.jpg" style="width: 766px;" class="fr-fic fr-dib"></h2>In the future, BSH Hausger&auml;te GmbH will be using a <a href="https://www.kuka.com/en-us/products/mobility/mobile-robot-systems/kuka-flexfellow" rel="noopener noreferrer" target="_blank">KUKA flexFELLOW</a> to carry out the installation of pump wells on the dishwasher production line.&nbsp;<article data-ignore="true">The initial situationThe <a href="https://www.bsh-group.com/us/" rel="noopener noreferrer" target="_blank">BSH Hausger&auml;te</a> GmbH factory in Dillingen, Germany is Europe&rsquo;s largest and most modern factory for dishwashers. Around 10,000 machines leave the production line every day in two-shift operation. There are a total of seven production lines for manufacturing dishwashers.Screw fastening of the pump covers in the dishwasher is an ergonomically inconvenient task as the employee has to lean into the dishwasher housing to perform the task. The mechanical assistant now takes on this monotonous step in the process.&nbsp;The task &nbsp; &nbsp;&nbsp;&nbsp; &nbsp;Its contribution has already been tested successfully over several months as part of a pilot system. In addition to relieving the human operator of a cumbersome task, the robot also contributes by documenting its work and indicating whether the screws have been tightened correctly or not.The solution &nbsp; &nbsp; &nbsp; &nbsp;The key challenge when designing the system was to ensure that the manual workstation remained unchanged and that the automation solution could be integrated into the existing assembly line. Its sensitive and responsive capabilities enable it to recognize if, for example, a pump well component is not positioned correctly and then presses it into place.&nbsp;<iframe src="https://www.youtube.com/embed/jtkJQJXO7AQ?&wmode=opaque" allowfullscreen="" class="fr-draggable" style="width: 767px; height: 413px;" frameborder="0"></iframe>The advantages of using a cobot:<ul><li>Reduction of the manual workload in an ergonomically inconvenient task</li><li>Automated documentation of work steps</li><li>Indication of correct screw fastening operation</li></ul>Using its responsive capabilities, the cobot can then calibrate itself independently at its workstation, use a search run mode to find the screw positions, re-apply pressure to the component if it is not correctly positioned and finally tighten the four screws. <hr><h1> </h1><h1>Further Reading</h1><ul><li>Specifications of the <a href="https://www.wevolver.com/wevolver.staff/kuka.lbr.iiwa/" rel="noopener noreferrer" target="_blank">KUKA LBR iiwa</a></li><li>&quot;<a href="http://bit.ly/kuka-future" rel="noopener noreferrer" target="_blank"></a><a href="http://bit.ly/kuka-whitepaper-US" rel="noopener noreferrer" target="_blank">Shaping future production landscapes,</a>&quot; KUKA&#39;s white paper describing the &#39;megatrends&#39; that impact manufacturing and 3 key factors (people, technology and adaptation) to designing production infrastructures that can meet the requirements of the future.</li></ul> </article>

In human-robot collaboration, human and machine work hand in hand. The human operator controls and monitors production, the robots perform the physically strenuous work. Both contribute their specific capabilities.

Human-robot collaboration: 3 Case Studies

There is a growing trend of using robots to do the tasks that could harm humans. Here are six fascinating examples.<h2>1. Firefighters Depend on Robots to Assess Burning Buildings</h2>Smoke and flames can make it nearly impossible for even the most experienced firefighters to navigate once they&rsquo;re inside a building that&rsquo;s on fire. However, there&rsquo;s a recent development called the SmokeBot that crews can <a href="https://www.maketecheasier.com/smokebot-robot-fight-fires-firefighters/" rel="noopener" target="_blank">send into flaming structures</a> to monitor the progression of fires and the damage caused.Something that sets the SmokeBot apart from other fire-ready robots is that it creates interior maps firefighters can use later to find their way around the building. It accomplishes this map-making feat with a combination of attachments including a thermal camera and gas sensors.The U.S. Navy also has a firefighting robot that extinguishes flames on ships. That piece of equipment has arms that can open doors or use hoses to help fight and control the fire.Robots don&rsquo;t necessarily remove humans from the equation, but they can let firefighters know in greater detail what to expect. Then, it&rsquo;s easier for those professionals to prepare or call for more assistance when needed.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1574191863839-5b28e74785ba9.jpg">SmokeBot &ndash; a robot serving rescue units<h2>2. A Cow-Herding Robot Keeps Farm Workers Safer</h2>Cows are generally docile, but when farmers deal with thousands of them at once while herding them to various areas of a farm, it&rsquo;s not hard to imagine how things could go wrong.<a href="https://www.cargill.com/story/meet-the-cowboy-robot-thats-making-cattle-herding-safer" rel="noopener" target="_blank">Cargill recently unveiled</a> a &ldquo;robotic cowboy&rdquo; on wheels that waves plastic bags on its limbs to compel the cows to move. Humans control the robot with remotes as they stand on catwalks, but they can do so at a distance that keeps them more protected if issues arise.Cargill associates asserted that it&rsquo;s highly preferable to replace a piece of equipment if accidents happen instead of putting people in harm&rsquo;s way. The use of a robot to work with cattle makes a dangerous but necessary job safer.<iframe src="https://www.youtube.com/embed/Aehth3w9h04?&feature=emb_title&wmode=opaque" frameborder="0" allowfullscreen="" class="fr-draggable" style="width: 1114px; height: 615px;"></iframe> 3. Robots Clean Up Nuclear SitesThe nuclear energy industry is full of dangers for humans, but it&rsquo;s frequently looking for ways to expose robots to the potential harm instead.Sellafield is a nuclear power plant in England that handles nuclear fuel reprocessing and nuclear decommissioning. There&rsquo;s one hot-spot known locally as &ldquo;D-Bay&rdquo; that started acting as a radioactive sludge depository in the 1970s. The sludge is a byproduct of decaying nuclear fuel and other debris &mdash; and Sellafield has the equivalent of <a href="https://www.gov.uk/government/news/robots-tackle-sellafields-notorious-radioactive-hot-spot" rel="noopener" target="_blank">35 concrete mixer trucks&rsquo;</a> worth of it.After a decade of planning, officials at Sellafield and their partners were ready to start using robots that sucked up the dangerous material with arms mounted on overhead cranes. Another tool can attach to a crane and chop large pieces of debris into smaller chunks. Operators stay shielded behind a wall while manoeuvring the robots.The Shadow Robot Company also have a <a href="https://www.shadowrobot.com/wp-content/uploads/Introducing-the-Shadow-Telehand.pdf" rel="noopener" target="_blank">custom-made teleoperation product</a> which uses innovative technology to help transform work within radioactive environments via remote handling in alpha glove boxes. Their robot hand means operators can remotely handle materials in a glove box from afar at no risk to their well-being. They can even operate the Hand in their shirt sleeves at an office desk with full control over the robot&rsquo;s movements back at the cell.Robots are also <a href="https://www.wired.com/story/fukushima-robot-cleanup/" rel="noopener" target="_blank">helping with the cleanup process</a> at Fukushima, a nuclear plant in Japan that got devastated by an earthquake and tsunamis in 2011. One robot called the Sunfish helped find missing fuel inside a radioactive containment vessel. Several other robots couldn&rsquo;t handle the radiation, but the Sunfish is helping engineers learn how to build other robots to assist in dealing with the aftermath.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1574191702468-ezgif-6-e37770a42569.gif">Credit: Shadow Robot Company, internal demo of our teleoperation product <h2>4. Decreasing Welding Dangers With Specialized Robots</h2>Welding requires professionals to work with extremely hot temperatures and carry out precise tasks during their shifts. But statistics indicate that robotic welding makes up <a href="https://www.fairlawntool.com/blog/complete-guide-robotic-welding/" rel="noopener" target="_blank">29 percent of welding applications</a>. Spot welding is currently the most common use, but arc welding is catching up in popularity.Arc welding is a particularly good fit for robotics when it comes to safety. That&rsquo;s because it involves both chemical reactions and hot temperatures, both of which could be dangerous to humans.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1574191978370-wel.gif" style="width: 1042px;" class="fr-fic fr-dib">Besides, welding exposes humans to hazardous fumes. It can also cause a condition from ultraviolet light called ultraviolet keratitis. When operating welding robots, humans stay behind safety fences or interlocking doors. However, they&rsquo;re still needed during the process to inspect the robot&rsquo;s work and ensure the equipment works as expected.Many people nervously anticipate robots taking over their jobs. But this is an example of how robots could supplement our roles by making them less dangerous than before.<h2>5. Rescuing Victims of Snow-Related Disasters With Robots</h2>Snowfall is a common occurrence that can turn deadly &mdash; especially when avalanches happen. Researchers have spent years investigating ways to use robots for snow rescues, and they&rsquo;re coming up with continually improved methods.At the University of Bologna, <a href="https://www.unibo.it/en/notice-board/from-sherpa-to-airborne-the-new-generation-of-mountain-rescue-robot-drones" rel="noopener" target="_blank">a project is underway</a> to enhance robot-facilitated emergency assistance for victims trapped by snow. One method uses drones and radar to find people in distress. The team working on this initiative believes the results will save more lives and better equip rescuers to find people faster than they otherwise might.<h2>6. Relying on Robots for Pipeline and Fuel Tank Inspections</h2>Pipeline inspectors are instrumental in preventing oil spills, and they cope with claustrophobic environments and other job-related risks. But research indicates that even when pipeline checks happen, those assessments often fail to detect issues. In other cases, <a href="https://www.climatechangenews.com/2018/08/07/oil-pipeline-inspection-industry-going-wrong-surveys-fail-prevent-spills/" rel="noopener" target="_blank">problems do not get accurately reported</a>. These shortcomings have given rise to calls for improvements in pipeline inspection methodology and technology.Previous methods to inspect pipelines included sending pressurised water inside to test their strength or digging into the ground to carry out spot tests. But beginning a few years ago, a company called Diakont emerged as one of the first to <a href="https://phys.org/news/2017-06-robots-oil.html" rel="noopener" target="_blank">send robots inside pipelines</a> to have a look.The two arms on the robots have ultrasonic sensors that measure the circumference of a pipeline and assess structural integrity. Diakont also developed a robot for fuel storage tanks that keeps operators away from the volatile environment caused by the presence of flammable material. This robot doesn&rsquo;t require draining the containers first, and that advantage prevents up to two months of downtime where the tank remains out of operation while removing the fuel.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1574191609887-Diakont-Robot.jpg">Credit: CNBC.com, Diakont RobotIf you have questions, or feedback, feel free to leave them below in the comments.<hr><h2>Sponsored content</h2>This article was created in partnership with the Shadow Robot Company. If you are interested in sharing sponsored content on the Wevolver platform email us at richard [at] wevolver.com. Wevolver reaches more than 6 million young, professional engineers and engineering students every month. To learn more visit our &quot;<a href="https://www.wevolver.com/marketing-solutions" rel="noopener noreferrer" target="_blank">Marketing Solutions</a>&quot; page.

The world is full of life-threatening jobs. There was a time when humans merely gritted their teeth, accepted the risk and trusted that the training they received would protect them from harm. However, there is a growing trend of using robots to do the tasks that could harm humans.

How robots are saving humans from doing dangerous jobs

Henrik Christensen shares his views on the impact of the robot revolution and stories from his life in European and US robotics research.

The impact of the robot revolution with roboticist Henrik Christensen

Most robots used in factories today are powerful but blind. They are programmed to do specific tasks repeatedly with high precision and speed, but until now have not been able to adapt to changes in the environment or handling variables. Robot arms often lack the ability to grasp an object if it is moved a few inches, not to mention picking or sorting millions of different products in a warehouse.As a result system integrators design shaker tables, feeders, conveyor belts, etc., so robots can be offered the same parts in the right orientation. Larger industrial robots need cages so they don’t accidentally hurt nearby people. Unlike human hands, which can perform tens of thousands of different tasks, robot arms can only be programmed for single tasks, and they need the proper end effectors.Additionally, most robots cannot sense, unless you equip them with force or vision sensors. They don’t know if tasks are performed properly, because they cannot see or feel.Traditional computer vision requires pre-registration of all objects, i.e. scanning and building computer-aided design (CAD) models for all items beforehand with expensive 3D cameras. The process is time-consuming and inflexible. Even if there was a box with exactly the same shape, but a different size, a robot couldn’t automatically recognize the differences and figure out how to grasp the new item.In addition, translucent packaging, reflective surfaces, and deformable objects (such as food and clothing) make robotic vision an even more difficult problem to solve. The additional non-robot hardware and integration end up costing four times more than a robot arm, which costs anywhere between $10,000 to $100,000.Adding intelligence to robotsTo solve these issues, deep reinforcement learning (DRL) is unleashing the full potential of robotics, giving them the ability to recognize and react to their surroundings and handle product variations. Robots can now learn from experiences with off-the-shelf cameras and deep neural networks.With enough data and practice, robots can now teach themselves new abilities, as was seen with robots’ ability to identify images and win video games. With every grasp and trial, a robot gets smarter and more adept at mastering the task. Furthermore, cloud-connected robots allow learnings to be shared with each other. This is a tremendous shift, making robotics solutions more dexterous, flexible, and scalable.Deep Learning (DL) is primarily used for image classification. Deep neural networks learn representations, rather than task-specific algorithms.Reinforcement learning (RL) is inspired by behavioral psychology. It learns control policies by trial-and-error through reward or punishment.Deep Reinforcement Learning (DRL) combines these two approaches. It is currently suited for:· Simple or simulated tasks· Fault-tolerant tasks· Just enough variation to make explicit modeling difficult· Easy-to-define goals (reward functions)· Semi-structured environmentsWarehouse as a testing ground for AIAutomation in warehouses is widely considered the “low-hanging fruit” for these technologies because tasks tend to be similar across warehouses, and the environment is more structured. For example, order picking represents more than 40% of operational costs across most warehouses. While tasks are repetitive, they involve some variation with products and packaging design, making it impossible to hard-program robots in advance.Furthermore, labor costs account for up to 70% of a warehouse’s total budget. Pressured by e-commerce companies such as Amazon, retailers are looking to automation to address labor shortages and reduce costs. Customer demand, price competition, and fast delivery requirements are driving a strong business case for warehouses to be the first adopter of AI-enabled robots.Chart: Why do we see AI-enabled robots in the warehouse first?<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNTY4OTAzMzQ5MzI3LTFfTDhkY3A1c1lfcEU0MTdqdW9VaEMwZy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib"> Most tasks in a warehouse are fault tolerant, unlike tasks on a production floor or in the automotive industry. If a robot accidentally drops an object in a warehouse, it can try again as long as it achieves the required speed and success rates without damaging the goods.DRL is particularly suited for fault-tolerant simple tasks — actions a human can perform within a few seconds — with enough variation in semi-structured environments. As technology improves in environments like warehouses, it can then be applied in other industries, such as manufacturing, where tasks are more complicated and more uncertainties exist. Thus, an automated warehouse acts as an incubator, predicting a knowledge spill-over to many other market segments.Piece-picking is the holy grailWarehouse automation has been around for decades, in the form of automated storage, retrieval systems, and mobile robots like those from Kiva (now Amazon Robotics), that move items around.But most of the pick-and-pack operations within a warehouse are still performed by humans, with labor costs accounting for 50 to 70% of the overall warehouse budget. Worker shortages have driven up salaries by a rate of 6 to 8% every year, while robot costs have fallen considerably since the 1990s.Piece-picking has long been the holy grail of robotics. Year after year, incumbents such as Amazon and KUKA host robotics challenges for startups and academic teams to create machines that can identify, pick up and stow goods. The emergence of deep learning allows robots to recognize, pick, and place hundreds or up to thousands of items.But the technology is not yet perfect. It’s still challenging for a machine to recognize tens of millions of objects, and manipulate deformable objects or items with transparent packages. However, our interviews with Locus Robotics and Osaro indicate that many industry experts expect the technology will mature. In addition, we will have a robot vision camera that can recognize almost everything in a warehouse within five years. If that happens, it will affect not just warehouses, but retail, delivery, and many other applications that we haven’t yet imagined.Pushing towards standard modelsTechnology improvements in warehouse automation and beyond will also push the mature, but fragmented, robotics industry to become more standardized and modular. Given the nature of narrow AI, most Silicon Valley startups in the AI-enabled robotics industry choose to focus on specific vertical applications and collect proprietary data as a defensive advantage.Piece-picking by robots is the best example. As the technology improves, we will see warehouse piece-picking startups enter manufacturing, food, and agriculture markets. Moving away from manually programming a robot and having it learn and act autonomously on specific simple tasks — and sharing the learning — will become more important in the future.However, it’s not easy to find scalable and technically feasible business cases like piece picking that customers are willing to pay for. For example, assembly tasks vary from factory to factory, making it difficult to get sufficient training data or scale a solution across different customers.Given current technology constraints, there’s still a trade-off between precision and flexibility. Deep learning allows robots to handle variation, but these AI-enabled robots still cannot achieve the same precision and accuracy as traditional robots, at least not today.Some startups are providing modules or technology stacks for other players in the ecosystem to mitigate the complexity and scalability issues mentioned above. New areas where startups are likely to enter and disrupt include teleoperation modules, controllers that allow for safer human-robot collaboration, human-centered AI protocols, and sensors that can quantify human behavior.Eventually, we may see unified operating systems for AI-enabled robots similar to Windows or Android in the consumer electronics space, or standard user interfaces that will fundamentally transform the fragmented robotics industry and further accelerate its disruption.Standard modules and interfaces will bring down the cost to enter the market and shorten the time to market. As DRL and robotics technology improves, we will see a boom across industries including agriculture, food processing, home, and even surgical robotics.How technology will transform the industry landscapeLed by industry leaders like Amazon and Ocado, a British online grocery store, retailers see efficient and cost-effective supply chains as a key to success and are incentivized to automate their warehouses for lower cost, lower management complexity, and higher scalability. While Amazon keeps its Kiva mobile robots and automated warehouse solutions for in-house use only, Ocado launched its fulfillment platform to help other companies store, pick, and transport groceries. But most retailers still rely on material-handling companies and system integrators to build and maintain an automated warehouse.Incumbent material-handling companies tend to exploit the current technology, focusing on high-revenue-potential retrofit projects. This gives startups an opportunity to enter from the lower-end markets, where incumbents are less interested, and thus would not respond immediately. Dozens of startups offer scalable and affordable solutions that address customer needs for robots that can handle high-variability, low-precision tasks, such as piece picking in the warehouses.On the other hand, large robotics companies have long focused on developing high-speed and more-precise solutions to serve their main customers in the automotive and manufacturing industries. The pursuit of absolute precision is contrary to the process of deep learning, where results improve over time through trial and error.Some robotics companies saw an opportunity to enter new segments that require robots to handle high-variability, but lower-precision tasks, and began to build in-house AI teams and explore partnerships with startups developing deep neural network solutions for robots.There are currently more than 20 warehouse piece-picking robot startups worldwide, including Osaro and Righthand Robotics. Most of these startups (between seed round and Series C funding) are working on pilot projects with incumbent material-handling, retail, and robotics companies.Software and services will also capture more values, while premiums in hardware will decrease. With this power shift, as well as potential mergers and acquisitions, the landscape will change significantly over the next few years.The future of AI-enabled roboticsThoughts vary on the effect that AI and DRL will have on the robotics industry. One scenario is the robotics industry will consolidate because of the AI network effect, which says results improve as the amount of training data they’re given increases. This idea is that whoever owns the most proprietary data will win the competition.However, new researches in the areas of Transfer Learning, Meta Learning, and Zero-Shot Learning make it possible to learn from fewer and fewer examples, generalizing learning results across different fields. Cloud-based AI and robotics will also help accelerate learning and diffusion.As these technologies evolve, the landscape may face yet another significant change. This all starts from the warehouse!

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