Uninterrupted consistent network coverage has historically been, and still is, hard to achieve. Cellular and wireless networks come with particular limitations, offering fragmented connectivity and use across devices. Power efficiency makes this challenge even more difficult as devices require a continuously active connection.To solve this challenge, we set out to test LTE CAT-1 as a new solution.Our goal was to test continuous strong connectivity even in the remotest of areas where the technology infrastructure significantly lags behind the developed world and connectivity is low.&nbsp;The question we asked ourselves was, could we bridge this wide gap and provide reliable internet access even in the areas with the weakest connectivity infrastructure?Turns out, we could.Sodaq leads the world&nbsp;in low-power sensing and tracking. To tackle the connectivity challenge, we partnered&nbsp;with Monogoto &ndash; a&nbsp;software-defined connectivity platform that offers cloud-based connectivity for next-gen devices.&nbsp;&nbsp;<img data-fr-image-pasted="true" src="https://monogoto.io/wp-content/uploads/2023/05/6Y3A0857-1024x683.jpg" alt="An Epic Quest to Validate Continuous Global Connectivity" width="800" height="534" srcset="https://monogoto.io/wp-content/uploads/2023/05/6Y3A0857-1024x683.jpg 1024w, https://monogoto.io/wp-content/uploads/2023/05/6Y3A0857-300x200.jpg 300w, https://monogoto.io/wp-content/uploads/2023/05/6Y3A0857-768x512.jpg 768w, https://monogoto.io/wp-content/uploads/2023/05/6Y3A0857-1536x1024.jpg 1536w, https://monogoto.io/wp-content/uploads/2023/05/6Y3A0857-2048x1365.jpg 2048w" sizes="(max-width: 800px) 100vw, 800px" class="fr-fic fr-dii">&nbsp;Through this partnership, we live-tested multiple tracking devices across Europe and Africa. An airplane carried multiple SODAQ-manufactured devices with the NINA/LENA series of U-blox, powered by Monogoto connectivity. Each tracking device showed the route traveled and reported from each landing location.The journey covered 10 countries, beginning in Europe, following the Nile River through Egypt and Sudan, and continuing towards the east African coastal region of Tanzania before heading further south to Mozambique, Malawi, Zimbabwe, Botswana, and finally South Africa.&nbsp;Other technologies haven&rsquo;t solved it yetAt the Mobile World Congress 2018, it was highlighted that LTE-M and NB-IoT, both&nbsp;<a data-wpel-link="external" href="https://en.wikipedia.org/wiki/Low-power_wide-area_network" rel="external noopener noreferrer" target="_blank">low-power wide-area network</a> <a data-wpel-link="external" href="https://en.wikipedia.org/wiki/Radio_communication" rel="external noopener noreferrer" target="_blank">radio communication</a> technologies for connected devices, could not solve the connectivity challenge in countries with limited IoT capabilities. It would take years to deploy these technologies due to the resource-intensive work required for roaming contracts with multiple providers. This may contribute to the reason why chip and module vendors have noted that sales of LPWAN modules did not meet original estimations.&nbsp;Testing a new way: LTE CAT-1LTE CAT-1 is a ubiquitous technology used by smartphones all over the world and we believed it could offer low-cost and low-power connectivity. CAT-1 technology has been in development for years and its potential to revolutionize global connectivity has been long anticipated. Devices with LTE (Long-Term Evolution) technology are capable of achieving higher data transfer speeds compared to older generations like 2G or 3G.&nbsp;Because it is compatible with older network technologies such as 2G and 3G, even countries with outdated infrastructure can leverage their existing 2G and 3G networks. Plus, the focus on wider coverage allows network providers to extend their services to remote or rural areas that may not have had access to cellular connectivity before.&nbsp;What we learnedWe learned that true global connectivity could be achieved.&nbsp;Our key takeaway is that CAT-1 is well positioned to provide continuous coverage even in the remotest of areas &ndash; it has the benefit of being highly efficient, and needing low power usage, without the downside of CAT-M or NB-IoT coverage limitations. Our partner Monogoto was able to provide that connectivity directly and we look forward to seeing what comes next of the partnership.

In today’s world, continuous connectivity sounds like an everyday given. But delivering it is far from simple.

An Epic Quest to Validate Continuous Global Connectivity

The&nbsp;<a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/research-aircraft-fleet/istar-dassault-falcon-2000lx-d-bdlr" target="_blank">In-flight Systems and Technology Airborne Research (ISTAR)</a> aircraft has begun scientific operations. ISTAR flights focus on the flight-mechanical and flight-dynamic characteristics of the modified Dassault Falcon 2000LX. Novel sensors measure how the aircraft behaves during specific flight manoeuvres. The German Aerospace Center (Deutsches Zentrum f&uuml;r Luft- und Raumfahrt; DLR) will use the results to further develop ISTAR&rsquo;s digital twin. This will lead to more energy-efficient aircraft designs. The digital twin becomes an exact virtual image of the aircraft in a computer.The flight manoeuvres and ground tests bring together data from novel measurement techniques with state-of-the-art simulation tools. &quot;The digital twin will make virtual flight tests possible in the future,&quot; explains project manager Heiko von Geyr from the&nbsp;<a href="https://www.dlr.de/en/as" target="_blank">DLR Institute of Aerodynamics and Flow Technology</a> in&nbsp;<a href="https://www.dlr.de/en/dlr/locations-and-offices/braunschweig" target="_blank">Braunschweig</a>. ISTAR should not be limited to testing solely within a flight simulator; instead, it should be possible for the aircraft to undergo realistic testing. The aim of the&nbsp;<a href="https://www.dlr.de/as/en/desktopdefault.aspx/tabid-18236/28992_read-76291/" rel="noopener noreferrer" target="_blank">High-speed inflight validation (HighFly)</a> project is to combine several processes: &quot;This enables us to predict the structural deformation, the airflow and the accelerations at any point on ISTAR at any given time,&quot; explains Geyr. Until the researchers can realistically map the aircraft&rsquo;s behaviour digitally, they have to compare the computational models with measurement results and develop them further. &quot;Only then will we be able to determine whether an aircraft behaves exactly the same on the computer as it does under real conditions.&quot;ISTAR has been taking off from the airport at DLR&rsquo;s Braunschweig site for measurement flights since the end of February 2023. In Braunschweig, the aircraft was modified for scientific purposes and prepared for the project. Almost 400 manoeuvres have been flown so far at different speeds, altitudes and in different environments in a reserved airspace over Mecklenburg-Western Pomerania. In the future, researchers will be able to test new aircraft designs digitally at a relatively early stage using a digital twin. This will also enable them to assess whether changes affect the aircraft characteristics and flight behaviour. In addition to virtual flight tests, the researchers are also working towards simulation-based certification in the long term. This could accelerate time-consuming and cost-intensive approval procedures in aeronautics.<h2>HighFly project investigates changes during flight manoeuvres</h2>To measure how the airflow on the wings changes during different flight manoeuvres and how much the wings deform during flight, the researchers attached a dotted foil on the left wing. Up close, some of the dots are round, and some have an elongated shape. Looking through the cameras inside the fuselage pointing at the wing from a certain angle, all the dots appear the same. When the wings bend during flight, the cameras record the change in position of the dots. This is how the local deformation of the wing is determined.Sensors are distributed on and under the right wing. These Microelectromechanical Systems (MEMS) sensors measure the pressure and temperature distribution with high accuracy and high frequency during the flight tests. &ldquo;The MEMS system is a completely new development within the project and is being used here for the first time in the transonic speed range under real flight conditions,&rdquo; says Geyr. In the transonic range, the aircraft flies slower than the speed of sound, but the airflow around the aerofoil still reaches supersonic speed in places. This results in a very complex flow with compression shocks, which must be reliably detected by the MEMS sensors.In addition, three MEMS sensor surfaces are installed near the flight deck. They measure pressure fluctuations in the thin boundary layers, which are created here by the shape of the aircraft. The DLR Institute of Aerodynamics and Flow Technology in&nbsp;<a href="https://www.dlr.de/en/dlr/locations-and-offices/goettingen" target="_blank">G&ouml;ttingen</a> developed the MEMS sensor surfaces.<h2>Valuable database</h2>The test pilots fly precisely defined manoeuvres with ISTAR at 11,000, 25,000, 35,000 and 45,000 feet. These correspond to altitudes of 3350, 7620, 10,670 and 13,720 metres. The computers located in the cabin store all the readings. The researchers later compare the treasure trove of data with the results of the&nbsp;<a href="https://www.dlr.de/en/media/videos/2017/video-future-aircraft-engineering-the-numerical-simulation" target="_blank">numerical simulation</a>. Supercomputers, such as DLR&rsquo;s CARO and CARA clusters, are essential for this. &quot;With our measurements, we cover the entire transonic flight range, pushing the boundaries of what can be achieved during flight. The combination of measurement techniques makes this database unique and extremely valuable,&quot; explains Geyr.Researchers from the<a href="https://www.dlr.de/en/ft" target="_blank">&nbsp;DLR Institute of Flight Systems</a> have also conducted Parameter Identification (PID) flights as part of the HighFly project. This results in a flight dynamic simulation model of ISTAR. This can then be used in a flight simulator such as the&nbsp;<a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/air-vehicle-simulator" target="_blank">Air Vehicle Simulator (AVES)</a>. &ldquo;In this way, we can first test newly developed flight control and assistance systems using the simulator before we test them during flight,&rdquo; says Christian Raab from the DLR Institute of Flight Systems.<h2>Ground tests for the digital twin</h2>On the ground, the researchers from the&nbsp;<a href="https://www.dlr.de/en/ae" target="_blank">DLR Institute of Aeroelasticity</a> conducted a&nbsp;<a href="https://www.dlr.de/en/latest/news/2021/03/20210702_vibrating-research-aircraft" target="_blank">Ground Vibration Test</a> (GVT) and Taxi Vibration Tests (TVT) in the summer of 2021. &quot;The results are relevant far beyond the HighFly project; every modification of ISTAR needs an aeroelastic analysis to demonstrate the operational safety of the modification,&quot; explains Marc B&ouml;swald from the DLR Institute of Aeroelasticity. Aircraft do not have a completely rigid structure. To fly safely, elasticity must therefore be taken into account.During the GVT, ISTAR was supported on jacks and subjected to vibrations. More than 200 additional sensors measured ISTAR&rsquo;s reactions, and these were used to create a model. In the TVT, ISTAR was pulled along the runway. Uneven ground causes vibrations via the landing gear. During the current flights, the researchers also monitored the aeroelastic properties of ISTAR using newly developed methods. In future, these tests could also be carried out using the digital twin.The DLR Institute of Aerodynamics and Flow Technology also measured the airflow to the engines with&nbsp;<a href="https://www.dlr.de/en/research-and-transfer/research-infrastructure/particle-image-velocimetry-goettingen" target="_blank">Particle Image Velocimetry (PIV)</a> for various thrust settings. The engine inflow influences the aerodynamics of ISTAR. The data obtained has also been made available for the digital twin.

The upgrading of the In-flight Systems and Technology Airborne Research (ISTAR) aircraft will take place in several stages. As an airborne simulator, it is advancing the digitalisation of aeronautics. The research aircraft is based at DLR’s Flight Experiments Facility in Braunschweig, where it is part of the largest civilian research fleet in Europe. Credits: DLR (CC BY-NC-ND 3.0)

ISTAR flights focus on the flight-mechanical and flight-dynamic characteristics of the modified Dassault Falcon 2000LX. Novel sensors measure how the aircraft behaves during specific flight manoeuvres.

Four hundred flight manoeuvres with ISTAR advance its digital twin

For centuries humans have been fascinated by how easily birds glide through the air. Their instinctive ability to use wind to their advantage is what inspired Morpho, the new inspection drone developed by Elythor. Part winged drone, part quadcopter, Morpho is a hybrid unmanned aerial vehicle (UAV) that can change shape according to the task at hand. Its adaptive wings extend the drone&rsquo;s flight time and give it greater maneuverability. Combined with embedded sensors and cameras, these features let it fly just as well in enclosed spaces as in the open air, making it ideal for inspecting power plants and other infrastructure like high-voltage power lines, wind turbines, gas pipelines and offshore oil platforms.<iframe width="640" height="360" src="https://www.youtube.com/embed/tOUkn7YmYV4?&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> <h2>Cutting costs by a third and reducing inspection time</h2>When sitting on its four legs, Morpho has a streamlined shape that resembles a tiny rocket. But that quickly changes once its rotors start whirring. The drone&rsquo;s vertical take-off capability means it can be deployed in confined spaces, fly to within just a few centimeters of a piece of equipment, and inspect the equipment without bumping into it. Once the inspection is completed, Morpho can shift its motors and rotate into a horizontal, more aerodynamic position and swiftly fly off to the next inspection site. If the drone encounters strong or rapidly changing winds along the way, it will expand or contract its wings as necessary to maintain its trajectory. It can also glide under the right wind conditions. All that saves time during inspections, but also means the drone can fly for longer. &ldquo;We calculated that using Morpho can cut the time and cost of a drone inspection by an average of 35%,&rdquo; says Harry Vourtsis, Elythor co-founder and CEO. Once Morpho reaches the next inspection site, it can fold up its wings and return to a vertical position in order to fly right up to the equipment.<blockquote>Our design can reduce power use by up to 85% when the drone is flying in the vertical position&nbsp; - Nathan M&uuml;ller, co-founder of Elythor&nbsp;</blockquote><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1683465765223-003ee723.jpg" style="width: 766px;" class="fr-fic fr-dib">Harry Vourtsis, CEO and co-founder, and Nathan M&uuml;ller, co-founder of the start-up. EPFL/Alain Herzog&nbsp;<h2 id="isPasted">Asymmetric wing adjustment provides stability in strong winds</h2>&ldquo;Winged drones have the advantage of longer flight times, while quadcopters have better manoeuvrability,&rdquo; says Vourtsis. &ldquo;We combined the two and added an adaptative wing system that reduces the drone&rsquo;s power requirement even further.&rdquo; Most fixed-wing vertical take-off and landing (VTOL) drones represent a compromise, with wings that are small enough to reduce friction during vertical flight yet large enough to provide sufficient lift during horizontal flight. VTOL drones also have trouble taking off and landing in strong winds due to their large surface area perpendicular to the wind.Morpho&rsquo;s wing control system is the result of several years of research at EPFL&rsquo;s Laboratory of Intelligent Systems and has been described in a number of scientific articles. The system includes sensors linked to a software program for monitoring wind direction and speed. Nathan M&uuml;ller, also an Elythor co-founder, explains: &ldquo;The controller automatically selects whether to hold the wings in place or let them move freely with the wind, based on the drone&rsquo;s trajectory and effective speed, along with any changes in wind direction. The wings&rsquo; surface area can also be adjusted either symmetrically or asymmetrically depending on wind direction.&rdquo;The algorithms powering the control system seek not just to optimize the trade-off between air friction and lift, but also to minimize power use. This entails taking advantage of wind currents to let the drone glide or adjusting the wings asymmetrically to regulate its yaw &ndash; or the rotation around its vertical axis. This provides greater stability in heavy winds.Quantitative studies carried out at the EPFL lab show that Morpho delivers considerably better maneuverability and power efficiency. &ldquo;Our design can reduce power use by up to 85% when the drone is fyling in the vertical position,&rdquo; says M&uuml;ller. &ldquo;It also significantly improves the drone&rsquo;s attitude and stability.&rdquo; These benefits mean Morpho can perform inspections under a broader range of weather conditions.<h2>&ldquo;We want to revolutionize inspection platforms&rdquo;</h2>Because Elythor aims to provide customers with a turnkey solution, it&rsquo;s also developing software for compiling and analyzing the data collected by Morpho. &ldquo;We want to revolutionize inspection platforms,&rdquo; says Vourtsis. Today, power plant operators use different kinds of drones depending on where the equipment to inspect is located. And the facilities sometimes stretch over several kilometers and include complicated structures that must be entered, like wind turbines and transmission towersElythor has raised a substantial amount of startup funding and plans to get its drone ready for industrial production in the coming months, with a market launch slated for the end of this year.<hr>References<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202200297" rel="noopener" target="_blank">Wind Defiant Morphing Drones in Advanced Intelligent Systems</a> (January 2023)<a href="https://infoscience.epfl.ch/record/301328" target="_blank">Resilient drones with morphing wings&nbsp;</a>-Harry Vourtsis Thesis (March 2023)<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9591382" rel="noopener" target="_blank">Robotic Elytra: Insect-Inspired Protective Wings for Resilient and Multi-Modal Drones</a> - IEEE Robotics and Automation Letters (January 2022)<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9479684" rel="noopener" target="_blank">Insect Inspired Self-Righting for Fixed-Wing Drones</a> - IEEE Robotics and Automation Letters (October 2021)

Elythor, an EPFL spin-off, has developed a new drone whose wing shape can adapt to wind conditions and flight position in real time, reducing the drone’s energy consumption. What’s more, the position of the wings can change, allowing the drone to fly vertically or horizontally. These features make it a perfect candidate for inspecting power plants.

New inspection drone uses wind to lengthen flight times

In this article, we will dive deep into the world of simultaneous localization and mapping using Lidar technology. Lidar SLAM has been gaining popularity in recent years, thanks to its versatility and applications across various domains, including autonomous vehicles, mobile robotics, and indoor mapping.

Lidar SLAM: The Ultimate Guide to Simultaneous Localization and Mapping

<h2 dir="ltr" id="isPasted">Fast track into the cloud with CGConnect</h2>&quot;Enterprises who rely on drones and robotics for business operation often own a diverse range of uncrewed vehicles that may not be compatible with one another. <a href="https://youtu.be/lumuuYy2fHU" target="_blank">CGConnect</a> is designed to solve this pain point by linking them to the Cloud Ground Control platform, regardless of manufacturer or model, turning them into a holistic, connected fleet,&quot; said Michal Weiss, Head of Product at Cloud Ground Control.<iframe width="640" height="360" src="https://www.youtube.com/embed/fogtQQ3N4rQ?&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> <h2 dir="ltr">Plug-and-play connectivity</h2>Using CGConnect, remote users gain instant access to Cloud Ground Control&#39;s features, including real-time telemetry, cloud storage, video and payload data, all from a web browser simultaneously. Weighing only 55 grams with similar sizing to a credit card, CGConnect can be integrated into any product design, offering the following benefits:<ul id="isPasted"><li dir="ltr">Open platform: The customisable open platform operates on the MAVLink standard. This multiplies potential product applications and enables diverse autonomous vehicles and payloads to operate as a coordinated fleet.</li><li dir="ltr">Robotic agnostic: Works flexibly with open-sourced libraries and is agnostic to the type of technology and vehicle enterprises may wish to use.&nbsp;</li></ul><ul><li dir="ltr">White labelled:&nbsp;Available as a white label product, allowing users to rebrand the user interface to complement business branding and coding requirements.</li><li dir="ltr">High-grade security:&nbsp;Utilises military-grade encryption and authentication to safeguard data and IP from vulnerabilities and security breaches, helping users meet compliance obligations.</li><li dir="ltr">Simple and accessible: Revolutionises multi-drone operation by making it simple, cost-effective and accessible to users of every skill grade.</li><li dir="ltr">AI modelling:&nbsp;The platform runs AI algorithms in the cloud, relaying real-time camera feed data to the end user to support versatile missions, such as object detection, tracking and thermal imaging.</li><li dir="ltr">Edge AI: CGConnect supports edge AI to perform intensive object identification and classification directly on the vehicle for dynamic missions.</li></ul><iframe width="640" height="360" src="https://www.youtube.com/embed/lumuuYy2fHU?&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> <h2 dir="ltr" id="isPasted">Unlock new markets</h2>Integrating CGConnect into a vehicle&#39;s design expands the product&#39;s functionality and applications while saving development time. This allows manufacturers to fine-tune the product&#39;s competitive advantage, expanding the robot&#39;s potential and ultimately unlocking access to new markets.<iframe width="640" height="360" src="https://www.youtube.com/embed/-jVsC77f5bo?&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> Cloud Ground Control helped Clean Earth Rovers transform its Rover AVPros into an autonomous, connected fleet. By integrating CGConnect, Clean Earth Rovers were able to provide customers with real-time situational awareness and greatly accelerate recovery efforts in ocean health.&nbsp;<a href="https://www.youtube.com/watch?v=-jVsC77f5bo&t=22s" target="_blank">Discover the integration here</a>.For more information, visit&nbsp;<a href="http://www.cloudgroundcontrol.com/manufacturers" target="_blank">www.cloudgroundcontrol.com/manufacturers</a>.&nbsp;

CGConnect, its cellular micro-modem that uses 4G/5G networks to link any drone or robotic vehicle to Cloud Ground Control’s cloud-based drone fleet management platform, enabling live-streaming, command and control from a web browser.

Transform multiple unmanned vehicles into connected fleet with new  micro-cellular modem

Their laser-based sensing approach, detailed in a paper <a href="https://www.sciencedirect.com/science/article/pii/S0034425723000640?dgcid=author" target="_blank">published</a> Mar. 1 in Remote Sensing of Environment, can accurately detect and quantify both large greenhouse gas leaks and leaks up to 25 times smaller than those typically detected at natural gas facilities using other methods, localizing the emissions source to within a meter. Because it takes advantage of the remote-sensing capabilities of lasers combined with the agility of drones, the new technology can also be used to quickly spot otherwise unseen leaks in hard-to-access areas, an innovation the researchers said unlocks game-changing potential for atmospheric sensing.&ldquo;Current approaches for detecting leaks often rely on handheld infrared cameras that are labor-intensive to operate and insensitive to small leaks, or they use methods that require setting up extensive measurement infrastructure ahead of time,&rdquo; said <a href="https://ece.princeton.edu/people/gerard-wysocki" target="_blank">Gerard Wysocki</a>, associate professor of <a href="https://ece.princeton.edu/" target="_blank">electrical and computer engineering</a> and associated faculty at the <a href="https://acee.princeton.edu/" target="_blank">Andlinger Center for Energy and the Environment</a>. &ldquo;But with a drone, you are completely free in how you are able to set up your sensing area.&rdquo;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1682346135815-20230328_methane_drone_BD_03_CROPPED_v3-300x225+%281%29.jpg" style="width: 766px;" class="fr-fic fr-dib">The retroflector mounted onto the drone. Photo by Bumper DeJesus&nbsp;The researchers&rsquo; approach consists of a small drone outfitted with only a retroreflector, a type of mirror that reflects incoming light directly back to the source, and a base station of gas sensing equipment with the capability to track the drone&rsquo;s movement during flight. Bouncing a laser beam off the drone as it flies to set points around a suspected leak allows an operator to pinpoint the source of the leak and measure its intensity.&ldquo;That&rsquo;s really the holy grail of leak detection,&rdquo; said <a href="https://cee.princeton.edu/people/mark-zondlo" target="_blank">Mark Zondlo</a>, study co-author, professor of <a href="https://cee.princeton.edu/" target="_blank">civil and environmental engineering</a>, and associated faculty at the Andlinger Center for Energy and the Environment.While drone-based techniques for atmospheric sensing exist, they usually require mounting a gas sensor directly onto a drone, a practice that Zondlo said quickly comes up against some major roadblocks related to how much weight a drone can carry and how risky it is to fly a drone overloaded with costly sensors in hazardous environments.&ldquo;You really can&rsquo;t fit more than one gas sensor on a drone at a time, otherwise it just becomes too big and bulky to fly. And you probably don&rsquo;t want to risk flying an expensive sensor over the lagoon of a wastewater treatment plant or the flaring of a compressor station,&rdquo; Zondlo said.Instead of overburdening the drone with sensors, the Princeton group offloaded the expensive gas sensing components to the base station, which can fit on a mobile platform such as a van, meaning that the drone only needed to carry a small mirror. That change allowed the researchers to use smaller, less expensive drones with longer flight times to collect highly detailed emissions data across large areas, potentially unlocking the ability to monitor entire natural gas transmission and distribution facilities in a single drone flight. &ldquo;Our approach allows us to bypass the major constraints of using drones and enables us to make full use of their potential,&rdquo; Zondlo said. &ldquo;It&rsquo;s really a paradigm shift in how we can use drones for atmospheric sensing.&rdquo;The sensing method could also enable simultaneous measurements of multiple gases, a feat that is exceedingly difficult with other drone-based approaches due to size, weight, and power considerations. Michael Soskind, the first author of the study and a graduate student in electrical and computer engineering, said adding the ability to measure other gases like carbon dioxide and ammonia alongside methane would be as simple as adding other lasers of different wavelengths to the base system. &ldquo;All you&rsquo;d need to do is add a secondary laser to the system,&rdquo; he explained. &ldquo;The rest of the system is already built out to do the work.&rdquo;Because it offers users a great amount of flexibility, Wysocki said he sees the team&rsquo;s approach as a technology platform that could spur future innovation and applications beyond methane leak detection.&ldquo;The most exciting thing is not simply the methane sensing abilities of the technology we developed,&rdquo; he said. &ldquo;It&rsquo;s really about unlocking the capability for researchers and practitioners to use drones and other remote sensing techniques to take detailed measurements of small leaks and reconstruct emissions plumes. It&rsquo;s a technology that opens the doors for efficient leak detection and repair, which can help producers mitigate the safety and environmental hazards of those leaks, while also saving them time and money.&rdquo;The article, &ldquo;<a href="https://www.sciencedirect.com/science/article/pii/S0034425723000640?dgcid=author" target="_blank">Stationary and drone-assisted methane plume localization with dispersion spectroscopy</a>,&rdquo; was published Mar. 1 in Remote Sensing of Environment. In addition to Wysocki, Zondlo, and Soskind, authors include Nathan Li, Daniel Moore, Lars Wendt, and James McSpiritt of Princeton University, as well as Yifeng Chen, a former graduate student at Princeton University who now works at NEO Monitors. The work was supported by the U.S. Department of Energy&rsquo;s National Energy Technology Laboratory, the National Defense Science and Engineering Graduate Fellowship, and the High Meadows Environmental Institute through the Walbridge Fund Graduate Award for Environmental Research.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1682346155567-IMG_3129_toned.jpg" style="width: 766px;" class="fr-fic fr-dib">Lars Wendt, study co-author, operates the drone during field experiments. (Photo courtesy of the researchers)&nbsp;

Michael Soskind, first author and graduate student in electrical and computer engineering, stands with the drone. Photo by Bumper DeJesus

As evidence mounts that gas drilling and sewer systems leak far more greenhouse gases than previously believed, a team of Princeton researchers has developed a method to pinpoint leaks both big and small for speedy repair.

Using drones and lasers, researchers pinpoint greenhouse gas leaks

This article was first published on&nbsp;<a href="https://news.mit.edu/2023/drones-navigate-unseen-environments-liquid-neural-networks-0419" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>. &nbsp;<hr>In the vast, expansive skies where birds once ruled supreme, a new crop of aviators is taking flight. These pioneers of the air are not living creatures, but rather a product of deliberate innovation: drones. But these aren&rsquo;t your typical flying bots, humming around like mechanical bees. Rather, they&rsquo;re avian-inspired marvels that soar through the sky, guided by liquid neural networks to navigate ever-changing and unseen environments with precision and ease.Inspired by the adaptable nature of organic brains, researchers from MIT&rsquo;s Computer Science and Artificial Intelligence Laboratory (CSAIL) have introduced a method for robust flight navigation agents to master vision-based fly-to-target tasks in intricate, unfamiliar environments. The liquid neural networks, which can continuously adapt to new data inputs, showed prowess in making reliable decisions in unknown domains like forests, urban landscapes, and environments with added noise, rotation, and occlusion. These adaptable models, which outperformed many state-of-the-art counterparts in navigation tasks, could enable potential real-world drone applications like search and rescue, delivery, and wildlife monitoring.The researchers&#39; recent study, <a href="https://www.science.org/doi/10.1126/scirobotics.adc8892" target="_blank">published today in Science Robotics</a>, details how this new breed of agents can adapt to significant distribution shifts, a long-standing challenge in the field. The team&rsquo;s new class of machine-learning algorithms, however, captures the causal structure of tasks from high-dimensional, unstructured data, such as pixel inputs from a drone-mounted camera. These networks can then extract crucial aspects of a task (i.e., understand the task at hand) and ignore irrelevant features, allowing acquired navigation skills to transfer targets seamlessly to new environments.<iframe width="640" height="360" src="https://www.youtube.com/embed/RPFbk6D_dNw?&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>Drones navigate unseen environments with liquid neural networks.&nbsp;&ldquo;We are thrilled by the immense potential of our learning-based control approach for robots, as it lays the groundwork for solving problems that arise when training in one environment and deploying in a completely distinct environment without additional training,&rdquo; says Daniela Rus, CSAIL director and the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT. &ldquo;Our experiments demonstrate that we can effectively teach a drone to locate an object in a forest during summer, and then deploy the model in winter, with vastly different surroundings, or even in urban settings, with varied tasks such as seeking and following. This adaptability is made possible by the causal underpinnings of our solutions. These flexible algorithms could one day aid in decision-making based on data streams that change over time, such as medical diagnosis and autonomous driving applications.&rdquo;A daunting challenge was at the forefront: Do machine-learning systems understand the task they are given from data when flying drones to an unlabeled object? And, would they be able to transfer their learned skill and task to new environments with drastic changes in scenery, such as flying from a forest to an urban landscape? What&rsquo;s more, unlike the remarkable abilities of our biological brains, deep learning systems struggle with capturing causality, frequently over-fitting their training data and failing to adapt to new environments or changing conditions. This is especially troubling for resource-limited embedded systems, like aerial drones, that need to traverse varied environments and respond to obstacles instantaneously.&nbsp;The liquid networks, in contrast, offer promising preliminary indications of their capacity to address this crucial weakness in deep learning systems. The team&rsquo;s system was first trained on data collected by a human pilot, to see how they transferred learned navigation skills to new environments under drastic changes in scenery and conditions. Unlike traditional neural networks that only learn during the training phase, the liquid neural net&rsquo;s parameters can change over time, making them not only interpretable, but more resilient to unexpected or noisy data.&nbsp;In a series of quadrotor closed-loop control experiments, the drones underwent range tests, stress tests, target rotation and occlusion, hiking with adversaries, triangular loops between objects, and dynamic target tracking. They tracked moving targets, and executed multi-step loops between objects in never-before-seen environments, surpassing performance of other cutting-edge counterparts.&nbsp;The team believes that the ability to learn from limited expert data and understand a given task while generalizing to new environments could make autonomous drone deployment more efficient, cost-effective, and reliable. Liquid neural networks, they noted, could enable autonomous air mobility drones to be used for environmental monitoring, package delivery, autonomous vehicles, and robotic assistants.&nbsp;&ldquo;The experimental setup presented in our work tests the reasoning capabilities of various deep learning systems in controlled and straightforward scenarios,&rdquo; says MIT CSAIL Research Affiliate Ramin Hasani. &ldquo;There is still so much room left for future research and development on more complex reasoning challenges for AI systems in autonomous navigation applications, which has to be tested before we can safely deploy them in our society.&rdquo;&ldquo;Robust learning and performance in out-of-distribution tasks and scenarios are some of the key problems that machine learning and autonomous robotic systems have to conquer to make further inroads in society-critical applications,&rdquo; says Alessio Lomuscio, professor of AI safety in the Department of Computing at Imperial College London. &ldquo;In this context, the performance of liquid neural networks, a novel brain-inspired paradigm developed by the authors at MIT, reported in this study is remarkable. If these results are confirmed in other experiments, the paradigm here developed will contribute to making AI and robotic systems more reliable, robust, and efficient.&quot;Clearly, the sky is no longer the limit, but rather a vast playground for the boundless possibilities of these airborne marvels.&nbsp;Hasani and PhD student Makram Chahine; Patrick Kao &#39;22, MEng &#39;22; and PhD student Aaron Ray SM &#39;21 wrote the paper with Ryan Shubert &#39;20, MEng &#39;22; MIT postdocs Mathias Lechner and Alexander Amini; and Rus.This research was supported, in part, by Schmidt Futures, the U.S. Air Force Research Laboratory, the U.S. Air Force Artificial Intelligence Accelerator, and the Boeing Co.<hr>&quot;Reprinted with permission of&nbsp;<a href="https://news.mit.edu/2023/drones-navigate-unseen-environments-liquid-neural-networks-0419" id="isPasted" rel="noopener noreferrer" target="_blank">MIT News</a>&quot; &nbsp;

Makram Chahine, a PhD student in electrical engineering and computer science and an MIT CSAIL affiliate, leads a drone used to test liquid neural networks. Photo: Mike Grimmett/MIT CSAIL

MIT researchers exhibit a new advancement in autonomous drone navigation, using brain-inspired liquid neural networks that excel in out-of-distribution scenarios.

Drones navigate unseen environments with liquid neural networks

This flying robot attaches to various assets to perform contact based Non-Destructive Testing

Intelligent flying robots can perform Non-Destructive Testing inspections on various assets across various industries, including tanks, towers, pipes, flare stacks, ships, and more.

FLYING ROBOT'S RANGE OF ASSETS

A&nbsp;<a href="https://www.ae.utexas.edu/facultysites/topcu/wiki/index.php/Verifiable,_Control-Oriented_Learning_On_The_Fly" target="_blank">research team</a> from Princeton, the University of Texas, and Northeastern University is working under a $7.5 million grant from the U.S. Air Force Office of Scientific Research to pave the way for creating such a system. The basic research the team is doing could someday extend to aircraft controls and many other applications, including controlling disease epidemics or making more accurate predictions about climate change or species survival, said&nbsp;<a href="https://engineering.princeton.edu/faculty/amir-ali-ahmadi" target="_blank">Amir Ali Ahmadi</a>, a professor of&nbsp;<a href="https://orfe.princeton.edu/" target="_blank">operations research and financial engineering</a> at Princeton and a member of the research team.The goal is to exert measures of control over a &ldquo;dynamical system&rdquo; &mdash; one that changes as it moves. Most dynamical systems are notoriously difficult to predict and manage. Ahmadi, along with colleagues&nbsp;<a href="https://www.math.princeton.edu/people/charles-fefferman" target="_blank">Charles Fefferman</a>, the Herbert E. Jones, Jr. &rsquo;43 University Professor of Mathematics, and&nbsp;<a href="https://engineering.princeton.edu/faculty/clarence-rowley" target="_blank">Clarence Rowley</a>, the Sin-I Cheng Professor in Engineering Science, are trying to design algorithms that can learn the behavior of dynamical systems from data.&ldquo;A dynamical system is any entity in some space that evolves through time,&rdquo; Ahmadi said. &ldquo;So, an airplane is a dynamical system; a robot is a dynamical system; the spread of a virus is a dynamical system.&rdquo;Gaining control is particularly tough when data is limited, said Ahmadi. In the case of a damaged aircraft, &ldquo;the plane has changed, and you have less than a minute to come up with a new model of control,&rdquo; he said.Predicting future performance based on extremely sparse data is a common problem. It is hard to recommend the best response to a disease outbreak, for example, when very little is known about the spread of illness.In a recent&nbsp;<a href="https://epubs.siam.org/doi/abs/10.1137/20M1388644" target="_blank">article</a> in the SIAM Review, Ahmadi&rsquo;s research team presented an approach that uses additional information to rapidly respond to changing conditions in which little data is available for decision-making. This additional information, which mathematicians call side information, acts in the same way that experience or professional expertise does for a human. For example, a doctor might never have seen a particular disease before, but years of experience will help her make a good judgment on how to treat the patient.&ldquo;That is what this entire project is about,&rdquo; Ahmadi said. &ldquo;It is about learning a system from very little data and eventually controlling it in a way that we desire.&rdquo;<h2>Starting simple</h2>Long-term goals, such as aircraft controls, are beyond the scope of the immediate project. Rather, the work under the Air Force grant is focusing on much simpler examples in the hopes of learning more about controlling a system riddled with unknowns.&ldquo;In standard control theory, you understand what the controls do. We&rsquo;re trying to make a more powerful version of that theory in which you don&rsquo;t know what the controls do, but you learn by applying them,&rdquo; Fefferman said. He is working with Rowley on relatively simple sub-problems of dynamical systems &mdash; for instance, trying to temporarily halt an object as it moves along a straight line at a constant speed.&nbsp;In addition, the researchers want to use as little energy as possible to exert control &mdash; just as a pilot would want to do in a plane with limited fuel.Another problem they may tackle is an advanced version of a problem commonly assigned to undergraduate mechanical engineering majors: controlling an inverted pendulum &mdash; similar to trying to balance a broomstick in the palm of your hand. The controller would learn the behaviors of the system almost instantly and without knowing where its mass is centered. To do that, they would create equations for controls based on a few seconds of observation, then modify the controls after recording what they do. The model would be designed to rapidly go through several learn-and-control iterations.<h2>Knowledge versus control</h2>The problems the team explores involve tradeoffs between exploring functionality and exploiting the knowledge gained, Rowley said. &ldquo;If you exploit your knowledge too soon, the model may not be good enough to land a plane. But if you spend too much time learning its behavior, the plane may crash.&rdquo;There is no single technique for controlling a system with unknown dynamics, said&nbsp;<a href="https://www.ae.utexas.edu/people/faculty/faculty-directory/topcu" target="_blank">Ufuk Topcu</a>, a team member and associate professor at the University of Texas. But one of the keys is to select the most valuable data to work on. &ldquo;You have to tackle it from multiple angles and chop the big problem into more manageable pieces to identify what&rsquo;s worth learning,&rdquo; he said.When the grant ends, the researchers expect to have algorithms for controlling at least some aspects of a dynamical system. Though their model may not be fast enough to operate in real time, it should be able to show which controls are possible in a changing system and with what degree of certainty they can succeed, Ahmadi said.The&nbsp;<a href="https://epubs.siam.org/doi/abs/10.1137/20M1388644" target="_blank">article</a>&nbsp;&ldquo;Learning Dynamical Systems with Side Information&rdquo; was published in the February issue of the Society for Industrial and Applied Mathematics Review. Besides Ahmadi, authors include Bachir El Khadir, a former graduate student at Princeton who is now at the investment firm Two Sigma. Support for the project was provided in part by the Air Force Office of Scientific Research, the National Science Foundation, DARPA, the Sloan Research Fellowship, the Google Faculty Award, and an Innovation Award of the Princeton School of Engineering and Applied Science.

The researchers are working to improve controls for flight systems and other applications that demand quick responses. Photo by Bumper DeJesus

Commercial airplanes can be controlled by autopilot. But what happens if a wing gets damaged or an engine malfunctions? Is it possible to design a software system with a feedback loop — a system that quickly tests how controls operate on the damaged vessel and makes adjustments on the fly to give it the best chance of landing safely?

aviation