In this episode, we interview Edward Mehr, CEO of Machina Labs, the ambitious startup that's transforming the manufacturing scene one robot at a time.<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/45a10a6a-073a-4a4d-861e-e7899ef8cd50?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><h3 id="isPasted">EPISODE NOTES</h3>LINKS:<a href="https://www.wevolver.com/newsletter-subscription/the.next.byte" target="_blank">Sign up</a> to receive The Next Byte's email newsletter!Listen to&nbsp;<a href="https://pod.link/wevolver/episode/26ec14ba9119f58a6be526ef075c4708" target="_blank">Episode 118</a> - where we first covered MachinaCheck out&nbsp;<a href="http://machinalabs.ai/careers" target="_blank">Machina's careers page</a>&nbsp;--TIMESTAMPS;(01:25) - how Ed’s nontraditional education equipped him to build startups(08:29) - manufacturing challenges and the beginnings of agile manufacturing(14:40) - why 3D printing was successful in the aerospace industry(20:29) - using agile manufacturing to disrupt new industries(23:44) - the “secret sauce” behind Machina’s technology(34:31) - why AI robots will revolutionize sheet metal forming(44:46) - the impact of next-gen AI-enabled robotic craftsmen(50:21) - the future of Machina and Ed’s role as CEO(60:28) - Ed’s advice for aspiring founders &amp; technologists<hr id="isPasted">As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.To learn more about this show, please visit our<a href="https://www.wevolver.com/profile/the.next.byte" target="_blank">&nbsp;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<a href="https://podcasts.apple.com/nl/podcast/the-next-byte/id1548255861?l=en" target="_blank" rel="nofollow">&nbsp;Apple</a> podcasts,<a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" target="_blank" rel="nofollow">&nbsp;Spotify</a>, and most of your favorite podcast platforms!--The Next Byte: We're two engineers on a mission to simplify complex science &amp; technology, making it easy to understand. In each episode of our show, we dive into world-changing tech (such as AI, robotics, 3D printing, IoT, &amp; much more), all while keeping it entertaining &amp; engaging along the way.

In this episode, we interview Edward Mehr, CEO of Machina Labs, the ambitious startup that's transforming the manufacturing scene one robot at a time.

Podcast: Ed Mehr & Machina's Robot Army Transform Manufacturing (Interview)

Imagine a world with precision medicine, where a swarm of microrobots delivers a payload of medicine directly to ailing cells. Or one where aerial or marine drones can collectively survey an area while exchanging minimal information about their location.One early step towards realizing such technologies is being able to simultaneously simulate swarming behaviors and synchronized timing &ndash; behaviors found in slime molds, sperm and fireflies, for example.In 2014, Cornell researchers first introduced a simple model of swarmalators &ndash; short for &lsquo;swarming oscillator&rsquo; &ndash; where particles self-organize to synchronize in both time and space. In the study, &quot;<a href="https://www.nature.com/articles/s41467-023-36563-4" target="_blank">Diverse Behaviors in Non-uniform Chiral and Non-chiral Swarmalators</a>,&rdquo; which published Feb. 20 in the journal Nature Communications, they expanded this model to make it more useful for engineering microrobots, better understand existing, observed biological behaviors, and for theoreticians to experiment in this field.&ldquo;We wanted a simple mathematical model that can lay the foundation for swarmalators in general, something that captures all of the complex emergent phenomena we see in natural and engineered swarms,&rdquo; said Kirstin Petersen, the paper&rsquo;s senior author, assistant professor and an Aref and Manon Lahham Faculty Fellow in the Department of Electrical and Computer Engineering in Cornell Engineering.Steven Ceron, Ph.D. &lsquo;22, a former graduate student in Petersen&rsquo;s lab, is the paper&rsquo;s first author, and Kevin O&rsquo;Keeffe Ph.D. &lsquo;17, a former graduate student in applied mathematics, is a co-author. &nbsp; &nbsp;O&rsquo;Keeffe compared this model to the largest doll in a set of Russian dolls, with each smaller doll representing models capable of simulating more refined behaviors. &ldquo;We&rsquo;ve tried to come up with a model that is as simple as possible in the hope of capturing generic phenomena,&rdquo; he said.The researchers simplified their model to work with just four mathematical constants linked together to produce diverse emergent behaviors, such as aggregation, dispersion, vortices, traveling waves, and bouncing clusters.The new model can mimic particles in nature that each operate at different natural frequencies, as some objects move slower and faster around a trajectory than others. The researchers also added chirality, or the ability for a particle to move in a circle, because many examples in nature, such as sperm, swim in circles and in vortices. And particles in the model exhibit local coupling, so they sense and respond only to their local neighbors.At its core, the model combines swarming behaviors with synchronization in time. Examples of swarming from nature include flocking birds or herds of stampeding buffalo, where individuals move together as a group. Synchronized timing can be found in cardiac pacemaker cells that fire an electric impulse in unison, shocking the heart into regular repeated beats. Sperm represent both phenomena together, as they can beat their tails in unison while swimming as a group. Fireflies are also known to fly in swarms while flashing in synchrony.&ldquo;That&rsquo;s what makes them swarmalators, because there&rsquo;s two of the self-organizing forces going on at the same time,&rdquo; O&rsquo;Keeffe said.The model doesn&#39;t try to model a specific real world swarmalator, such as sperm, robotic drones, or magnetic domain walls. Rather, it tries to model the behavior common to all those systems &shy;&ndash; it aims for generality, rather than specificity. As an example, the model was shown to reproduce behaviors found in microbial slime molds, which can operate as individual cells, but when starved will aggregate into a slug and eventually a fruiting body.&ldquo;These very simple coupled mechanisms can potentially be implemented on swarms of tiny robots with very limited power, computational and memory resources, which in spite of their individual simplicity can work together to produce the complex swarming behaviors we predict in our model,&rdquo; Petersen said.&nbsp;One future application could be for precision medicine, where tiny magnetized insoluble particles could swarm and be synchronized in relation to each other and then controlled to deliver a payload to tissues in need of a therapy, O&rsquo;Keeffe said.

Kirstin Petersen, assistant professor of electrical and computer engineering, and colleagues, have developed a simple model called a swarmalator, where particles self-organize to synchronize in both time and space. Credit: Noël Heaney/Cornell University

Imagine a world with precision medicine, where a swarm of microrobots delivers a payload of medicine directly to ailing cells. Or one where aerial or marine drones can collectively survey an area while exchanging minimal information about their location.

'Swarmalators' better envision synchronized microbots

In this episode, we talk about about how an ant inspired robotics platform could be the future of swarm robotics due to its simple, affordable, flexible, and scalable nature.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/fdb515e5-6c75-45e8-be46-421484ee763f?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">Mouser Electronics</a>. &nbsp; &nbsp;&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjcyNzM2MjkwOTU5LU1vdXNlci1zcG9uc29yLTEtYS5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib"><hr><h3 id="isPasted">EPISODE NOTES</h3>(5:30) - The Physical Intelligence of Ant And Robot CollectivesThis episode was brought to you by Mouser, our favorite place to get electronic components for any project. Click <a href="https://resources.mouser.com/automotive/the-connected-car-addresses-congestion-and-safety-challenges" target="_blank">HERE</a> to read the article about how connected cars and a smart transportation infrastructure could be the cure for congested highways!<hr>As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.To learn more about this show, please visit our&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>,&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!

In this episode, we talk about about how an ant inspired robotics platform could be the future of swarm robotics due to its simple, affordable, flexible, and scalable nature.

Podcast: Ant Inspired Swarm Robots

This article was first published on <a href="https://news.mit.edu/2022/assembler-robots-structures-voxels-1122" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;<hr>Researchers at MIT have made significant steps toward creating robots that could practically and economically assemble nearly anything, including things much larger than themselves, from vehicles to buildings to larger robots.The new work, from MIT&rsquo;s Center for Bits and Atoms (CBA), builds on years of research, including recent studies demonstrating that objects such as a deformable airplane wing and a functional racing car could be assembled from tiny identical lightweight pieces &mdash; and that robotic devices could be built to carry out some of this assembly work. Now, the team has shown that both the assembler bots and the components of the structure being built can all be made of the same subunits, and the robots can move independently in large numbers to accomplish large-scale assemblies quickly.The new work is reported in the journal Nature Communications Engineering, in a <a href="https://www.nature.com/articles/s44172-022-00034-3" target="_blank">paper</a> by CBA doctoral student Amira Abdel-Rahman, Professor and CBA Director Neil Gershenfeld, and three others.<iframe src="https://www.youtube.com/embed/G94FDMGLwCc?&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: 430px;"></iframe>A fully autonomous self-replicating robot assembly system capable of both assembling larger structures, including larger robots, and planning the best construction sequence is still years away, Gershenfeld says. But the new work makes important strides toward that goal, including working out the complex tasks of when to build more robots and how big to make them, as well as how to organize swarms of bots of different sizes to build a structure efficiently without crashing into each other.As in previous experiments, the new system involves large, usable structures built from an array of tiny identical subunits called voxels (the volumetric equivalent of a 2-D pixel). But while earlier voxels were purely mechanical structural pieces, the team has now developed complex voxels that each can carry both power and data from one unit to the next. This could enable the building of structures that can not only bear loads but also carry out work, such as lifting, moving and manipulating materials &mdash; including the voxels themselves.&ldquo;When we&rsquo;re building these structures, you have to build in intelligence,&rdquo; Gershenfeld says. While earlier versions of assembler bots were connected by bundles of wires to their power source and control systems, &ldquo;what emerged was the idea of structural electronics &mdash; of making voxels that transmit power and data as well as force.&rdquo; Looking at the new system in operation, he points out, &ldquo;There&rsquo;s no wires. There&rsquo;s just the structure.&rdquo;The robots themselves consist of a string of several voxels joined end-to-end. These can grab another voxel using attachment points on one end, then move inchworm-like to the desired position, where the voxel can be attached to the growing structure and released there.Gershenfeld explains that while the earlier system demonstrated by members of his group could in principle build arbitrarily large structures, as the size of those structures reached a certain point in relation to the size of the assembler robot, the process would become increasingly inefficient because of the ever-longer paths each bot would have to travel to bring each piece to its destination. At that point, with the new system, the bots could decide it was time to build a larger version of themselves that could reach longer distances and reduce the travel time. An even bigger structure might require yet another such step, with the new larger robots creating yet larger ones, while parts of a structure that include lots of fine detail may require more of the smallest robots.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1669171695142-output.gif" style="width: 766px;" class="fr-fic fr-dib">Credit: Amira Abdel-Rahman/MIT Center for Bits and Atoms&nbsp;As these robotic devices work on assembling something, Abdel-Rahman says, they face choices at every step along the way: &ldquo;It could build a structure, or it could build another robot of the same size, or it could build a bigger robot.&rdquo; Part of the work the researchers have been focusing on is creating the algorithms for such decision-making.&ldquo;For example, if you want to build a cone or a half-sphere,&rdquo; she says, &ldquo;how do you start the path planning, and how do you divide this shape&rdquo; into different areas that different bots can work on? The software they developed allows someone to input a shape and get an output that shows where to place the first block, and each one after that, based on the distances that need to be traversed.There are thousands of papers published on route-planning for robots, Gershenfeld says. &ldquo;But the step after that, of the robot having to make the decision to build another robot or a different kind of robot &mdash; that&rsquo;s new. There&rsquo;s really nothing prior on that.&rdquo;While the experimental system can carry out the assembly and includes the power and data links, in the current versions the connectors between the tiny subunits are not strong enough to bear the necessary loads. The team, including graduate student Miana Smith, is now focusing on developing stronger connectors. &ldquo;These robots can walk and can place parts,&rdquo; Gershenfeld says, &ldquo;but we are almost &mdash;&nbsp;but not quite &mdash; at the point where one of these robots makes another one and it walks away. And that&rsquo;s down to fine-tuning of things, like the force of actuators and the strength of joints. &hellip; But it&rsquo;s far enough along that these are the parts that will lead to it.&rdquo;Ultimately, such systems might be used to construct a wide variety of large, high-value structures. For example, currently the way airplanes are built involves huge factories with gantries much larger than the components they build, and then &ldquo;when you make a jumbo jet, you need jumbo jets to carry the parts of the jumbo jet to make it,&rdquo; Gershenfeld says. With a system like this built up from tiny components assembled by tiny robots, &ldquo;The final assembly of the airplane is the only assembly.&rdquo;Similarly, in producing a new car, &ldquo;you can spend a year on tooling&rdquo; before the first car gets actually built, he says. The new system would bypass that whole process. Such potential efficiencies are why Gershenfeld and his students have been working closely with car companies, aviation companies, and NASA. But even the relatively low-tech building construction industry could potentially also benefit.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1669171859295-MIT-SwarmRobot-02-press.jpg" style="width: 766px;" class="fr-fic fr-dib">The new study shows that both the assembler bots and the components of the structure being built can all be made of the same subunits, and the robots can move independently in large numbers to accomplish large-scale assemblies quickly. Courtesy of the researchersWhile there has been increasing interest in 3-D-printed houses, today those require printing machinery as large or larger than the house being built. Again, the potential for such structures to instead be assembled by swarms of tiny robots could provide benefits. And the Defense Advanced Research Projects Agency is also interested in the work for the possibility of building structures for coastal protection against erosion and sea level rise.Aaron Becker, an associate professor of electrical and computer engineering at the University of Houston, who was not associated with this research, calls this paper &ldquo;a home run &mdash; [offering] an innovative hardware system, a new way to think about scaling a swarm, and rigorous algorithms.&rdquo;Becker adds: &ldquo;This paper examines a critical area of reconfigurable systems: how to quickly scale up a robotic workforce and use it to efficiently assemble materials into a desired structure. &hellip; This is the first work I&rsquo;ve seen that attacks the problem from a radically new perspective &mdash; using a raw set of robot parts to build a suite of robots whose sizes are optimized to build the desired structure (and other robots) as fast as possible.&rdquo;The research team also included MIT-CBA student Benjamin Jenett and Christopher Cameron, who is now at the U.S. Army Research Laboratory. The work was supported by NASA, the U.S. Army Research Laboratory, and CBA consortia funding.<hr>&quot;Reprinted with permission of <a href="https://news.mit.edu/2022/assembler-robots-structures-voxels-1122" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot;&nbsp;

Researchers at MIT have made significant steps toward creating robots that could practically and economically assemble nearly anything, including things much larger than themselves, from vehicles to buildings to larger robots. The new system involves large, usable structures built from an array of tiny identical subunits called voxels (the volumetric equivalent of a 2-D pixel). Courtesy of the researchers

Researchers make progress toward groups of robots that could build almost anything, including buildings, vehicles, and even bigger robots.

Flocks of assembler robots show potential for making larger structures

This article was first published on&nbsp;<a href="https://www.csail.mit.edu/news/reprogrammable-materials-selectively-self-assemble" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;<hr>While automated manufacturing is ubiquitous today, it was once a nascent field birthed by inventors such as Oliver Evans, who is credited with creating the first fully automated industrial process, in flour mill he built and gradually automated in the late 1700s. The processes for creating automated structures or machines are still very top-down, requiring humans, factories, or robots to do the assembling and making.&nbsp;However, the way nature does assembly is ubiquitously bottom-up; animals and plants are self-assembled at a cellular level, relying on proteins to self-fold into target geometries that encode all the different functions that keep us ticking. For a more bio-inspired, bottom-up approach to assembly, then, human-architected materials need to do better on their own. Making them scalable, selective, and reprogrammable in a way that could mimic nature&rsquo;s versatility means some teething problems, though.&nbsp;Now, researchers from MIT&rsquo;s Computer Science and Artificial Intelligence Laboratory (CSAIL) have attempted to get over these growing pains with a new method: introducing magnetically reprogrammable materials that they coat different parts with &mdash; like robotic cubes &mdash; to let them self-assemble. Key to their process is a way to make these magnetic programs highly selective about what they connect with, enabling robust self-assembly into specific shapes and chosen configurations.&nbsp;<iframe src="https://www.youtube.com/embed/tWDWDf08XhE?&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: 768px; height: 429px;"></iframe>The soft magnetic material coating the researchers used, sourced from inexpensive refrigerator magnets, endows each of the cubes they built with a magnetic signature on each of its faces. The signatures ensure that each face is selectively attractive to only one other face from all the other cubes, in both translation and rotation. All of the cubes &mdash; which run for about 23 cents &mdash; can be magnetically programmed at a very fine resolution. Once they&#39;re tossed into a water tank (they used eight cubes for a demo), with a totally random disturbance &mdash; you could even just shake them in a box &mdash; they&rsquo;ll bump into each other. If they meet the wrong mate, they&#39;ll drop off, but if they find their suitable mate, they&#39;ll attach.&nbsp;An analogy would be to think of a set of furniture parts that you need to assemble into a chair. Traditionally, you&rsquo;d need a set of instructions to manually assemble parts into a chair (a top-down approach), but using the researchers&rsquo; method, these same parts, once programmed magnetically, would self-assemble into the chair using just a random disturbance that makes them collide. Without the signatures they generate, however, the chair would assemble with its legs in the wrong places.&ldquo;This work is a step forward in terms of the resolution, cost, and efficacy with which we can self-assemble particular structures,&rdquo; says Martin Nisser, a PhD student in MIT&#39;s Department of Electrical Engineering and Computer Science (EECS), an affiliate of CSAIL, and the lead author on a <a data-lf-fd-inspected-kn9eq4roawkarlvp="true" href="http://groups.csail.mit.edu/hcie/files/research-projects/selective/selective.pdf" target="_blank">new paper about the system</a>. &ldquo;Prior work in self-assembly has typically required individual parts to be geometrically dissimilar, just like puzzle pieces, which requires individual fabrication of all the parts. Using magnetic programs, however, we can bulk-manufacture homogeneous parts and program them to acquire specific target structures, and importantly, reprogram them to acquire new shapes later on without having to refabricate the parts anew.&rdquo;&nbsp;Using the team&rsquo;s magnetic plotting machine, one can stick a cube back in the plotter and reprogram it. Every time the plotter touches the material, it creates either a &ldquo;north&rdquo;- or &ldquo;south&rdquo;-oriented magnetic pixel on the cube&rsquo;s soft magnetic coating, letting the cubes be repurposed to assemble new target shapes when required. Before plotting, a search algorithm checks each signature for mutual compatibility with all previously programmed signatures to ensure they are selective enough for successful self-assembly.With self-assembly, you can go the passive or active route. With active assembly, robotic parts modulate their behavior online to locate, position, and bond to their neighbors, and each module needs to be embedded with hardware for the computation, sensing, and actuation required to self-assemble themselves. What&#39;s more, a human or computer is needed in the loop to actively control the actuators embedded in each part to make it move. While active assembly has been successful in reconfiguring a variety of robotic systems, the cost and complexity of the electronics and actuators have been a significant barrier to scaling self-assembling hardware up in numbers and down in size.&nbsp;With passive methods like these researchers&rsquo;, there&rsquo;s no need for embedded actuation and control.Once programmed and set free under a random disturbance that gives them the energy to collide with one another, they&rsquo;re on their own to shapeshift, without any guiding intelligence. &nbsp;If you want a structure built from hundreds or thousands of parts, like a ladder or bridge, for example, you wouldn&rsquo;t want to manufacture a million uniquely different parts, or to have to re-manufacture them when you need a second structure assembled.The trick the team used toward this goal lies in the mathematical description of the magnetic signatures, which describes each signature as a 2D matrix of pixels. These matrices ensure that any magnetically programmed parts that shouldn&rsquo;t connect will interact to produce just as many pixels in attraction as those in repulsion, letting them remain agnostic to all non-mating parts in both translation and rotation.&nbsp;While the system is currently good enough to do self-assembly using a handful of cubes, the team wants to further develop the mathematical descriptions of the signatures. In particular, they want to leverage design heuristics that would enable assembly with very large numbers of cubes, while avoiding computationally expensive search algorithms.&nbsp;&ldquo;Self-assembly processes are ubiquitous in nature, leading to the incredibly complex and beautiful life we see all around us,&rdquo; says Hod Lipson, the James and Sally Scapa Professor of Innovation at Columbia University, who was not involved in the paper. &ldquo;But the underpinnings of self-assembly have baffled engineers: How do two proteins destined to join find each other in a soup of billions of other proteins? Lacking the answer, we have been able to self-assemble only relatively simple structures so far, and resort to top-down manufacturing for the rest. This paper goes a long way to answer this question, proposing a new way in which self-assembling building blocks can find each other. Hopefully, this will allow us to begin climbing the ladder of self-assembled complexity.&rdquo;Nisser wrote the paper alongside recent EECS graduates Yashaswini Makaram &#39;21 and Faraz Faruqi SM &#39;22, both of whom are former CSAIL affiliates; Ryo Suzuki, assistant professor of computer science at the University of Calgary; and MIT associate professor of EECS Stefanie Mueller, who is a CSAIL affiliate. They will present their research at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022).<hr>&quot;Reprinted with permission of&nbsp;<a href="https://www.csail.mit.edu/news/reprogrammable-materials-selectively-self-assemble" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot;&nbsp;

While automated manufacturing is ubiquitous today, it was once a nascent field birthed by inventors such as Oliver Evans, who is credited with creating the first fully automated industrial process, in flour mill he built and gradually automated in the late 1700s.

Reprogrammable materials selectively self-assemble

A robot, or a group of robots, on a mission often have conflicting priorities. &nbsp;The primary mission of a team of search and rescue robots, for example, is to find survivors. But when they do, they also need to be able to send information to the rest of the team. The best way to find survivors may be to spread out, but the best way to send information is to stay close together. If robotic teams are going to be deployed in more real-world situations, they&rsquo;re going to need a system to balance these priorities.Computer scientists at the&nbsp;<a href="https://www.seas.harvard.edu/" target="_blank">Harvard John A. Paulson School of Engineering and Applied Sciences</a> (SEAS) have developed a new control mechanism for robotic teams that allow them to automatically calculate the tradeoffs of maintaining a resilient communication network and achieving their primary goal of moving through an environment. The team calculated which types of environments a robotic team could navigate while maintaining communication and which environments would force a team to break communication.&ldquo;We wanted to understand and analyze the tradeoff between making progress and maintaining a resilient communication network and build a control mechanism that can deal with those tradeoffs without deadlocking the robotic team,&rdquo; said&nbsp;<a href="https://gil.seas.harvard.edu/" target="_blank">Stephanie Gil, Assistant Professor of Computer Science at SEAS and senior author of the paper.</a><a href="https://gil.seas.harvard.edu/" target="_blank">The research was presented recently at the&nbsp;</a><a data-lf-fd-inspected-kn9eq4roawkarlvp="true" href="http://www.roboticsproceedings.org/rss18/p053.pdf" target="_blank">Robotics: Science and Systems</a> conference, where it was selected as a finalist for best paper.In multi-robot systems, communication is often dependent on the distance between individual robots. Many robotic teams use networked communication, relaying signals from one robot to another to cover increased distance between team members.&ldquo;A resilient communication network has multiple paths of communication between any robot on the team to any other and so even if multiple robots were to fail, there are other ways to pass the information,&rdquo; said Matthew Cavorsi, a graduate student at SEAS and first author of the paper.<iframe src="https://www.youtube.com/embed/HPdAcipt9K8?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-gtm-yt-inspected-12="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" style="width: 767px; height: 427px;"></iframe>SEAS researchers developed a new control mechanism for robotic teams maintaining a resilient communication network while moving through an environment.&nbsp;These resilient robotic systems need to maintain specific formations with certain distances between each robot to keep those multiple paths of communications open. But what happens when an environment, such as a narrow hallway, constricts the formation? If the robotic team is forced into a single-file line, the resiliency of the network breaks down because there is only one path along which to send a signal.&ldquo;If the communication network presents single points of failure, then this multi-robot system becomes very fragile,&rdquo; said Lorenzo Sabattini, Associate Professor of Robotics at the University of Modena and Reggio Emilia in Italy and co-author of the paper. &nbsp;&ldquo;Our work aims at removing this issue by determining when the system can maintain resiliency and achieve its goal, when resiliency and the goal are incompatible, and how the system can regain resiliency as quickly as possible after having to temporarily break it, thus paving the way towards reliable real-world application of multi-robot systems.&rdquo;The team identified a cut-off point where the robotic team is connected enough to be resilient. Through models and experiments, the team demonstrated that the control mechanism can compute the connectedness of the team as it moves and makes adjustments to keep it within the threshold.Cavorsi developed two types of controls: one where resiliency is a soft constraint and one where resiliency is a hard constraint.If a multi-robot system with resiliency as a soft constraint encounters an environment that would force it to drop below the resiliency threshold, such as a narrow hallway or large boulder, it will break resiliency long enough to move through the environment. If resiliency is a hard constraint, the system will turn back or find another way through the environment that doesn&rsquo;t require it to break resiliency.Soft constraint resiliency could work well for search and rescue robots where achieving the goal of finding survivors is worth the risk of temporarily weakening their communications. Resiliency could be more important for robotic systems on Mars, for example, where maintaining communications and avoiding dangerous obstacles is critical to the mission.<iframe src="https://www.youtube.com/embed/E7E59Mm-pS0?&wmode=opaque&rel=0&enablejsapi=1&origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-gtm-yt-inspected-12="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" style="width: 767px; height: 418px;"></iframe>Multi-robot teams may one day provide assistance to traditional rescue efforts.&nbsp;The research was co-authored by Beatrice Capelli and Lorenzo Sabattini.&nbsp;

A new control mechanism allows teams of robots (above) to calculate the tradeoffs of maintaining a communication network and move through difficult terrain, including narrow hallways. (Credit: REACT Lab/Harvard SEAS) 

Resilient robot teams juggle competing priorities without deadlock

Resilient robots

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"></iframe> <hr>This podcast is sponsored by <a href="https://www2.mouser.com/" id="isPasted" rel="nofollow" target="_blank">Mouser Electronics</a>.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1638866441849-Mouser-sponsor-1-a.jpg" style="width: 766px;" class="fr-fic fr-dib">&nbsp;<hr><h3 id="isPasted">EPISODE NOTES</h3>(1:41) -&nbsp;<a href="https://www.wevolver.com/article/adaptive-swarm-robotics-could-revolutionize-smart-agriculture" target="_blank">Adaptive Swarm Robotics Could Revolutionize Agriculture 🚜🤖</a>(12:11) -&nbsp;<a href="https://www.wevolver.com/article/autonomous-agricultural-robots-revolutionize-organic-farming" target="_blank">Autonomous Agricultural Robots Revolutionize Organic Farming 🌾🚜</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">here&nbsp;</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">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 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

swarm robotics

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