Aerospace Engineering Flight Dynamics

Researchers at Hong Kong University MaRS Lab have just published another jaw dropping paper featuring their safety-assured high-speed aerial robot path planning system dubbed "SUPER". With a single MID360 lidar sensor they repeatedly achieved autonomous one-shot navigation at speeds exceeding 20m/s in obstacle rich environments. Since it only requires a single lidar these vehicles can be built with a small footprint and navigate completely independent of light, GPS and radio link. This is not just #SLAM on a #drone, in fact the SUPER system continuously computes two trajectories in each re-planning cycle—a high-speed exploratory trajectory and a conservative backup trajectory. The exploratory trajectory is designed to maximize speed by considering both known free spaces and unknown areas, allowing the drone to fly aggressively and efficiently toward its goal. In contrast, the backup trajectory is entirely confined within the known free spaces identified by the point-cloud map, ensuring that if unforeseen obstacles are encountered or if the system’s perception becomes uncertain, the system can safely switch to a precomputed, collision-free path. The direct use of LIDAR point clouds for mapping eliminates the need for time-consuming occupancy grid updates and complex data fusion algorithms. Combined with an efficient dual-trajectory planning framework, this leads to significant reductions in computation time—often an order of magnitude faster than comparable SLAM-based systems—allowing the MAV to operate at higher speeds without sacrificing safety. This two-pronged planning strategy is particularly innovative because it directly addresses the classic speed-safety trade-off in autonomous navigation. By planning an exploratory trajectory that pushes the speed envelope and a backup trajectory that guarantees safety, SUPER can achieve high-speed flight (demonstrated speeds exceeding 20 meters per second) without compromising on collision avoidance. If you've been tracking the progress of autonomy in aerial robotics and matching it to the winning strategies emerging in Ukraine, it's clear we're likely to experience another ChatGPT moment in this domain, very soon. #LiDAR scanners will continue to get smaller and cheaper, solid state VSCEL based sensors are rapidly improving and it is conceivable that vehicles with this capability can be built and deployed with a bill of materials below $1000. Link to the paper in the comments below.

Still confused about lift? So is science. A short paper by Holger Babinsky (Cambridge) reminded me how complex — and beautiful — wings really are. If you think wings work because “air goes faster over the top and creates lift,” you’re not alone. It’s the standard classroom story — and it’s wrong. 📄 I recently read a short and very honest guide by Holger Babinsky (from Cambridge’s outreach series) titled “How Wings Work”. And it opens with this brutally honest statement: “There is no single simple explanation of how a wing works.” That hit me hard — and made me realize how much of engineering is about modeling, not magic. Here’s what the article (and modern aerodynamics) actually say: ✅ The “equal transit time” theory is false. NASA disproved it in 2003. Air over the top of the wing doesn’t “catch up” with the air below. It gets there faster. So that logic falls apart. ✅ Bernoulli’s principle isn’t enough. It assumes frictionless, steady flow. Real air is sticky, swirls, separates, and breaks rules. You need Navier–Stokes equations — which, fun fact, have no general solution. ✅ Newton’s third law does apply. Wings push air downward, and the air pushes back upward. The lift is partly due to momentum change, not just pressure. ✅ Vortices matter — a lot. Especially at the wingtips. Small drones (under 1 meter wide) use vortex lift almost entirely. For them, traditional airfoil theory doesn’t work. ✅ Small aircraft don’t follow big-airplane rules. If your wing is under 5 meters and you fly under 100 km/h, you’re in a 3D vortex-driven regime. Classic NACA airfoils? Not optimal anymore. What I loved about Anderson’s short paper is that it respects the complexity. It doesn’t dumb things down. Instead, it says: “Here’s what we do know. And here’s what we don’t.” That kind of honesty is rare — and useful. What this means for students (and future engineers): It’s okay to not have a “perfect answer.” Engineering is about tradeoffs and models. Don’t memorize myths — question them. Lift comes from circulation, momentum change, pressure gradients, and vortex systems. You can’t reduce it to one sentence. Use the tools: CFD, wind tunnels, experiments. Even NASA still does all three. P.S. Want to design better drones or VTOLs? Start by forgetting textbook aerodynamics. Build your own models for small-scale, low-speed, high-vortex flight. Not a prescription or recommendation — just data and perspective. You are responsible for your own decisions. #engineeringstudents #aerodynamics #aircraftdesign #fluiddynamics #cfdsimulation #howwingswork #johnanderson #cambridgeengineering #startuplearning #vtolflight #dronedesign #learningbydoing

Flying Without GPS: How UAVs Are Evolving in Denied Environments As GPS becomes increasingly vulnerable to jamming and spoofing, the future of UAV operations depends on how well these systems can navigate without it—or how creatively we can maintain access to reliable positioning. From military missions in contested zones to commercial drones in urban airspace, GPS-denied environments are now a defining challenge. The next generation of UAVs must be resilient, autonomous, and capable of navigating blind—or connected. Here’s where I see innovation accelerating: 1. Visual Odometry & SLAM Computer vision techniques like SLAM (Simultaneous Localization and Mapping) allow drones to map and localize in real time using onboard cameras and sensors. 2. Inertial Navigation Systems (INS) Accelerometers and gyros track motion—critical for short-term navigation, especially when paired with visual systems to correct drift. 3. Terrain Referenced Navigation (TRN) By comparing radar or LiDAR profiles to known maps, UAVs can position themselves even without satellite signals. 4. Magnetic & RF Mapping Some systems leverage Earth’s magnetic anomalies or ambient RF signals (Wi-Fi, cellular, broadcast) for passive, resilient positioning. 5. Fiber Optic Cable Integration Ground-based UAVs or command relay systems can stay connected to GPS-time and positioning data through secure fiber optic links. In some scenarios—such as perimeter surveillance or fixed-wing UAV launch zones—tethered UAVs or systems with partial autonomy can use high-speed fiber to maintain real-time PNT data, bypassing jammable satellite links altogether. 6. Multi-Modal Autonomy The most robust systems blend all of the above: vision, RF, terrain, inertial, and even fiber-connected nodes—cross-checking data with onboard AI to adapt in real time. Why It Matters: In defence, drones must survive in electronic warfare environments. In commercial use, they must operate safely in complex, signal-degraded spaces. From air to ground, the push for resilient, redundant navigation is accelerating—and fiber-based links are now part of the solution. The ability to operate in or around GPS-denied zones isn’t a luxury—it’s fast becoming a baseline requirement for UAV autonomy and survivability. Question.... Which navigation method do you see scaling fastest—vision-based, RF, terrain, tethered fiber, or something else? #UAV #DefenseTech #GPSDenied #FiberOptic #DualUse #Navigation #Drones #Aerospace #PNT #AI

"The U.S. Marine Corps has begun flying its stealthy Kratos' XQ-58A Valkyrie drones, making it the second known operator of the type beyond the U.S. Air Force. The Marines now plan to evaluate the drones as highly autonomous surveillance and reconnaissance assets, electronic warfare platforms, and wingmen for crewed fighters, including in kinetic roles. The first flight of a Marine XQ-58 took place on October 3, the service announced today. The drone was launched from Eglin Air Force Base in Florida. The test flight was conducted in cooperation with the Air Force's 40th Flight Test Squadron, part of the 96th Test Wing at Eglin, and the U.S. Navy's Naval Air Warfare Center Aircraft Division (NAWCAD), part of Naval Air Systems Command (NAVAIR). The Office of the Undersecretary of Defense for Research and Engineering, or OUSD(R&E), has also been involved in the Marine Valkyrie program. As can be seen in the video above, the XQ-58 is a completely runway-independent design that uses a rocket-assisted takeoff method via a static ground-based launcher. The drone uses a parachute recovery system to get back on the ground, with inflatable airbags helping to cushion it when it touches down. According to Kratos, the XQ-58, with its 30-foot overall length and 27-foot wingspan, has a maximum range of around 3,000 miles and a maximum launch weight of 6,500 pounds (including up to 600 pounds in its internal payload bay and/or another 600 pounds under the wings). It has a subsonic cruising speed of Mach 0.72 and can hit an absolute top speed of around Mach 0.85. "This XQ-58A test flight and the data collected ... not only help to inform future requirements for the Marine Corps," Scott Bey, a prototyping and experimentation portfolio manager at OUSD(R&E), said in a statement about the October 3 sortie. “It fuels continued joint innovation and experimentation opportunities and demonstrates the agility that can be achieved through partnership.” https://lnkd.in/e9FUEKYh

A chief engineer reached out to us today & this was top of mind for new capabilities he needs: "Modeling families of air vehicles to varying missions, Automation of performance analysis, trade studies, multi-disciplinary optimizations including cost, Design automation direct from requirements." Here's what's interesting about that list: each item forces a tradeoff: do you go low-fidelity and fast, or high-fidelity and slow. Neither option is good. You can definitely go fast drawing up quick planforms or tubes with wings, but will the design close when trying to integrate all of the real stuff? Usually you need a high-fidelity CAD model to know this, but by the time it's modeled up and nothing fits, it's too late. Higher-fidelity parametric models break when flexed, even undergoing small changes like changing the leading edge angle I've seen cause errors. Faster speed only reinforces the Lock-In Trap. Teams freeze architecture early because exploring alternatives feels too slow, and end up over many month- long cycles trying to close out the design, possibly one that might not close. Next week, he'll sit with an nTop engineer to go through a workflow that shows exactly what he's asking for: 1) UAV family modeling: Fully parametric models that never break when you change parameters. Build once, scale across your entire family. 2) Performance analysis automation: Embedded analysis (LBM, AVL/XFOIL, DATCOM, SUAVE integration) gives instant performance feedback as you modify geometry. No export workflows. 3) Trade studies & MDO: Generate hundreds of variants automatically, all simulation-ready. Zero geometry failures in optimization loops. 4) Requirements to design: Encode mission requirements directly into parametric logic that drives geometry generation. The programs that win will be the ones that stop accepting the speed vs fidelity tradeoff. If you're dealing with the same constraints, DM me.

A low-altitude economy could be very interesting for archipelago nations of Southeast Asia with thousands of far flung islands, eg Indonesia. These drones will likely fly on battery electric which means the grid needs to be upgraded at the designated charging points. Not to mention the increased amount of renewables to supply the green electricity. "China flew its biggest-yet unmanned cargo aircraft designed for civilian use, as the world’s top drone-making nation steps up test flights of unmanned aerial vehicles (UAVs) that could ultimately ferry everything from takeaways to people. Packing a payload capacity of 2 metric tonnes, the twin-engine aircraft took off on Aug 11 on an inaugural flight for a trip of about 20 minutes in south-western Sichuan province, state media said, citing developer Sichuan Tengden Sci-tech Innovation. With a wingspan of 16.1m and a height of 4.6m, the aircraft, built entirely by government-funded Tengden, is slightly larger than the world’s most popular light aircraft, the four-seat Cessna 172. China's civilian drone makers are testing larger payloads as the government pushes to build a low-altitude economy, with the aviation regulator seeing a 2 trillion yuan (S$369 billion) industry by 2030, for a four-fold expansion from 2023. Tengden’s test flight followed the maiden flight in June of HH-100, a cargo drone developed by Aviation Industry Corp of China (AVIC) with payload capacity of 700kg and a flight radius of 520km. In 2025, AVIC plans to test its biggest cargo drone, the TP2000, which can carry up to 2 tonnes of cargo a distance of 2,000 km. In a report in 2024, the government identified the low-altitude economy as a new growth engine for the first time, with vertical mobility seen as a “new productive force” in areas such as passenger transport and cargo deliveries. In April, aviation authorities issued a production certificate to UAV maker EHang Holdings, based in the southern city of Guangzhou, for its passenger-carrying drone, China’s first such document for an autonomous passenger drone. A month later, cargo drone firm Phoenix Wings, part of delivery giant SF Express, started delivering fresh fruit from the island province of Hainan to southern Guangdong, using Fengzhou-90 drones developed by SF, a unit of S.F. Holding." https://lnkd.in/gQB3fzGr

𝗗𝗿𝗼𝗻𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗳𝗹𝗶𝗴𝗵𝘁 — 𝗶𝘁’𝘀 𝗮 𝗳𝘂𝗹𝗹 𝗲𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺. Behind every stable flight is a system designed to survive gravity, vibration, packet loss, and sensor noise in real time. 𝗖𝗼𝗿𝗲 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗕𝗹𝗼𝗰𝗸𝘀 𝗶𝗻 𝗮 𝗗𝗿𝗼𝗻𝗲: 💠Flight Controller (MCU/RTOS-based). 💠Sensor Fusion (IMU, GPS, magnetometer). 💠Motor Control (PWM, ESC, PID loop). 💠Communication Module (RF/LoRa/4G). 💠Failsafe Systems (GPS lock, altitude failback, return-to-home). 💠Power Monitoring (LiPo battery sensing + protection logic). 🔺Challenges in R&D: ✳️Tuning PID in unstable wind. ✳️Syncing ESCs with minimal jitter. ✳️Dealing with brownout resets in mid-air. ✳️Latency in live video + command feedback. ✳️EMI from motors affecting IMU reads. ✳️Integrating AI at the edge. (target lock, tracking, collision avoidance). > “Building a drone isn’t just about flying-it’s about orchestrating dozens of real-time systems to keep flying.” #DroneDevelopment #EmbeddedSystems #RTOS #MotorControl #SensorFusion #FlightController #FirmwareEngineering #EdgeAI #PhDThoughts #LoRa #Quadcopters #PIDTuning #Embeddedc #Embedded #Linux #OS

We are excited to share our latest work on downwash modeling for drones, published in IEEE Robotics and Automation Letters! PDF: https://lnkd.in/dd8TEYkH Video: https://lnkd.in/dydmArdf We present a computationally efficient model for estimating the far-field airflow caused by quadrotors in hover and slow flight. This is important as drones are becoming integral to applications from agriculture to public safety, and understanding the aerodynamic disturbances is critical. We show that the combined airflow from quadrotor propellers can be well approximated as a turbulent jet beyond 2.5 drone diameters below the vehicle. Our model relies on classical turbulent jet theory, which removes the need for expensive CFD simulations. We also demonstrate the model's effectiveness in multi-agent scenarios, reducing altitude deviations by 4x when compensating for the downwash of another drone when passing below. Curious? Check out the paper! Reference: "Robotics meets Fluid Dynamics: A Characterization of the Induced Airflow around a Quadrotor" IEEE Robotics and Automation Letters, 2025 PDF: https://lnkd.in/dd8TEYkH Video: https://lnkd.in/dydmArdf Kudos to Leonard Bauersfeld, Koen Muller, Dominic Ziegler, Filippo Coletti! University of Zurich, UZH Innovation Hub, UZH Department of Informatics, European Research Council (ERC), AUTOASSESS, Switzerland Innovation Park Zurich

Transwing #drone design is a cruise-capable, transitioning eVTOL airframe – but the way it shape-shifts between hover and cruise modes makes it one of the smartest aircraft on the market. Equipped with the right sensors, unmanned aerial vehicles (UAVs) can help determine radiation levels after incidents in nuclear facilities as well as during routine monitoring. Over the last few years, drones have become a popular tool for a variety of applications at nuclear facilities, including both indoor and outdoor inspections and mapping. The industry is starting to trust unmanned aircraft systems (UAS) to improve safety for their workers, save time and cut costs—and that includes using the technology to detect radiation levels after incidents and during routine monitoring. Drones can help acquire radiation levels during routine monitoring and after accidents. “Using UAVs to detect radiation in a post-accident scenario would be extremely beneficial,” said Sam Johnson, technical leader—nuclear plant support for EPRI. “The systems would allow us to map the radiation levels within an area so we know where we can safely send personnel and where we can’t. Pterodynamics has built a number of prototypes, the current model being the X-P4, with a 13-foot (4-m) wingspan. It's capable of lifting 15 lb (6.8 kg) of payload at a maximum takeoff weight (MTOW) of 84 lb (38 kg), then covering 69 miles (111 km) of range in an hour of cruise speed before the battery runs out. In a sprint, it can do 115 mph (185 km/h) But it's the superb range that got the US Navy interested as an alternative for missions involving highly expensive helicopters to be scrambled with pilot and all. While the X-P4 is quite a hefty drone, needing two people to carry it, Pterodynamics is planning to go much bigger. The X-P5 will have a 22-ft (6.7-m) wingspan, and it's designed to carry 50 lb (23 kg) of payload up to 575 miles (925 km) using a hybrid power system. And the X-P6 will take things up another level with a turbogenerator powertrain, carrying 220 lb (100 kg) up to 978 miles (1,574 km) on a 30-ft (9.1-m) wingspan. Radiation detection and monitoring is just two of many drone applications the nuclear industry will continue to explore. The possibility of using drones to inspect a variety of assets to check for leaks, cracks and other issues is generating a lot of interest, as are more security-related applications that help keep these facilities and their workers safe. While a UAS certainly isn’t the only tool, it’s becoming a more effective tool that the nuclear industry is trusting more and more—a trend experts expect to continue as the technology evolves and employees become more comfortable operating these systems. #drones #uav #unmannedaerialsystem #nuclear #navy #radiation #ev #aerospace #newjersey #engineering #design #aviation #aircraft