Hidden occupational health risks impact both employees and businesses across sectors such as manufacturing, logistics, retail, and construction. By addressing these risks early and promoting safer working conditions, companies can achieve a 46% increase in efficiency!&nbsp;&nbsp;To discuss the critical role of safe work environments, Xsens is hosting a crash course webinar series that consists of two chapters. The first chapter,&nbsp;<a href="https://www.movella.com/resources/live-stream/workplace-ergonomics-maximising-workplace-health-and-efficiency" target="_blank" rel="noreferrer noopener">Workplace Ergonomics: Maximize Employee Well-being and Productivity</a>, was held on April 30, and you can&nbsp;now&nbsp;<a href="https://bit.ly/3W7ePEn" target="_blank" rel="noopener noreferrer">register for the second session</a>, which takes place on July 9.&nbsp;&nbsp;<iframe width="640" height="360" src="https://www.youtube.com/embed/NxUrHnd9xmQ?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp;</iframe>&nbsp;&nbsp;Here are the topics we discussed in the first chapter: &nbsp;&nbsp;<ol start="1"><li data-leveltext="%1." data-font="Aptos" data-listid="1" data-list-defn-props='{"335552541":0,"335559685":720,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}' data-aria-posinset="1" data-aria-level="1">What is workplace ergonomics, and how does it impact occupational health and safety?&nbsp;&nbsp;</li></ol>Workplace ergonomics is a critical aspect of occupational health &amp; safety. It involves tailoring workspaces and processes in line with human physical and cognitive abilities. It aims to reduce the risk of musculoskeletal disorders (MSDs) and enhance a facility's safety and productivity. Poor ergonomics can lead to significant financial impacts, including high injury costs and operational disruptions. On average, workplace injuries cost businesses over $20,000 per incident and result in about 20 days (about 3 weeks) of lost work time.&nbsp;&nbsp;<ol start="2"><li data-leveltext="%1." data-font="Aptos" data-listid="1" data-list-defn-props='{"335552541":0,"335559685":720,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}' data-aria-posinset="2" data-aria-level="1">Why is workplace ergonomics so important for organizations? &nbsp;&nbsp;</li></ol>Poor ergonomics in workplaces can result in high injury costs and challenges in maintaining operational continuity. Furthermore, companies need to abide by international standards, such as ISO, OSHA, and EN norms, that serve as objective tools for ergonomic assessments and imply legal consequences for non-compliant facilities.&nbsp;&nbsp;<ol start="3"><li data-leveltext="%1." data-font="Aptos" data-listid="1" data-list-defn-props='{"335552541":0,"335559685":720,"335559991":360,"469769242":[65533,0],"469777803":"left","469777804":"%1.","469777815":"hybridMultilevel"}' data-aria-posinset="3" data-aria-level="1">What are some of the different ergonomic assessment methods and tools often used by organizations?&nbsp;&nbsp;</li></ol>There are several options for conducting ergonomic assessment today:&nbsp;<ul><li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props='{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="1" data-aria-level="1">Traditional ergonomic assessments rely on manual methods such as checklists and video recordings to observe worker postures and movements based on international standards such as ISO and OSHA.&nbsp;&nbsp;</li><li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props='{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="2" data-aria-level="1">Advanced software tools, including Siemens Process Simulate and CATIA, offer digital workspace design and ergonomic assessment capabilities.&nbsp;&nbsp;</li><li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props='{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="3" data-aria-level="1">AI-powered video analysis tools automate assessments by tracking joint angles and movements but may not fully capture dynamic work environments.&nbsp;&nbsp;</li><li data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props='{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}' data-aria-posinset="4" data-aria-level="1">Motion capture, allowing for rapid data collection and&nbsp;objective&nbsp;analysis, offers a quick return on investment by reducing injury rates and associated costs.&nbsp;&nbsp;</li></ul>Prioritizing improved ergonomics can profoundly impact worker well-being and satisfaction. Businesses can lower costs and boost productivity by reducing injuries and creating safer work environments. &nbsp;&nbsp;&nbsp;Do not miss the second chapter of our webinar series, where we will talk about the role of motion capture in promoting occupational health &amp; safety, real-life use cases, and ROI! Register today:&nbsp;<a href="https://bit.ly/3W7ePEn" target="_blank" rel="noopener noreferrer">https://bit.ly/3W7ePEn</a>&nbsp;&nbsp;

A woman wearing Xsens Awinda suit sorting our fruits in the supermarket environment. The pose analytics are visualized using Scalefit

Improved occupational health and safety can enhance productivity by 46% and boost employee well-being. To address these opportunities, Xsens is launching a crash course on topics like injury risk reduction, regulation compliance, and the positive influence of safer workspaces.

A crash course on occupational health & safety: safer workplaces for improved productivity

A robot mimics the folded look of rose petals to grasp complex shapes more easily than a traditional hand. A pneumatic clamp makes it easier for people with motor disabilities to safely wield kitchen knives. Prostheses utilize shape memory polymers to better replicate the range of motion of a limb.All three of these concepts fall under the category of “soft robotics,” a field that uses strings, magnets, air, water and other non-rigid elements to design robotic solutions for biomechanical or industrial challenges.The Soft Robotics Toolkit (SRT), developed by the&nbsp;<a href="http://biodesign.seas.harvard.edu/" target="_blank">Harvard Biodesign Lab</a> at the&nbsp;<a href="https://seas.harvard.edu/" target="_blank">Harvard John A. Paulson School of Engineering and Applied Sciences</a> (SEAS), aims to provide a platform for designers, researchers and students to share information and resources in the field. Led by Conor Walsh, Paul A. Maeder Professor of Engineering and Applied Sciences at SEAS, the group recently resumed the SRT Competition, which challenged teams to design, fabricate and present their soft robotic innovations. The competition ran annually from 2015-18, then returned as a virtual showcase this year.“We have a new team at the SRT, and with that team comes new excitement and eagerness to continue to provide useful and engaging content to our audience,” said Jesse Grupper, a mechanical engineering research fellow in the Harvard Biodesign Lab and member of the&nbsp;<a href="https://softroboticstoolkit.com/" target="_blank">SRT team</a>. “We want to continue to benefit our SRT community and felt like now was the time to show that we are still committed and want to continue to see growth in the field.”Thirty-five teams from around the world competed in Open, College and High School categories of the SRT Competition. Projects were judged based on innovation, implementation, and documentation, with cash prizes awarded to the top designs in each category.“Sharing these novel projects on our site garners the interest of other researchers, encourages beginners in the field to join the community, and excites members to continue to contribute their own projects,” Grupper said. “Overall, the SRT Competition enables more people to become involved with the community which further pushes the boundaries of the field as a whole.”<iframe width="640" height="360" src="https://www.youtube.com/embed/E1wAI09LaoY?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>ROSE: Torsional Robotic Soft Gripper (Japan Advanced Institute of Science and Technology )First place in the Open category went to ROSE, a torsional soft robotic gripper designed at the Japan Advanced Institute of Science and Technology. The funnel-shaped gripper folds around and encompasses objects, enabling the soft membrane to grasp and hold complex, small or slippery objects. The Open runner-up prize went to a team from Northeastern University, which designed a series of pneumatically actuated origami robots that can support the weight of a human being.<iframe width="640" height="360" src="https://www.youtube.com/embed/H6yJK9ToCL8?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>Dual-Mode Soft Robotics for Enhancing Kitchen Independence for Fine Motor Disabilities (Barker College in Hornsby, Australia)In the College category, a team from Barker College in Australia designed a pair of soft grippers for use in the kitchen. When actuated, the devices hold or clamp vegetables in place, making it easier for a person with motor challenges to chop or cut them. Runner-up went to a soft robotic hand designed at the University of Illinois at Urbana-Champaign.<iframe width="640" height="360" src="https://www.youtube.com/embed/3nRMOhhcFqM?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>4D Prosthetic (Quarry Lane School)A team from Quarry Lane School in California won the High School category with its 4D Prosthetic. The device uses shape memory polymers to simulate complex motions such as fingers gripping an object. The polymers hold their shape until exposed to heat such as a hot water bath, at which point they can be reshaped into a new position.“The SRT Team has undergone a lot of changes in the last five years,” Grupper said. “While our staff has shifted, and some of our objectives have changed, we've always been dedicated to expanding the field, and fostering an inclusive space for those that want to join our community.”

A robot mimics the folded look of rose petals to grasp complex shapes more easily than a traditional hand. A pneumatic clamp makes it easier for people with motor disabilities to safely wield kitchen knives. Prostheses utilize shape memory polymers to better replicate the range of motion of a limb.

A world of soft robotics

A multidisciplinary research team from multiple institutions &nbsp;— led by the <a href="https://www.uri.edu/" target="_blank">University of Rhode Island (URI)</a> — successfully demonstrated new technologies that can obtain preserved tissue and high-resolution 3D images within minutes of encountering some of the most fragile animals in the deep ocean.The research team, which included <a href="https://seas.harvard.edu/person/robert-wood" target="_blank">Robert J. Wood,</a> the Harry Lewis and Marlyn McGrath Professor of Engineering and Applied Sciences at the <a href="https://seas.harvard.edu/" target="_blank">Harvard John A. Paulson School of Engineering and Applied Sciences&nbsp;</a>(SEAS), demonstrated that it is possible to shave years from the process of determining whether a new or rare species has been discovered.The results of their work are published today in the journal <a href="https://www.science.org/doi/10.1126/sciadv.adj4960" target="_blank">Science Advances</a>.Roboticists, ocean engineers, bioengineers, and marine and molecular biologists from&nbsp;SEAS, URI’s Department of Ocean Engineering; the Bigelow Laboratory for Ocean Sciences in East Boothbay, Maine; Monterey Bay Aquarium Research Institute (MBARI) in California; PA Consulting, a worldwide firm that focuses on innovation; and the Department of Natural Sciences at Baruch College, City University of New York, made up the team. The paper represents five years of research.Revolutionary advancements in underwater imaging, robotics, and genomic sequencing have reshaped marine exploration, the study reads. The research shows that within minutes of an encounter with a deep-sea animal, it is possible to capture detailed measurements and motion of the animal, obtain an entire genome, and generate a comprehensive list of genes being expressed that point to their physiological status in the deep ocean. The result of this rich digital data is a ‘cybertype’ of a single animal, rather than a physical ‘holotype’ that is traditionally found in museum collections.“Currently, if researchers want to describe what they believe is a new species, they face an arduous process,” said&nbsp;URI Professor Brennan Phillips. “The way it is done now is you capture a specimen, which is very difficult because a lot of these animals are so delicate and tissue-thin, and it’s likely you may not be able to collect them at all. But if you successfully collect an animal, you then preserve it in a jar. Then begins a long process of physically bringing that specimen to different collections around the world where it is compared to existing organisms. After a long time, sometimes up to 21 years, scientists may reach consensus that this is a new species.&nbsp;Again, these are deep-sea, thin little wisps of animals. The current workflow is not appropriate. It’s a major reason why we have so many undescribed species in the ocean.”Information gained from the study—and others that follow—could be useful for extinction prevention studies, as it provides a wealth of information from a single specimen gained during a single encounter. The work also responds to the growing call among researchers for compassionate collection, which minimizes harm to animals by using advanced technologies to collect information. Future studies and development could allow for complete scans and inventories of life in the deep sea within a catch-and-release framework.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1705965458949-Marine+animals+at+focus.jpg" style="width: 300px;" class="fr-fic fr-dib">Composite image of gelatinous deep-sea animals observed and sampled in the study. &nbsp;Clockwise starting from upper left: the holoplanktonic polychaete&nbsp;Tomopteris&nbsp;sp.,&nbsp;the siphonophore&nbsp;Marrus claudanielis, the siphonophore&nbsp;Erenna sp., and the salp&nbsp;Pegea sp. (Photos courtesy of ROV SuBastian science camera, Schmidt Ocean Institute.)“The vision was: How might a marine biologist work to better understand and connect to deep-sea life decades or centuries into the future?” said David Gruber, Distinguished Professor of Biology at Baruch College, City University of New York, and an Explorer with National Geographic Society. “This is a demonstration on how an interdisciplinary team could work collaboratively to provide an enormous amount of new information on deep-sea life after one brief encounter. The ultimate goal is to continue down this path and refine the technology to be as minimally-invasive as possible — akin to a doctor's check-up in the deep sea. This approach is becoming increasingly important with current extinction rates being 100 times higher than background extinction rates.”Phillips said because collecting these samples has always been hard, there are many deep-sea species that have yet to be identified. “When you look at climate change and deep-sea mining and their potential effects, it is unsettling,” Phillips said. “You realize you don’t have a full baseline of species, and you may not know what you’ve lost before it’s too late. If you want to know what has been there before it’s gone, this is a new way to do that.”&nbsp;The mission, which was funded by the Schmidt Ocean Institute and its&nbsp;<a href="https://schmidtocean.org/cruise/designing-the-future-2/" target="_blank">Designing the Future&nbsp;</a>program, and conducted on its research vessel&nbsp;Falkor, included two expeditions off the coast of Hawaii and San Diego in 2019 and 2021. The team collected as many as 14 preserved tissue samples a day, along with terabytes of quantitative digital imagery. Together, the study provided:&nbsp; &nbsp;<ul><li>The first complete assembled and annotated transcriptome (genes being made in the animals’ habitat) of&nbsp;Pegea tunicate, a marine invertebrate animal;</li><li>Details of the molecular basis of environmental sensing of a holoplanktonic&nbsp;Tomopteris polychaete (marine worm), which spends its entire life in the water column;</li><li>Details of the full transcriptomes of two siphonophores, (gelatinous zooplankton composed of specialized parts growing together in a chain)&nbsp;Erenna&nbsp;sp. and&nbsp;Marrus claudanielis, as well as the&nbsp;Pegea tunicate and&nbsp;Tomopteris polychaete;</li><li>Full morphological (form and structure) characterizations using digital imaging of each animal while at depth.</li></ul>The lead author of the paper, John Burns, a senior research scientist at Bigelow Laboratory, conducted the genomic analysis on four animals sampled at depths of almost 4,000 feet.“What we were able to achieve with these animals is remarkable,” Burns said. “For me, this is best seen in the sequence data we generated for the&nbsp;Tomopteris worm: We captured it while it was exploring its environment and were able to infer that it was scanning the water using two long sensory whiskers near its head for ‘sweet’ tastes: likely sugars associated with prey, and possibly for ammonia: a waste product of its typical prey.“With that information, we can envision how it hunts by following chemical trails in its open water habitat,” Burns said. “I don’t think that would have been possible without the innovative technology invented and employed by the engineers on the team that allowed complete preservation of the information from the animals within minutes of an encounter.”Burns said another study with Gruber looked at how capture methods affect jellyfish ribonucleic acid, known as RNA, one of the building blocks of life. That sequence of information can start to change after about 10 minutes of stressful conditions, even with gentle collection. The Designing the Future technologies overcome this by preserving the information before the animal’s cells start to respond to stress, according to Burns. “We also discovered that three of the animals we captured have huge genomes: each having nearly 10 times the DNA in a cell compared to us humans,” Burns said. “For the fourth, with a more modestly sized genome (about 3% the size of a human genome) we were able to use cutting edge sequencing methods to build the most cohesive and complete genome of a salp to date.”<blockquote>We are seeing the impact of new types of marine robots for midwater and deep-sea exploration. Not only are robots going places that are difficult or impossible for humans to reach, our devices investigate, interact with, and collect specimens using a gentle touch or no touch at all.ROBERT J. WOODHARRY LEWIS AND MARLYN MCGRATH PROFESSOR OF ENGINEERING AND APPLIED SCIENCES</blockquote>SEAS and URI brought to the mission a rotary-actuated folding dodecahedron (RAD-2), an innovative origami-inspired robotic encapsulation device, which collected animal tissue samples and almost instantaneously preserved that tissue at depth.&nbsp;“We are seeing the impact of new types of marine robots for midwater and deep-sea exploration,” said Wood. “Not only are robots going places that are difficult or impossible for humans to reach, our devices investigate, interact with, and collect specimens using a gentle touch or no touch at all.”Imaging systems from MBARI’s Bioinspiration Lab that included a laser-scanning imaging device called&nbsp;<a href="https://www.mbari.org/technology/deeppiv/" target="_blank">DeepPIV</a> and a three-dimensional lightfield camera called&nbsp;<a href="https://www.mbari.org/technology/eyeris/" target="_blank">EyeRIS</a> enabled the measurement and reconstruction of three-dimensional morphology, or body shape, of the animals in their natural environment.“We cannot protect what we do not yet fully understand. Advanced imaging technologies can accelerate our efforts to document the diversity of life in the ocean. The faster we can catalog marine life, the better we can assess and track the impact of human actions like climate change and mining on ocean environments,” said Kakani Katija, bioengineer and principal engineer of the Bioinspiration Lab at MBARI.&nbsp;“We have these remotely operated vehicles out there with advanced imaging systems, which can create a three-dimensional model after only a few minutes,” Phillips said. “We were able to approach a tiny jellyfish in a matter of seconds, collect high-resolution 3D images to the control room, and our team was able to tell in a matter of minutes that the tentacles were exactly 5 millimeters long. Then, we had extremely well-preserved tissue samples of the same animal within a matter of minutes."

Multi-institution team uses quantitative imaging technology, innovative robotic device to capture tissue in minutes, preserve for advanced genomic study

Research demonstrates success of new technology in conducting deep-sea research on fragile organisms

<h2 dir="ltr" id="isPasted">What Is ESG?&nbsp;</h2>ESG stands for Environmental, Social and Governance – three key factors used to measure the societal impact and sustainability of a business or organisation. ESG metrics are gaining in importance, with companies facing mounting social pressure to comply with evolving regulations. Regulators in America and the European Union are increasing their oversight in this area, so there’s a strong focus on investment in ESG reporting.The challenge facing companies today is to address these key factors of the ESG framework. You’ll be looking at:&nbsp;<ul><li dir="ltr">Environmental issues, including energy usage and climate change impact&nbsp;</li><li dir="ltr">Social issues, including equity and inclusion</li><li dir="ltr">Governance issues, including transparency and accountability</li></ul>Most organisations are still using a time-consuming and error-prone approach to comply with ESG regulations. This means a manual process of data collection, analysis and reporting, with all its attendant risks. High volumes of raw data are often still processed by dispersed team members, using outdated and unspecialised tools like spreadsheets and calculators. Without a centralised intake point and streamlined digital processing, there’s no single source of truth, resulting in a risky and inefficient workflow.&nbsp;However, the tech-driven advances of Industry 4.0 are introducing&nbsp;<a href="https://www.rowse.co.uk/blog/post/the-role-of-ai-in-sustainable-manufacturing" target="_blank">new methods</a> of keeping up with ESG compliance. Intelligent automation (IA) makes compliance not only possible but will transform your whole operation. Adopting appropriate technology like digital processing solutions is therefore crucial to the ESG agenda.&nbsp;<h2 dir="ltr">What Technologies Support ESG Compliance?</h2>As the pressure rises on ESG considerations, many companies will be considering IA technologies to improve their performance. These include process intelligence (PI), IA cloud services and robotic process automation (RPA) strategies that can help businesses become greener and more sustainable.&nbsp;It’s important to distinguish IA from AI (artificial intelligence), which simulates human thinking. AI is only one of the factors contributing to IA,&nbsp;<a href="https://www.rowse.co.uk/blog/post/how-to-use-ai-ml-to-optimise-manufacturing-costs" target="_blank">along with machine learning</a> (ML), RPA, etc. Taken all together, this cognitive technology is able to expand factory automation capabilities.&nbsp;Process intelligence is another way to describe big data and its analysis. It takes data collected from various business software systems such as Enterprise Resource Planning and Customer Relationship Management. It then uses this data to analyse your organisational processes, to find areas for potential improvement. PI detects process irregularities and identifies their root cause, which is often human error. It then works with RPA to deliver an automated solution that drastically reduces human intervention – even removing that factor altogether.&nbsp;<h2 dir="ltr">Technology’s Role In ESG Compliance&nbsp;</h2><h3 dir="ltr">Environmental</h3>To address climate threats like greenhouse gas emissions and become carbon neutral, you’ll have to take into account your current energy consumption. You will also need to assess the impact of ESG compliance on existing employees and how compliance aligns with your overall business strategy. Data collection and analysis tools like PI can help you understand your process flows and how they can fit into the ESG compliance framework, making environmental targets easier to achieve. For instance, you can identify process bottlenecks like sluggish or blocked supply chains. In such ways, you can streamline your operations and cut down your carbon emissions.<h3 dir="ltr">Social&nbsp;</h3>PI also helps identify instances where staff and/or customers are not treated fairly, equally or inclusively. It can locate potential conflicts of interest and suggest solutions that will help you address these issues promptly. This is a step towards the long-term development of trust between all members of a company and its customers. It will also improve your organisation’s business and social reputation, thus having a positive impact on its ESG performance.<h3 dir="ltr">Governance&nbsp;</h3>Transparency in regulatory reporting is founded on increasing data quality and data governance, automating processes and streamlining ESG data. ESG compliance includes following data privacy protection regulations, tracking the transparency of supply chains and enforcing inclusive hiring practices. PI tools help identify aspects of these areas where greater automation is an advantage to ESG reporting. These are primarily those where full transparency demands real-time frequent monitoring, as well as audit trails and demonstrable compliance.&nbsp;<h2 dir="ltr">Increased Efficiency</h2>Automating repetitive, time-consuming tasks with RPA liberates employees to concentrate on higher-level tasks. This also reduces these tasks’ carbon footprint.Cost savings derived from increased efficiency might include labour costs on those tasks now automated, and less time spent on customer-related inquiries and service.RPA performs automated tasks with greater accuracy, improving data quality and reducing errors. It also works 24/7, collecting data constantly in real-time and can be scaled up or down in response to changing market conditions. Since RPA can work in almost any system with a user interface, it has access to many more data points.<h2 dir="ltr">Reducing Your Carbon Footprint With RPA</h2>Digital workers are generally located on servers that don’t move from place to place. They create no carbon footprint – unlike human workers, who travel to and from the workplace. Neither do they produce waste from items consumed during working hours, such as disposable cups and food packaging.Automation with RPA digital workers can help to reduce your carbon footprint, as they’re more energy-efficient than human beings. They run on electricity rather than food and drink, so usually require less energy to carry out the same tasks.&nbsp;Modern computing equipment is also much more energy-efficient, especially when using cloud services, which further reduces their carbon footprint. Digital workers can operate continuously, 24/7, so they’re more efficient over longer periods. They’re also consistently far more accurate. They complete more work in the same amount of time as humans, with fewer errors or do-overs.These factors all contribute to digital workers creating a lower carbon footprint than human workers.<h2 dir="ltr">Factory Automation And ESG Compliance&nbsp;</h2>The combination of RPA and PI can significantly improve your ESG performance. Once production processes are automated and optimised, you’ll start seeing reductions in waste and environmental threats. Your carbon footprint will be smaller, while your energy efficiency will improve. Your social impact will be enhanced by freeing human employees for higher-level work, thus encouraging a more engaged and motivated workforce.&nbsp;All these factors can be collected as data and assessed by off-the-shelf software packages that make ESG compliance much easier.

The Role Of Factory Automation In ESG Compliance

Discover the role of factory automation in ESG compliance.

At EPFL's&nbsp;<a href="https://www.epfl.ch/labs/create/" target="_blank">CREATE lab</a>, under the guidance of Josie Hughes, a breakthrough has been made in the realm of soft robotics. Drawing inspiration from the versatile movement of elephant trunks and octopus tentacles, the team introduced the trimmed helicoid — a novel robotic structure that promises greater compliance and control in robotic designs. With a blend of keen biological observation and computational modeling, the researchers have now unveiled a soft robot arm capable of intricate tasks, ensuring safer human-robot interactions. The findings, detailing both the structure and methodology, are a collaboration with the Department of Cognitive Robotics at TU Delft and were published in Nature's new journal, npj Robotics.Professor Hughes highlighted the importance of this development: "Through the invention of a new architectured structure, the trimmed helicoid, we've designed a robot arm that excels in control, range of motion, and safety. When the novel architecture is combined with distributed actuation— where multiple actuators are placed throughout a structure or device—this robot arm has a vast range of motion, high precision, and is inherently safe for human interaction."Whereas traditional robots are rigid, often making them unsuitable for delicate tasks or close human interactions, CREATE's soft robot arm is designed for safer interactions with humans and adaptability to a wider range of tasks. With an unprecedented combination of flexibility and precision, the soft and compliant nature of the arm reduces potential risks during human-robot interactions. This opens doors for its application in healthcare, elderly care, and more. Unlike their rigid counterparts, the soft robot arm can adapt to different shapes and surfaces, making it an ideal tool for intricate tasks like picking fruits or handling fragile items. In industry, it might become the go-to solution for delicate assembly lines, working alongside humans, augmenting their capacity instead of replacing them. The agricultural industry could also benefit from its gentle touch in handling crops, accompanying workers to lessen their workload during intense harvesting periods.The crux of the research lies in the robotic arm’s novel architecture. The researchers have creatively modified a spring-like spiral, which they call a 'helicoid', by trimming parts of it to give it diverse functionalities. This seemingly simple act has allowed them to precisely control how flexible or stiff the spiral becomes in different directions. By adjusting its shape, they can make the inner part resistant to being squashed and the outer part flexible enough to bend easily. With this special design, they've created a soft robot that can move and act in ways previously unseen, showing the kind of dexterity and soft touch found in nature, like in an elephant's trunk or an octopus's tentacle."By observing these animals and developing a novel architectured structure, we aim to mimic this range of motion and control found in Nature," Josie Hughesnoted. To do so, the team employed advanced computer modeling to turn observations into tangible results. Using these models, they iteratively tested their innovative spiral designs — into a final trimmed helicoid shape. Qinghua Guan and Francesco Stella, who spearheaded the actual creation of the robot, gave insights into the design and optimization process: "We introduce a specific surface into the computer model, then trim and adjust. Computational methods guide us, helping assess the optimal geometric structure for maximum workspace and compliance." The result? A robotic creation, drawing from nature, but refined with precise human ingenuity and computer modeling. “In the end, our computer models were accurate to the point where we only had to build one version of the arm.”The advancements at EPFL's CREATE lab symbolize a pivotal shift in the robotics field. Traditional robotic applications, dominated by rigid mechanics, could see a shift towards this softer, more human-friendly counterpart. A patent has been filed for this first commercial soft manipulator, and a EPFL and TU Delft joint start-up has been launched under the name Helix Robotics. As Professor Hughes aptly summarized, "Merging keen observations from nature with precise computational modeling has unveiled the potential of soft robotics for future commercial applications. As we move forward, our aim is to bring robots closer to humans, not just in proximity but in understanding and collaboration. We hope that this soft robotic arm exemplifies a future where machines assist, complement, and understand human needs more deeply than ever before."<hr>Professor Josie Hughes will be showing the Helix robot along with several other of her creations at the&nbsp;<a href="https://swissroboticsday.ch/" target="_blank" rel="noopener">Swiss Robotics Day</a> at ETHZ on November 3rd. Many other EPFL professors will be showcasing their work. The day is dedicated to the best of Swiss robotics across industry, startups, universities, research labs, nonprofits, and government. The event features high-profile speakers (Boston Dynamics, Disney) and panelists, along with startup pitches and 50+ exhibition booths with live demos.ReferencesGuan, Q., Stella, F., Della Santina, C. et al. Trimmed helicoids: an architectured soft structure yielding soft robots with high precision, large workspace, and compliant interactions. npj Robot 1, 4 (2023). https://doi.org/10.1038/s44182-023-00004-7

EPFL researchers have designed a bio-inspired robot with a novel trimmed helicoid structure that allows for a wide range of motion and safe interaction with humans.

Soft, elephant trunk-like robot for close interaction with humans

This article was first published on <a href="https://news.mit.edu/2023/ai-technique-robots-manipulate-objects-whole-bodies-0824" target="_blank" rel="noopener noreferrer" id="isPasted">MIT News</a>. &nbsp;<hr>Imagine you want to carry a large, heavy box up a flight of stairs. You might spread your fingers out and lift that box with both hands, then hold it on top of your forearms and balance it against your chest, using your whole body to manipulate the box.&nbsp;Humans are generally good at whole-body manipulation, but robots struggle with such tasks. To the robot, each spot where the box could touch any point on the carrier&rsquo;s fingers, arms, and torso represents a contact event that it must reason about. With billions of potential contact events, planning for this task quickly becomes intractable.Now MIT researchers found a way to simplify this process, known as contact-rich manipulation planning. They use an AI technique called smoothing, which summarizes many contact events into a smaller number of decisions, to enable even a simple algorithm to quickly identify an effective manipulation plan for the robot.<iframe width="640" height="360" src="https://www.youtube.com/embed/D8VIzBRy_m4?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>While still in its early days, this method could potentially enable factories to use smaller, mobile robots that can manipulate objects with their entire arms or bodies, rather than large robotic arms that can only grasp using fingertips. This may help reduce energy consumption and drive down costs. In addition, this technique could be useful in robots sent on exploration missions to Mars or other solar system bodies, since they could adapt to the environment quickly using only an onboard computer. &nbsp; &nbsp; &nbsp;&ldquo;Rather than thinking about this as a black-box system, if we can leverage the structure of these kinds of robotic systems using models, there is an opportunity to accelerate the whole procedure of trying to make these decisions and come up with contact-rich plans,&rdquo; says H.J. Terry Suh, an electrical engineering and computer science (EECS) graduate student and co-lead author of a&nbsp;<a href="http://groups.csail.mit.edu/robotics-center/public_papers/Pang22.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">paper</a> on this technique.Joining Suh on the paper are co-lead author Tao Pang PhD &rsquo;23, a roboticist at Boston Dynamics AI Institute; Lujie Yang, an EECS graduate student; and senior author Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The research appears this week in&nbsp;IEEE Transactions on Robotics.<h2>Learning about learning</h2>Reinforcement learning is a machine-learning technique where an agent, like a robot, learns to complete a task through trial and error with a reward for getting closer to a goal. Researchers say this type of learning takes a black-box approach because the system must learn everything about the world through trial and error.It has been used effectively for contact-rich manipulation planning, where the robot seeks to learn the best way to move an object in a specified manner.But because there may be billions of potential contact points that a robot must reason about when determining how to use its fingers, hands, arms, and body to interact with an object, this trial-and-error approach requires a great deal of computation.&ldquo;Reinforcement learning may need to go through millions of years in simulation time to actually be able to learn a policy,&rdquo; Suh adds.On the other hand, if researchers specifically design a physics-based model using their knowledge of the system and the task they want the robot to accomplish, that model incorporates structure about this world that makes it more efficient.Yet physics-based approaches aren&rsquo;t as effective as reinforcement learning when it comes to contact-rich manipulation planning &mdash; Suh and Pang wondered why.They conducted a detailed analysis and found that a technique known as smoothing enables reinforcement learning to perform so well.Many of the decisions a robot could make when determining how to manipulate an object aren&rsquo;t important in the grand scheme of things. For instance, each infinitesimal adjustment of one finger, whether or not it results in contact with the object, doesn&rsquo;t matter very much. &nbsp;Smoothing averages away many of those unimportant, intermediate decisions, leaving a few important ones.Reinforcement learning performs smoothing implicitly by trying many contact points and then computing a weighted average of the results. Drawing on this insight, the MIT researchers designed a simple model that performs a similar type of smoothing, enabling it to focus on core robot-object interactions and predict long-term behavior. They showed that this approach could be just as effective as reinforcement learning at generating complex plans.&ldquo;If you know a bit more about your problem, you can design more efficient algorithms,&rdquo; Pang says.<h2>A winning combination</h2>Even though smoothing greatly simplifies the decisions, searching through the remaining decisions can still be a difficult problem. So, the researchers combined their model with an algorithm that can rapidly and efficiently search through all possible decisions the robot could make.With this combination, the computation time was cut down to about a minute on a standard laptop.They first tested their approach in simulations where robotic hands were given tasks like moving a pen to a desired configuration, opening a door, or picking up a plate. In each instance, their model-based approach achieved the same performance as reinforcement learning, but in a fraction of the time. They saw similar results when they tested their model in hardware on real robotic arms.&ldquo;The same ideas that enable whole-body manipulation also work for planning with dexterous, human-like hands. Previously, most researchers said that reinforcement learning was the only approach that scaled to dexterous hands, but Terry and Tao showed that by taking this key idea of (randomized) smoothing from reinforcement learning, they can make more traditional planning methods work extremely well, too,&rdquo; Tedrake says.However, the model they developed relies on a simpler approximation of the real world, so it cannot handle very dynamic motions, such as objects falling. While effective for slower manipulation tasks, their approach cannot create a plan that would enable a robot to toss a can into a trash bin, for instance. In the future, the researchers plan to enhance their technique so it could tackle these highly dynamic motions.&ldquo;If you study your models carefully and really understand the problem you are trying to solve, there are definitely some gains you can achieve. There are benefits to doing things that are beyond the black box,&rdquo; Suh says.<hr>&quot;Reprinted with permission of <a href="https://news.mit.edu/2023/ai-technique-robots-manipulate-objects-whole-bodies-0824" target="_blank" rel="noopener noreferrer" id="isPasted">MIT News</a>&quot; &nbsp;

MIT researchers developed an AI technique that enables a robot to develop complex plans for manipulating an object using its entire hand, not just the fingertips. This model can generate effective plans in about a minute using a standard laptop. Here, a robot attempts to rotate a bucket 180 degrees. Image: Courtesy of the researchers

With a new technique, a robot can reason efficiently about moving objects using more than just its fingertips.

AI helps robots manipulate objects with their whole bodies

With their ability to process vast amounts of text data, and to use this information to answer prompts, neural networks known as large language models (LLMs) like Chat-GPT have been making headlines for their potential to change the way we <a href="https://longread.epfl.ch/en/dossier/artificial-intelligence-friend-or-foe/" target="_blank">write, learn, and even make art</a>. Now, EPFL researchers have applied the technology to a new sphere: robotic design.In <a href="https://www.nature.com/articles/s42256-023-00669-7" rel="noopener" target="_blank">a case study</a> published in Nature Machine Intelligence, Josie Hughes, head of the <a href="https://www.epfl.ch/labs/create/" target="_blank">Computational Robot Design &amp; Fabrication Lab</a> in the School of Engineering, EPFL PhD student Francesco Stella, and Cosimo Della Santina of TU Delft used Chat-GPT to design a working robotic tomato-harvester. The study provides a framework for humans and LLMs to design such devices collaboratively. Based on their experience, the researchers describe opportunities and risks of applying artificial intelligence (AI) tools to robotics, which they argue &ldquo;could change the way we design robots, while enriching and simplifying the process.&rdquo;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3MTc5NTUyMzcwLTM0Njh4NDYyNC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">&copy; CREATE Lab/EPFL&nbsp;&ldquo;Even though Chat-GPT is a language model and its code generation is text-based, it provided significant insights and intuition for physical design, and showed great potential as a sounding board to stimulate human creativity,&rdquo; says Hughes.Potential and pitfalls of AI as &lsquo;inventor&rsquo;In a first phase, the researchers and LLM engaged in an &lsquo;ideation&rsquo; discussion to define their robot&rsquo;s purpose, design parameters, and specifications. A second phase was devoted to realizing the robot in the real world, which involved refining the LLM-generated code, fabricating the device, and troubleshooting its functioning.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3MTc5NTc2MzA5LTMwMjR4NDAzMi5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">&copy; CREATE Lab/EPFL&nbsp;For the first phase, the researchers started at a high conceptual level, conversing with the LLM on future challenges to humanity, and identifying robotic crop harvesting as a solution to the challenge of global food supply. They then drew upon the LLM&rsquo;s access to global data from academic publications, technical manuals, books, and media to provide the &ldquo;most probable&rdquo; answer to prompts such as &ldquo;what features should a robot harvester have?&rdquo;Once a basic robotic format was identified (a motor-driven gripper for grasping ripe tomatoes), the researchers could then pose more specific questions, like &ldquo;what shape should the gripper have?&rdquo;, and ask the LLM to make technical suggestions including materials and computer code for controlling the device.&ldquo;While computation has been largely used to assist engineers with technical implementation, for the first time, an AI system can ideate new systems, thus automating high-level cognitive tasks. This could involve a shift of human roles to more technical ones,&rdquo; Stella says.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg3MTc5NzE2MjgzLWV6Z2lmLTUtNTdiZWU1MWE4Zi5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">&copy; CREATE Lab/EPFL&nbsp;In addition to assigning Chat-GPT the role of &lsquo;inventor&rsquo;, the researchers outlined other possible human-LLM collaboration modes in their paper. For example, &lsquo;collaborative exploration&rsquo; uses AI to augment researchers&rsquo; expertise by contributing wide-ranging knowledge beyond their own fields. AI can also act as a &lsquo;funnel&rsquo;, helping to refine the design process and providing technical input, with humans retaining creative control.As there are logical and ethical risks associated with each collaboration mode, the researchers caution that the role of LLMs must be carefully evaluated going forward. For example, the use of LLMs raises questions of bias, plagiarism, and intellectual property, as it&rsquo;s unclear if an LLM-generated design can be considered novel.&ldquo;In our study, Chat-GPT identified tomatoes as the crop &lsquo;most worth&rsquo; pursuing for a robotic harvester. However, this may be biased towards crops that are more covered in literature, as opposed to those where there is truly a real need. When decisions are made outside the scope of knowledge of the engineer, this can lead to significant ethical, engineering, or factual errors.&rdquo; Hughes says.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1687180178832-ezgif-5-62f982faad.jpg" style="width: 766px;" class="fr-fic fr-dib">&copy; CREATE Lab/EPFL&nbsp;Despite these cautions, Hughes and her team conclude, based on their experience, that LLMs have great potential to be a force for good, if well managed: &ldquo;The robotics community must therefore identify how to leverage these powerful tools to accelerate the advancement of robots in an ethical, sustainable, and socially empowering way.&rdquo;

EPFL researchers have used Chat-GPT-3 to develop a robotic gripper for harvesting tomatoes, in a first demonstration of the artificial intelligence tool’s potential for collaborating with humans on robot design.

Researchers unveil first Chat-GPT-designed robot

Welding Redefined: Unleashing the Power of Robotics for Unrivaled Precision, Efficiency, and Quality

Robotic Welding: A Comprehensive Guide

Grasping objects of different sizes, shapes and textures is a problem that is easy for a human, but challenging for a robot. Researchers from the University of Cambridge designed a soft, 3D printed robotic hand that cannot independently move its fingers but can still carry out a range of complex movements.&nbsp;The robot hand was trained to grasp different objects and was able to predict whether it would drop them by using the information provided from sensors placed on its &lsquo;skin&rsquo;.&nbsp;This type of passive movement makes the robot far easier to control and far more energy-efficient than robots with fully motorised fingers. The researchers say their adaptable design could be used in the development of low-cost robots that are capable of more natural movement and can learn to grasp a wide range of objects. The <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202200390" target="_blank">results</a> are reported in the journal Advanced Intelligent Systems.&nbsp;<iframe width="640" height="360" src="https://www.youtube.com/embed/haWziO6aOqc?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>In the natural world, movement results from the interplay between the brain and the body: this enables people and animals to move in complex ways without expending unnecessary amounts of energy. Over the past several years, soft components have begun to be integrated into robotics design thanks to advances in 3D printing techniques, which have allowed researchers to add complexity to simple, energy-efficient systems.&nbsp;The human hand is highly complex, and recreating all of its dexterity and adaptability in a robot is a massive research challenge. Most of today&rsquo;s advanced robots are not capable of manipulation tasks that small children can perform with ease. For example, humans instinctively know how much force to use when picking up an egg, but for a robot this is a challenge: too much force, and the egg could shatter; too little, and the robot could drop it. In addition, a fully actuated robot hand, with motors for each joint in each finger, requires a significant amount of energy.&nbsp;In <a href="http://www.eng.cam.ac.uk/profiles/fi224" target="_blank">Professor Fumiya&nbsp;</a><a href="http://www.eng.cam.ac.uk/profiles/fi224" target="_blank">Iida</a>&rsquo;s <a href="https://birlab.org/" target="_blank">Bio-Inspired Robotics Laboratory</a> in Cambridge&rsquo;s Department of Engineering, researchers have been developing potential solutions to both problems: a robot hand than can grasp a variety of objects with the correct amount of pressure while using a minimal amount of energy.&nbsp;&ldquo;In <a href="http://www.eng.cam.ac.uk/news/3d-printed-robot-hand-plays-piano" target="_blank">earlier experiments</a>, our lab has shown that it&rsquo;s possible to get a significant range of motion in a robot hand just by moving the wrist,&rdquo; said co-author Dr Thomas George-Thuruthel, who is now based at University College London (UCL) <a href="https://www.ucl.ac.uk/ucl-east/study-and-research/robotics-and-autonomous-systems" target="_blank">East</a>. &ldquo;We wanted to see whether a robot hand based on passive movement could not only grasp objects, but would be able to predict whether it was going to drop the objects or not, and adapt accordingly.&rdquo;&nbsp;The researchers used a 3D-printed anthropomorphic hand implanted with tactile sensors, so that the hand could sense what it was touching. The hand was only capable of passive, wrist-based movement.&nbsp;The team carried out more than 1,200 tests with the robot hand, observing its ability to grasp small objects without dropping them. The robot was initially trained using small 3D printed plastic balls, and grasped them using a pre-defined action obtained through human demonstrations.&nbsp;&ldquo;This kind of hand has a bit of springiness to it: it can pick things up by itself without any actuation of the fingers,&rdquo; said first author Dr Kieran Gilday, who is now based at EPFL in Lausanne, Switzerland. &ldquo;The tactile sensors give the robot a sense of how well the grip is going, so it knows when it&rsquo;s starting to slip. This helps it to predict when things will fail.&rdquo;&nbsp;The robot used trial and error to learn what kind of grip would be successful. After finishing the training with the balls, it then attempted to grasp different objects including a peach, a computer mouse and a roll of bubble wrap. In these tests, the hand was able to successfully grasp 11 of 14 objects.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1684333513339-CENTRE_RobotHand_CREDIT+Bio-Inspired+Robotics+Laboratory%2C+University+of+Cambridge.jpg" style="width: 766px;" class="fr-fic fr-dib">Credit: Bio-Inspired Robotics Laboratory, University of Cambridge.&nbsp;&ldquo;The sensors, which are sort of like the robot&rsquo;s skin, measure the pressure being applied to the object,&rdquo; said Dr George-Thuruthel. &ldquo;We can&rsquo;t say exactly what information the robot is getting, but it can theoretically estimate where the object has been grasped and with how much force.&rdquo;&nbsp;&ldquo;The robot learns that a combination of a particular motion and a particular set of sensor data will lead to failure, which makes it a customisable solution,&rdquo; said Dr Gilday. &ldquo;The hand is very simple, but it can pick up a lot of objects with the same strategy.&rdquo;&nbsp;&ldquo;The big advantage of this design is the range of motion we can get without using any actuators,&rdquo; said Professor Iida. &ldquo;We want to simplify the hand as much as possible. We can get lots of good information and a high degree of control without any actuators, so that when we do add them, we&rsquo;ll get more complex behaviour in a more efficient package.&rdquo;&nbsp;A fully actuated robotic hand, in addition to the amount of energy it requires, is also a complex control problem. The passive design of the Cambridge-designed hand, using a small number of sensors, is easier to control, provides a wide range of motion, and streamlines the learning process.&nbsp;In future, the system could be expanded in several ways, such as by adding computer vision capabilities, or teaching the robot to exploit its environment, which would enable it to grasp a wider range of objects.&nbsp;<hr>Reference: K. Gilday, Dr. T. George-Thuruthel, Prof. F. Iida. &lsquo;<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202200390" target="_blank">Predictive Learning of Error Recovery with a Sensorised Passivity-based Soft Anthropomorphic Hand</a>.&rsquo; Advanced Intelligent Systems (2023). 10.1002/aisy.202200390&nbsp;

Researchers have designed a low-cost, energy-efficient robotic hand that can grasp a range of objects – and not drop them – using just the movement of its wrist and the feeling in its ‘skin’.  

It's all in the wrist: energy-efficient robot hand learns how not to drop the ball

A bird landing on a branch makes the maneuver look like the easiest thing in the world, but in fact, the act of perching involves an extremely delicate balance of timing, high-impact forces, speed, and precision. It&rsquo;s a move so complex that no flapping-wing robot (ornithopter) has been able to master it, until now.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1671502161855-3678x2069.jpg" style="width: 300px;" class="fr-fic fr-dib">&copy; Raphael Zufferey&nbsp;Raphael Zufferey, a postdoctoral fellow in the Laboratory of Intelligent Systems (<a href="https://www.epfl.ch/labs/lis/" target="_blank">LIS</a>) and Biorobotics ab (<a href="https://www.epfl.ch/labs/biorob/" target="_blank">BioRob</a>) in the School of Engineering, is the first author on a recent Nature Communications&nbsp;<a href="https://www.nature.com/articles/s41467-022-35356-5" rel="noopener" target="_blank">paper</a> describing the unique landing gear that makes such perching possible. He built and tested it in collaboration with colleagues at the University of Seville, Spain, where the 700-gram ornithopter itself was developed as part of the European project <a href="https://griffin-erc-advanced-grant.eu/" rel="noopener" target="_blank">GRIFFIN</a>.&ldquo;This is the first phase of a larger project. Once an ornithopter can master landing autonomously on a tree branch, then it has the potential to carry out specific tasks, such as unobtrusively collecting biological samples or measurements from a tree. Eventually, it could even land on artificial structures, which could open up further areas of application,&rdquo; Zufferey says.He adds that the ability to land on a perch could provide a more efficient way for ornithopters &ndash; which, like many unmanned aerial vehicles (UAVs) have limited battery life &ndash; to recharge using solar energy, potentially making them ideal for long-range missions.&ldquo;This is a big step toward using flapping-wing robots, which as of now can really only do free flights, for manipulation tasks and other real-world applications,&rdquo; he says.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1671502227739-1440x836.jpg" style="width: 300px;" class="fr-fic fr-dib"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1671502199234-5472x3648.jpg" style="width: 300px;" class="fr-fic fr-dib">&copy; Raphael Zufferey&nbsp;Maximizing strength and precision; minimizing weight and speedThe engineering problems involved in landing an ornithopter on a perch without any external commands required managing many factors that nature has already so perfectly balanced. The ornithopter had to be able to slow down significantly as it perched, while still maintaining flight. The claw needed to be strong enough to grasp the perch and support the weight of the robot, without being so heavy that it could not be held aloft. &ldquo;That&rsquo;s one reason we went with a single claw rather than two,&rdquo; Zufferey notes. Finally, the robot needed to be able to perceive its environment and the perch in front of it in relation to its own position, speed, and trajectory.The researchers achieved all this by equipping the ornithopter with a fully on-board computer and navigation system, which was complemented by an external motion-capture system to help it determine its position. The ornithopter&rsquo;s leg-claw appendage was finely calibrated to compensate for the up-and-down oscillations of flight as it attempted to hone in on and grasp the perch. The claw itself was designed to absorb the robot&rsquo;s forward momentum upon impact, and to close quickly and firmly to support its weight. Once perched, the robot remains on the perch without energy expenditure.Even with all these factors to consider, Zufferey and his colleagues succeeded, ultimately building not just one but two claw-footed ornithopters to replicate their perching results.Looking ahead, Zufferey is already thinking about how their device could be expanded and improved, especially in an outdoor setting.&ldquo;At the moment, the flight experiments are carried out indoors, because we need to have a controlled flight zone with precise localization from the motion capture system. In the future, we would like to increase the robot&rsquo;s autonomy to perform perching and manipulation tasks outdoors in a more unpredictable environment.&rdquo;<hr>ReferencesZufferey, R., Tormo-Barbero, J., Feliu-Taleg&oacute;n, D. et al. How ornithopters can perch autonomously on a branch. Nat Commun 13, 7713 (2022). https://doi.org/10.1038/s41467-022-35356-5

EPFL researchers have developed a method that allows a flapping-wing robot to land autonomously on a horizontal perch using a claw-like mechanism. The innovation could significantly expand the scope of robot-assisted tasks.

Researchers develop winged robot that can land like a bird

If you&rsquo;ve ever played the claw game at an arcade, you know how hard it is to grab and hold onto objects using robotics grippers. Imagine how much more nerve-wracking that game would be if, instead of plush stuffed animals, you were trying to grab a fragile piece of endangered coral or a priceless artifact from a sunken ship.&nbsp;<iframe src="https://www.youtube.com/embed/SayuM8E_WaQ?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" style="width: 767px; height: 427px;"></iframe>Most of today&rsquo;s robotic grippers rely on embedded sensors, complex feedback loops, or advanced machine learning algorithms, combined with the skill of the operator, to grasp fragile or irregularly shaped objects. But researchers from the <a href="https://seas.harvard.edu/" target="_blank">Harvard John A. Paulson School of Engineering and Applied Sciences&nbsp;</a>(SEAS) have demonstrated an easier way.Taking inspiration from nature, they designed a new type of soft, robotic gripper that uses a collection of thin tentacles to entangle and ensnare objects, similar to how jellyfish collect stunned prey. Alone, individual tentacles, or filaments, are weak. But together, the collection of filaments can grasp and securely hold heavy and oddly shaped objects. The gripper relies on simple inflation to wrap around objects and doesn&rsquo;t require sensing, planning, or feedback control.&nbsp;The research was published in the <a href="https://www.pnas.org/doi/10.1073/pnas.2209819119" target="_blank">Proceedings of the National Academy of Sciences</a> (PNAS).&nbsp;&ldquo;With this research, we wanted to reimagine how we interact with objects,&rdquo; said Kaitlyn Becker, former graduate student and postdoctoral fellow at SEAS and first author of the paper. &ldquo;By taking advantage of the natural compliance of soft robotics and enhancing it with a compliant structure, we designed a gripper that is greater than the sum of its parts and a grasping strategy that can adapt to a range of complex objects with minimal planning and perception.&rdquo;&nbsp;Becker is currently an Assistant Professor of Mechanical Engineering at MIT.&nbsp;The gripper&rsquo;s strength and adaptability come from its ability to entangle itself with the object it is attempting to grasp. The foot-long filaments are hollow, rubber tubes. One side of the tube has thicker rubber than the other, so when the tube is pressurized, it curls like a pigtail or like straightened hair on a rainy day.&nbsp;The curls knot and entangle with each other and the object, with each entanglement increasing the strength of the hold. While the collective hold is strong, each contact is individually weak and won&rsquo;t damage even the most fragile object. To release the object, the filaments are simply depressurized.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjY2NzEyNjkwNTQ5LUdyaXBwZXJfQ2xvc2VVcC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">A close-up of the gripper&#39;s filaments wrapping around an object. (Credit: Harvard Microrobotics Lab/Harvard SEAS)&nbsp;The researchers used simulations and experiments to test the efficacy of the gripper, picking up a range of objects, including various houseplants and toys. The gripper could be used in real-world applications to grasp soft fruits and vegetables for agricultural production and distribution, delicate tissue in medical settings, even irregularly shaped objects in warehouses, such as glassware.&nbsp;This new approach to grasping combines&nbsp;<a href="https://www.seas.harvard.edu/person/l-mahadevan" target="_blank">Professor L. Mahadevan&rsquo;s</a> research on&nbsp;<a href="https://softmath.seas.harvard.edu/publication/topology-geometry-and-mechanics-of-strongly-stretched-and-twisted-filaments/" target="_blank">the topological mechanics of entangled filaments&nbsp;</a>with&nbsp;<a href="https://seas.harvard.edu/person/robert-wood" target="_blank">Professor Robert Wood&rsquo;s</a> research on&nbsp;<a href="https://www.seas.harvard.edu/news/2019/08/gentle-grip-gelatinous-creatures" target="_blank">soft robotic grippers</a>. &nbsp;&ldquo;Entanglement enables each highly compliant filament to conform locally with a target object leading to a secure but gentle topological grasp that is relatively independent of the details of the nature of the contact,&rdquo; said Mahadevan, the Lola England de Valpine Professor of Applied Mathematics in SEAS, and of Organismic and Evolutionary Biology, and Physics in FAS and co-corresponding author of the paper.&nbsp;&ldquo;This new approach to robotic grasping complements existing solutions by replacing simple, traditional grippers that require complex control strategies with extremely compliant, and morphologically complex filaments that can operate with very simple control,&rdquo; said Wood, the Harry Lewis and Marlyn McGrath Professor of Engineering and Applied Sciences and co-corresponding author of the paper. &ldquo;This approach expands the range of what&rsquo;s possible to pick up with robotic grippers.&rdquo;The research was co-authored by Clark Teeple, Nicholas Charles, Yeonsu Jung, Daniel Baum and James C. Weaver. It was supported in part by the Office of Naval Research, under grant N00014-17-1- 206 and the National Science Foundation under grants EFRI-1830901, DMR-1922321, DMR-2011754, DBI-1556164, and EFMA-1830901, the Simons Foundation, and the Henri Seydoux Fund.

The gripper wrapping around a succulent. (Credit: Harvard Microrobotics Lab/Harvard SEAS)

Jellyfish-like soft gripper mimics the mechanics of curly hair

Tentacle robot can gently grasp fragile objects

This article was first published on <a href="https://www.csail.mit.edu/news/flexible-way-grab-items-feeling" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;<hr>The notion of a large metallic robot that speaks in monotone and moves in lumbering, deliberate steps is somewhat hard to shake. But practitioners in the field of soft robotics have an entirely different image in mind &mdash; autonomous devices composed of compliant parts that are gentle to the touch, more closely resembling human fingers than R2-D2 or Robby the Robot.That model is now being pursued by Professor Edward Adelson and his Perceptual Science Group at MIT&rsquo;s Computer Science and Artificial Intelligence Laboratory (CSAIL). In a&nbsp;<a href="https://arxiv.org/abs/2204.07146" target="_blank">recent project</a>, Adelson and Sandra Liu &mdash; a mechanical engineering PhD student at CSAIL &mdash; have developed a robotic gripper using novel &ldquo;GelSight Fin Ray&rdquo; fingers that, like the human hand, is supple enough to manipulate objects. What sets this work apart from other efforts in the field is that Liu and Adelson have endowed their gripper with touch sensors that can meet or exceed the sensitivity of human skin.Their <a href="https://arxiv.org/abs/2204.07146" target="_blank">work was presented</a> last week at the 2022 IEEE 5th International Conference on Soft Robotics.<iframe src="https://www.youtube.com/embed/rvezSGdFPx0?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" style="width: 764px; height: 428px;"></iframe>The fin ray has become a popular item in soft robotics owing to a discovery made in 1997 by the German biologist Leif Kniese. He noticed that when he pushed against a fish&rsquo;s tail with his finger, the ray would bend toward the applied force, almost embracing his finger, rather than tilting away. The design has become popular, but it lacks tactile sensitivity. &ldquo;It&rsquo;s versatile because it can passively adapt to different shapes and therefore grasp a variety of objects,&rdquo; Liu explains. &ldquo;But in order to go beyond what others in the field had already done, we set out to incorporate a rich tactile sensor into our gripper.&rdquo;The gripper consists of two flexible fin ray fingers that conform to the shape of the object they come in contact with. The fingers themselves are assembled from flexible plastic materials made on a 3D printer, which is pretty standard in the field. However, the fingers typically used in soft robotic grippers have supportive cross-struts running through the length of their interiors, whereas Liu and Adelson hollowed out the interior region so they could create room for their camera and other sensory components.The camera is mounted to a semirigid backing on one end of the hollowed-out cavity, which is, itself, illuminated by LEDs. The camera faces a layer of &ldquo;sensory&rdquo; pads composed of silicone gel (known as &ldquo;GelSight&rdquo;) that is glued to a thin layer of acrylic material. The acrylic sheet, in turn, is attached to the plastic finger piece at the opposite end of the inner cavity. Upon touching an object, the finger will seamlessly fold around it, melding to the object&rsquo;s contours. By determining exactly how the silicone and acrylic sheets are deformed during this interaction, the camera &mdash; along with accompanying computational algorithms &mdash; can assess the general shape of the object, its surface roughness, its orientation in space, and the force being applied by (and imparted to) each finger.Liu and Adelson tested out their gripper in an experiment during which just one of the two fingers was &ldquo;sensorized.&rdquo; Their device successfully handled such items as a mini-screwdriver, a plastic strawberry, an acrylic paint tube, a Ball Mason jar, and a wine glass. While the gripper was holding the fake strawberry, for instance, the internal sensor was able to detect the &ldquo;seeds&rdquo; on its surface. The fingers grabbed the paint tube without squeezing so hard as to breach the container and spill its contents.The GelSight sensor could even make out the lettering on the Mason jar, and did so in a rather clever way. The overall shape of the jar was ascertained first by seeing how the acrylic sheet was bent when wrapped around it. That pattern was then subtracted, by a computer algorithm, from the deformation of the silicone pad, and what was left was the more subtle deformation due just to the letters.Glass objects are challenging for vision-based robots because of the refraction of the light. Tactile sensors are immune to such optical ambiguity. When the gripper picked up the wine glass, it could feel the orientation of the stem and could make sure the glass was pointing straight up before it was slowly lowered. When the base touched the tabletop, the gel pad sensed the contact. Proper placement occurred in seven out of 10 trials and, thankfully, no glass was harmed during the filming of this experiment.Wenzhen Yuan, an assistant professor in the Robotics Institute at Carnegie Mellon University who was not invovled with the research, says, &ldquo;Sensing with soft robots has been a big challenge, because it is difficult to set up sensors &mdash; which are traditionally rigid &mdash; on soft bodies,&rdquo; Yuan says. &ldquo;This paper provides a neat solution to that problem. The authors used a very smart design to make their vision-based sensor work for the compliant gripper, in this way generating very good results when robots grasp objects or interact with the external environment. The technology has lots of potential to be widely used for robotic grippers in real-world environments.&rdquo;Liu and Adelson can foresee many possible applications for the GelSight Fin Ray, but they are first contemplating some improvements. By hollowing out the finger to clear space for their sensory system, they introduced a structural instability, a tendency to twist, that they believe can be counteracted through better design. They want to make GelSight sensors that are compatible with soft robots devised by other research teams. And they also plan to develop a three-fingered gripper that could be useful in such tasks as picking up pieces of fruit and evaluating their ripeness.Tactile sensing, in their approach, is based on inexpensive components: a camera, some gel, and some LEDs. Liu hopes that with a technology like GelSight, &ldquo;it may be possible to come up with sensors that are both practical and affordable.&rdquo; That, at least, is one goal that she and others in the lab are striving toward.<hr>&quot;Reprinted with permission of&nbsp;<a href="https://www.csail.mit.edu/news/flexible-way-grab-items-feeling" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot;&nbsp;

In a recent project, Professor Edward Adelson and Sandra Liu — a mechanical engineering PhD student at CSAIL — have developed a robotic gripper using novel “GelSight Fin Ray” fingers that, like the human hand, is supple enough to manipulate objects.

A flexible way to grab items with feeling

<h2 id="isPasted">The Right Tool For The Job</h2>The end-of-arm tooling (EOAT) is one of the most important parts of your robot system. The EOAT spends the most time interacting directly with your parts and a well-designed tool/part interface can save both time and effort when programming a task that requires precision.There are different types of EOATs for different tasks. With its plug-and-play approach, the Forge/Ctrl makes it easy to attach and swap between a variety of EOATs, ensuring that you have the flexibility you need to optimize your task.Out of the box, Forge/OS supports several types of gripper EOATs:<ul><li>Electric and pneumatic multi-finger grippers</li><li>Pneumatically actuated magnetic grippers</li><li>Suction grippers</li></ul>Each of these types of grippers has its own benefits and drawbacks. For example, suction grippers come in a wide variety of sizes and material applications but usually require a flat and non-porous surface for best results. Magnetic grippers are very powerful but have limited material applications. Multi-finger grippers can provide precise placement but sometimes require extra fixturing for parts presentation. This document provides information to help you learn how each type of gripper can best be used.<h2>Electric and pneumatic multi-finger grippers</h2>Multi-finger grippers most commonly come in two types: two-finger grippers and three-finger grippers. Two-finger grippers (or parallel grippers) have two fingers that open and close along a single axis. Three-finger grippers (or centric grippers) have three fingers that open and close around a center point.Multi-finger grippers are ideal for tasks that require a lot of grip strength and repeatability is a major factor. Because these grippers are often large and require plenty of clearance for grabbing parts and introducing them to a machine, parts must be presented individually, like in a grid or auto-feeder, so the gripper can access a single part without colliding into other parts or objects.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1644539546005-pasted-image-0-7.webp" style="width: 766px;" class="fr-fic fr-dib">Both parallel and centric grippers often come with machineable or replaceable fingers, allowing you to customize the fingers to suit your task. Taking advantage of this customization capability can reduce the need for precise fixturing in part presentation. For example, you can machine the fingers to have a flat interior surface that presses against the top of the part. Using this surface in conjunction with a force sensor allows you to program a motion that picks up the parts by bottoming the part against the interior surface of the gripper finger, meaning parts of varying height will always be positioned in the same place in the gripper.<h2>Magnetic grippers</h2>Pneumatically actuated magnetic grippers provide superior strength and rigidity in applications containing iron (ferrous) materials. Magnetic grippers designed for EOAT applications often have a low-profile magnetic field, enabling the gripper to pick parts from a stack without disturbing any parts below the top of the stack.Despite the limited material applications of magnetic grippers, it’s worth taking advantage of their strength and repeatability. Magnetic grippers are good for applications where the robot arm may need to hold the part in place while it is being processed. They also work well in applications that seem suited for suction grippers but require more grip strength due to the high weight of ferrous materials.Pneumatically actuated magnetic grippers use an air supply to activate or deactivate the magnetic field by moving the magnet toward or away from the work surface of the gripper. Based on the manufacturer of the gripper, some magnetic grippers will be activated when air is applied, while others will be deactivated when air is applied.<h2>Suction grippers</h2>Suction grippers use the flow of air to create a negative pressure area inside of a suction cup. Based on the amount of airflow, suction can be increased or decreased. However, the versatility of suction gripping doesn’t come from the suction force but from the variety and flexibility of suction cups.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1644539564001-pasted-image-0-8.webp" style="width: 766px;" class="fr-fic fr-dib">Suction grippers are ideal for tasks where precision is not needed or else is handled by other means, such as gravity-feed trays or alternative alignment surfaces. Suction grippers are also great for applications where parts have unconventional geometries and require a custom EOAT.Many companies such as Piab and Schmalz make a wide array of suction cups for different applications. Cups may have a low-profile rubber interior for added grip against oily sheet metal or extra bellows and surface flexibility to increase contact against porous cardboard. Selecting the appropriate suction cup can mean the difference between a strong, repeatable grip and plenty of frustration with missed grabs.Because the Forge/Ctrl does not include an internal vacuum generator, the negative pressure inside a suction gripper is created through a generator on the EOAT. Generators can either be mounted directly above the suction cup or inline with the air supply. Direct generators are larger but generate more suction than inline generators. Inline generators enable much greater flexibility in designing custom gripping tools, such as low-profile gripping heads for tight spaces.

The end-of-arm tooling (EOAT) is one of the most important parts of your robot system. The EOAT spends the most time interacting directly with your parts and a well-designed tool/part interface can save both time and effort when programming a task that requires precision.

Automation 101: End-of-arm Tooling

In this episode, we talk about how starfish larvae inspired robots can revolutionize drug delivery and drones with flacon-like legs for landing. As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/b8a350ca-8fe0-4d5d-8b43-10464de4b1c5?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>&nbsp;<hr>This podcast is sponsored by <a href="https://www2.mouser.com/" id="isPasted" rel="nofollow" target="_blank">Mouser Electronics</a>. &nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjQwMDcyODU5NzI1LU1vdXNlci1zcG9uc29yLTEtYS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib"><hr><h3 id="isPasted">EPISODE NOTES</h3>(3:00) - Starfish-inspired Robot ⭐️🤖(11:30) - Drones With Bird-like Legs🚁🦅Episode 49 was brought to you by Mouser Electronics, Farbod &amp; Daniel’s favorite electronics distributor. Click <a href="https://www.youtube.com/watch?v=GWQRmPF7kpo" target="_blank">here</a> to watch the video of Grant Imahara at Carnegie Mellon University discussing biorobotics.<hr>About the podcast:Every day, some of the most innovative universities, companies, and individual technology developers share their knowledge on Wevolver. To ensure we can also provide this knowledge for the growing group of podcast listeners, we started a collaboration with two young engineers, Daniel Scott Mitchell &amp; Farbod Moghaddam who discuss the most interesting content in this podcast series.&nbsp;To learn more about this show, please visit the&nbsp;<a href="https://www.wevolver.com/profile/the.next.byte" target="_blank">shows page</a>. By following the page, you will get automatic updates by email when a new show is published.Be sure to give us a follow and review on&nbsp;<a href="https://podcasts.apple.com/nl/podcast/the-next-byte/id1548255861?l=en" rel="nofollow" target="_blank">Apple</a> podcasts,&nbsp;<a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" rel="nofollow" target="_blank">Spotify</a>, and most of your favorite podcast platforms!Take a few seconds to leave us a review. It really helps!&nbsp;<a href="https://apple.co/2RIsbZ2" rel="nofollow" target="_blank">https://apple.co/2RIsbZ2</a>If you do it and send us proof, we’ll give you a shoutout on the show.

In this episode, we talk about how starfish larvae inspired robots can revolutionize drug delivery and drones with flacon-like legs for landing. 

Podcast: Falcon Inspired Drones

The automation of a production line has become a major issue for many industries, and the need to increase productivity while having a good return on investment has caused many concerns. The main area in which these challenges are tackled is bin-picking.Moving a part from a container to place it to a specific position, ready for the next step, sounds very simple, but it becomes complex when you need to abide to cycle-time requirements.As demand for accurate, autonomous and flexible bin-picking systems increase, Visio Nerf has produced a handbook to the technology designed introduce key concepts and offer concrete examples of cutting-edge technology. Below are some of thehighlights from the handbook. The book guide can be downloaded <a href="https://content.visionerf.com/from-2d-to-3d-the-operating-principles-of-industrial-vision-systems-0?hsCtaTracking=59e2a72a-d663-450a-af5e-51d259af5ed1%7C0aa52600-51c0-4eb1-a0a5-d30cb78dc3a7" rel="noopener noreferrer" target="_blank">here.&nbsp;</a><h2>Introduction to Bin-Picking</h2>The automation of production cells is at the heart of current concerns for many industries, the main objective being to find the right balance between investment and improved productivity. One of the operations for which the evolution towards a high-performance automated production tool has become necessary is Bin-Picking.&nbsp;Bin-picking involves moving a part from its original position to a specific delivery zone to make it available for the next production step. The workpiece is initially placed in a container or on a conveyor in a random, semi or fully-ordered manner depending on the application. The main challenge of Bin-Picking is to offer an efficient automated solution to meet cycle time optimization requirements, to protect operators from difficult and repetitive tasks particularly in harsh environments while increasing the flexibility and productivity of the work cell. Technological advances in the field of machine vision and calculation software can now provide solutions adapted to most industrial applications.<h2>Essential criteria for assessing bin-picking</h2>In order to assess the efficiency of a Bin-Picking cell and determine its dimensioning, several criteria must be taken into account:&nbsp;<ul><li>Cell cycle time&nbsp;</li><li>Bin emptying rate&nbsp;</li><li>Part pick and place accuracy</li></ul>These elements aim to measure and improve, more or less directly, the profitability of the cell at different times of its cycle life. A fourth factor to consider, that is too often overlooked, is the flexibility of the cell or its ability to be reused in several applications.With Industry 4.0, companies are looking for agile cells that can be reused without too many modifications when the type of the parts to be picked changes. These four criteria are intrinsically linked. For example, a suboptimal emptying rate may require the addition of mechanical systems to shake the bin or a change to more appropriate tools to complete the task. Integration of such elements extends the cycle time and penalizes the flexibility of the cell. Appropriate bin-picking cell sizing is essential and involves finding the right balance between these criteria. &nbsp;To read more about the technology behind bin-picking, download the full guide, written by Adib Rebbouh with Visio-Nerf&nbsp;<a href="https://content.visionerf.com/from-2d-to-3d-the-operating-principles-of-industrial-vision-systems-0?hsCtaTracking=59e2a72a-d663-450a-af5e-51d259af5ed1%7C0aa52600-51c0-4eb1-a0a5-d30cb78dc3a7" rel="noopener noreferrer" target="_blank">here</a>.&nbsp;

robotic hands & grippers

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