swarm robots/multi-robot systems

The wind is not just 'wind' – but a complicated arrangement of turbulent features that are influenced by the surrounding environment. Air turbulence is created by the landscape, but also by buildings, roads and wind turbines. In the ESTABLIS-UAS project, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) is researching these flow effects. For this purpose, a swarm of drones ascends and measures these phenomena. The results can be used, for example, to improve the arrangement of turbines in a wind farm."Wind turbines are among the most important technologies for sustainable energy supply, both in Germany and worldwide. The further expansion of wind energy requires a significant increase in the performance of wind turbines. For this reason, the DLR Executive Board has decided to increase the annual funding for wind energy research by one million euros with immediate effect," explains Anke Kaysser-Pyzalla, Chair of the DLR Executive Board. "DLR has been conducting research in this area for many years. Its activities make use of the many synergies with aerospace research. The work ranges from fundamental research to the further development of individual components in cooperation with industry.""Understanding these turbulent, three-dimensional conditions plays an important role in the energy transition. This allows us to comprehend the loads to which wind turbines will be subjected during their lifecycle and to predict how much power they will feed into the energy grid," says project leader Norman Wildmann from DLR's <a href="https://www.dlr.de/content/en/institutes/institute-of-atmospheric-physics.html" target="_blank" title="Institute of Atmospheric Physics">Institute of Atmospheric Physics</a>. Up to 100 drones take off from the ground in a fixed formation for the ESTABLIS-UAS (Exposing spatio-temporal Structures of Turbulence in the Atmospheric Boundary Layer with In-Situ measurements by a fleet of Unmanned Aerial Systems) project. The Unmanned Aerial Systems (UAS) measure wind characteristics, temperature and humidity with high resolution. Tests were carried out in advance with up to 20 of these small drones. They are particularly robust so that they can maintain their position and deliver results even at higher wind speeds.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1647581507407-drones-in-takeoff-position.jpg" style="width: 766px;" class="fr-fic fr-dib">Drones in takeoff position. Credit: DLR (CC BY-NC-ND 3.0)<h2>Tests in a wind tunnel and at the Wind Energy Research Farm</h2>Wind turbines generate their own turbulent effects in addition to the flow phenomena that already exist. As such, one goal of wind energy research at DLR is to develop a model that clearly shows the effects on the turbines in the second or third rows of a wind farm. "There is still some need for optimisation here. The answers to questions about how the wind behaves at these points are very complex," explains Wildmann. "And this is not only dependent on the turbine, but also on local atmospheric conditions and the properties of the surrounding terrain. It is about a combination of the two."In addition to measurements on wind turbines, experiments are planned in the wind tunnel at the <a href="https://uol.de/en/" target="_blank" title="External link University of Oldenburg">University of Oldenburg</a>, which is a partner in the Research Alliance Wind Energy (Forschungsverbund Windenergie; <a href="https://www.fvwe.uni-oldenburg.de/en" target="_blank" title="External link Research Alliance Wind Energy">FVWE</a>), and at the <a href="https://windenergy-researchfarm.com/" target="_blank" title="External link DLR - Krummendeich Research Wind Farm WiValdi">DLR Wind Energy Research Farm in Krummendeich</a>. Two further measurement campaigns will take place as part of the international <a href="http://www.teamx-programme.org/" target="_blank" title="External link TEAMx">TEAMx</a> initiative, which is dedicated to the study of complex flows in the boundary layer over mountainous terrain. All experiments will be supplemented by numerical simulations. Ultimately, a comprehensive model for the representation of turbulent flows will be created.<h2>Models of the atmospheric boundary layer complement knowledge from remote sensing</h2>The lowest layer of the atmosphere – the 'Atmospheric Boundary Layer' (ABL) – is directly influenced by Earth’s surface. Exchange and transport processes in the ABL are mainly driven by turbulence, which extends over a large range of scales; some turbulent phenomena are a few millimetres across, others are over a kilometre in size. Physical models for the ABL, which extends from the ground to an altitude of approximately 2000 metres, are not yet very accurate. Turbulence from interconnected features such as gusts, slope and valley winds, cities, wind turbines or aircraft are difficult to capture. "The ESTABLIS-UAS measurements fill an observational gap between very small, local processes near the ground and large-scale observations by remote sensing, research aircraft and satellites," says Markus Rapp, Director of the DLR Institute of Atmospheric Physics in <a href="https://www.dlr.de/content/en/sites/oberpfaffenhofen.html" target="_blank" title="Oberpfaffenhofen">Oberpfaffenhofen</a>. "Combining this with ground-based sensors and remote sensing enables completely new insights into the interaction of complex flow phenomena." These models could then also explain how turbulent flows influence the mixing of the lower atmosphere. This is important, for example, in the dispersion of dust, pollutants and aerosols.

Measurements at a wind turbine. Credit: DLR (CC BY-NC-ND 3.0)

The research objective is to accurately represent the three-dimensional flows and turbulence in the lowest layer of the atmosphere.

DLR measures flow phenomena around wind turbines with a swarm of drones

In this episode, we talk about how robotic systems are being leveraged to assist farmers in streamlining their operations. As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/3140758b-6aa9-46a5-829f-e13f372ed163?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> <hr>This podcast is sponsored by <a href="https://www2.mouser.com/" id="isPasted" rel="nofollow" target="_blank" style="border: 5px solid orange;">Mouser Electronics (EXTERNAL)</a>.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjM4ODY2NDQxODQ5LU1vdXNlci1zcG9uc29yLTEtYS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">&nbsp;<hr><h3 id="isPasted">EPISODE NOTES</h3>(1:41) - Adaptive Swarm Robotics Could Revolutionize Agriculture 🚜🤖 (BAD)(12:11) -&nbsp;<a href="https://www.wevolver.com/article/autonomous-agricultural-robots-revolutionize-organic-farming" target="_blank" style="border: 5px solid green;">Autonomous Agricultural Robots Revolutionize Organic Farming 🌾🚜 (GOOD)</a>Episode 47 was brought to you by Mouser Electronics, Farbod &amp; Daniel’s favorite electronics distributor. Click&nbsp;<a href="https://www.mouser.com/blog/robotic-irrigation-reduces-farming-costs" target="_blank" style="border: 5px solid orange;">here&nbsp; (EXTERNAL)</a>to read about how robotic systems can be leveraged in farms to optimize their operation.<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" style="border: 5px solid green;">shows page (GOOD)</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" style="border: 5px solid orange;">Apple (EXTERNAL)</a> podcasts,&nbsp;<a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" rel="nofollow" target="_blank" style="border: 5px solid orange;">Spotify (EXTERNAL)</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" style="border: 5px solid orange;">https://apple.co/2RIsbZ2 (EXTERNAL)</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 robotic systems are being leveraged to assist farmers in streamlining their operations.

Podcast: How Robotic Farming Will Make Produce Better & Cheaper

There is strength in numbers. That’s true not only for humans, but for drones too. By flying in a swarm, they can cover larger areas and collect a wider range of data, since each drone can be equipped with different sensors.<iframe src="https://www.youtube.com/embed/cAXUKNGpMG4?&amp;wmode=opaque&amp;rel=0" frameborder="0" allowfullscreen="" class="fr-draggable" style="width: 787px; height: 445px;"></iframe><h2>Preventing drones from bumping into each other</h2>One reason why drone swarms haven’t been used more widely is the risk of gridlock within the swarm. Studies on the collective movement of animals show that each agent tends to coordinate its movements with the others, adjusting its trajectory so as to keep a safe inter-agent distance or to travel in alignment, for example.“In a drone swarm, when one drone changes its trajectory to avoid an obstacle, its neighbors automatically synchronize their movements accordingly,” says Dario Floreano, a professor at EPFL’s School of Engineering and head of the <a href="https://www.epfl.ch/labs/lis/" target="_blank">Laboratory of Intelligent Systems (LIS)</a>. “But that often causes the swarm to slow down, generates gridlock within the swarm or even leads to collisions.”<div class="fr-img-space-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1621898710271-5427x3618.jpg" style="width: 788px;" class="fr-fic fr-dib">Drones flying in a swarm © Alain Herzog / 2021 EPFL&nbsp;<h2 class="fr-img-space-wrap2">Not just reacting, but also predicting</h2></div>Enrica Soria, a PhD student at LIS, has come up with a new method for getting around that problem. She has developed a predictive control model that allows drones to not just react to others in a swarm, but also to anticipate their own movements and predict those of their neighbors. “Our model gives drones the ability to determine when a neighbor is about to slow down, meaning the slowdown has less of an effect on their own flight,” says Soria. The model works by programing in locally controlled, simple rules, such as a minimum inter-agent distance to maintain, a set velocity to keep, or a specific direction to follow. Soria’s work has just been published in <a href="https://www.nature.com/articles/s42256-021-00341-y" rel="noopener" target="_blank">Nature Machine Intelligence</a>.With Soria’s model, drones are much less dependent on commands issued by a central computer. Drones in aerial light shows, for example, get their instructions from a computer that calculates each one’s trajectory to avoid a collision. “But with our model, drones are commanded using local information and can modify their trajectories autonomously,” says Soria.<div class="fr-img-space-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1621898773720-6720x4480+%281%29.jpg" style="width: 788px;" class="fr-fic fr-dib">Drones flying in a swarm © Alain Herzog / 2021 EPFL&nbsp;<h2 class="fr-img-space-wrap2">A model inspired by nature</h2></div>Tests run at LIS show that Soria’s system improves the speed, order and safety of drone swarms in areas with a lot of obstacles. “We don’t yet know if, or to what extent, animals are able to predict the movements of those around them,” says Floreano. “But biologists have recently suggested that the synchronized direction changes observed in some large groups would require a more sophisticated cognitive ability than what has been believed until now.”<hr>ReferencesSoria, E., Schiano, F. &amp; Floreano, D. Predictive control of aerial swarms in cluttered environments. Nat Mach Intell (2021). <a href="https://doi.org/10.1038/s42256-021-00341-y" rel="noopener" target="_blank">DOI:&nbsp;10.1038/s42256-021-00341-y</a>

Drones flying in a swarm © Alain Herzog / 2021 EPFL

Engineers at EPFL have developed a predictive control model that allows swarms of drones to fly in cluttered environments quickly and safely. It works by enabling individual drones to predict their own behavior and that of their neighbors in the swarm.

Helping drone swarms avoid obstacles without hitting each other

This article answers the practical question: how does SPI work, detailing the theory behind the bus, the role of each signal, timing modes, architectures and topologies, and practical design considerations.

How Does SPI Work? A Comprehensive Guide for Digital and Hardware Engineers

The Serial Peripheral Interface (SPI) protocol is a critical communication standard for high-speed data exchange in embedded systems. This article discusses its architecture, advancements, and practical applications in engineering to help engineers optimize and overcome design challenges. 

SPI Protocol: Revolutionizing Data Communication in Embedded Systems

Animals like bats, whales and insects have long used acoustic signals for communication and navigation. Now, an international team of scientists have taken a page from nature's playbook to model micro-sized robots that use sound waves to coordinate into large swarms that exhibit intelligent-like behavior. The robot groups could one day carry out complex tasks like exploring disaster zones, cleaning up pollution, or performing medical treatments from inside the body, according to team lead Igor Aronson, Huck Chair Professor of Biomedical Engineering, Chemistry, and Mathematics at Penn State.“Picture swarms of bees or midges,” Aronson said. “They move, that creates sound, and the sound keeps them cohesive, many individuals acting as one.”The researchers published their work today (Aug. 12) in the journal <a href="https://journals.aps.org/prx/abstract/10.1103/m1hl-d18s">Physical Review X</a>.Since the miniature, sound-broadcasting swarms of micromachines are self-organizing, they can navigate tight spaces and even re-form themselves if deformed. The swarms’ collective — or emergent — intelligence could one day be harnessed to carry out tasks like cleaning up pollution in contaminated environments, Aronson explained.<iframe width="640" height="360" src="https://www.youtube.com/embed/45Ysoiup4Ro?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0"></iframe>Beyond the environment, the robot swarms could potentially work inside the body, delivering drugs directly to a problem area, for example. Their collective sensing also helps in detecting changes in surroundings, and their ability to “self-heal” means they can keep functioning as a collective unit even after breaking apart, which could be especially useful for threat detection and sensor applications, Aronson said.“This represents a significant leap toward creating smarter, more resilient and, ultimately, more useful microrobots with minimal complexity that could tackle some of our world's toughest problems,” he said. “The insights from this research are crucial for designing the next generation of microrobots, capable of performing complex tasks and responding to external cues in challenging environments.”For the study, the team developed a computer model to track the movements of tiny robots, each equipped with an acoustic emitter and a detector. They found that acoustic communication allowed the individual robotic agents to work together seamlessly, adapting their shape and behavior to their environment, much like a school of fish or a flock of birds.While the robots in the paper were computational agents within a theoretical — or agent-based — model, rather than physical devices that were manufactured, the simulations observed the emergence of collective intelligence that would likely appear in any experimental study with the same design, Aronson said.“We never expected our models to show such a high level of cohesion and intelligence from such simple robots,” Aronson said. “These are very simple electronic circuits. Each robot can move along in some direction, has a motor, a tiny microphone, speaker and an oscillator. That’s it, but nonetheless it’s capable of collective intelligence. It synchronizes its own oscillator to the frequency of the swarm’s acoustic field and migrates toward the strongest signal.”The discovery marks a new milestone for a budding field called active matter, the study of the collective behavior of self-propelled microscopic biological and synthetic agents, from swarms of bacteria or living cells to microrobots. It shows for the first time that sound waves can function as a means of controlling the micro-sized robots, Aronson explained. Up until now, active matter particles have been controlled predominantly through chemical signaling.“Acoustic waves work much better for communication than chemical signaling,” Aronson said. “Sound waves propagate faster and farther almost without loss of energy — and the design is much simpler. The robots effectively ‘hear’ and ‘find’ each other, leading to collective self-organization. Each element is very simple. The collective intelligence and functionality arise from minimal ingredients and simple acoustic communication.”The other authors on the paper are Alexander Ziepke, Ivan Maryshev and Erwin Frey of the Ludwig Maximilian University of Munich. The John Templeton Foundation funded the research.

A new study led by Penn State researchers shows for the first time how sound waves could function as a means of controlling micro-sized robots. Credit: Igor Aronson / Penn State. Creative Commons

Microrobots modeled in simulations communicate via sound to form 'acoustic swarms' and move collectively

Tiny robots use sound to self-organize into intelligent groups

Cyril Monette is fascinated by collective behavior. Having studied how insects like cockroaches make collective decisions, the third year doctoral student in the <a href="https://www.epfl.ch/labs/mobots/">EPFL’s Mobots Laboratory</a> is now turning his attention towards the very useful, and much less repulsive insect: the honeybee. And he’s doing so with the help of robotics.The bees inside of a hive naturally cluster into a volume the shape of a ball, at the intersection of the multiple frames of honeycomb inside of the hive. This ball-shaped configuration is likely optimal for the bees, yet observation hives developed by the scientific community study only one or two isolated frames at a time, forcing bees to occupy a flat disk instead.“The isolated observation hive was built that way to allow for automated observation thanks to cameras or direct observation,” explains Monette who would like to offer a way to study bee behavior in relation to honey stores but in an environment that respects their instincts for clustering in space. “Unless we provide an environment that allows the bees to behave naturally, the observations yielded can only approximate how intact and strong colonies behave.”<iframe width="640" height="360" src="https://www.youtube.com/embed/AURyl6eYbfY?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0"></iframe> Using the thermal properties of honeyTo do away with cameras altogether, researchers from Mobots have developed a thermal robotic beehive frame upon which bees can build honeycomb. The robotic frame consists of 64 temperature sensors that can measure the temperature across 10 distinct regions that can be heated up separately, and since the bees build honeycomb on both sides of the frame, that corresponds to 20 honeycomb patches per frame where bees can potentially store honey. The idea is to first locate where the honey stores are and evaluate the amount of honey present in those 10 regions over time, and then to study bee behavior in relation to that.“We’re interested in studying the relation between bee movement, their lifecycles and correlations with honey location within the hive over time. Our first challenge has been to accurately measure the amount of honey per region, in the absence of living bees, which we successfully achieved thanks to the thermal properties of honey,” explains Monette.Because of honey’s particular thermal properties, it heats up and cools down differently compared to empty honeycomb. By sending a pulse of heat at increments of either +1, +3 or +5 degrees Celsius, the researchers are able to characterize how a region of honeycomb full of honey reacts thermally and therefore deduce the amount of honey per region. Specifically, the volume of honey is modeled according to the heating and cooling times combined with a measure of heating dynamics. These robotic frames can be combined in a hive to map the entirety of a colony’s honey stocks.Other ecological studies“By combining ethology and robotics we can make unprecedent observations of bees in nearly fully natural conditions, revealing behaviors never observed with this precision. This allows us to challenge and improve existing hypotheses on honeybee behavior and at the same time learn techniques that can help us to protect bees,” says Francesco Mondada who leads the Mobots Laboratory. “Moreover, combining these elements with our expertise in education allows us to share these observations to a broader audience – sparking curiosity about these fascinating, lesser-known behaviors that showcase the complexity of social insects’ life.”Monette and colleagues are currently conducting experiments with their robotic observation hive to study bee behavior, testing various hypotheses in situ. “I’m intrigued by bee ethology, like the way the ball of bees expresses circadian rhythms by expanding and contracting throughout the day,” explains Monette. “With our robotic observation hive, I’m hoping to map honey resources and colony demographics over time, observe how the bee ball moves throughout the winter, and provide a framework for other ecological studies such as studying the impact of heatwaves on colonies.” <hr />ReferencesConference paper: <a href="https://ieeexplore.ieee.org/abstract/document/10843927" target="_blank" rel="noopener">https://ieeexplore.ieee.org/abstract/document/10843927</a>

EPFL researchers are developing robotic beehive frames that help locate honey stores inside of beehives over time, without relying on cameras. The aim is to develop new observation tools to study honeybee behavior that better fit the bees' natural way to occupy space compared to current methods.

Studying collective bee behavior thanks to robotics

Blackworms inspire collective behaviors for new robotic platform

What a Tangled Robot We Weave

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Tiny robots use sound to self-organize into intelligent groups

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Latest Posts

DLR measures flow phenomena around wind turbines with a swarm of drones

Podcast: How Robotic Farming Will Make Produce Better & Cheaper

Helping drone swarms avoid obstacles without hitting each other