Aerospace Engineering Flight Dynamics

The 'monocopter' is a type of #micro #aerial #vehicle (MAV) largely inspired from the flight of botanical samaras (Acer palmatum). A large section of its fuselage forms the single wing where all its useful aerodynamic forces are generated, making it achieve a highly efficient mode of flight. However, compared to a multi-rotor of similar weight, monocopters can be large and cumbersome for transport, mainly due to their large and rigid wing structure. Overall, the vehicle weighs 69 grams, achieves a maximum lateral speed of about 2.37 ms−1, an average power draw of 9.78W and a flight time of 16 min with its semi-rigid wing. In this work, a monocopter with a foldable, semi-rigid wing is proposed and its resulting flight performance is studied. The wing is non-rigid when not in flight and relies on centrifugal forces to become straightened during flight. The wing construction uses a special technique for its lightweight and semi-rigid design, and together with a purpose-designed autopilot board, the entire craft can be folded into a compact pocketable form factor, decreasing its footprint by 69%. The proposed craft accomplishes a controllable flight in 5 degrees of freedom by using only one thrust unit. It achieves altitude control by regulating the force generated from the thrust unit throughout multiple rotations. Lateral control is achieved by pulsing the thrust unit at specific instances during each cycle of rotation. A closed-loop feedback control is achieved using a motion-captured camera system, where a hybrid Proportional Stabilizer Controller and Proportional-Integral Position Controller are applied. #research #paper: https://lnkd.in/gbtUTExx #authors: Shane Kyi Hla Win, Luke Soe Thura Win, Danial Sufiyan, Shaohui Foong #robotics #engineering #quadcopter #drones #innovation #technology

Frequency Escalation in UAV Systems – Transmissions in the 7.5–12 GHz Band Recent observations indicate a clear upward shift in the radio spectrum used by unmanned aerial systems (UAS). Traditional ranges for command and video links — 300 MHz to 7.2 GHz — are now heavily saturated. Consequently, more UAVs are operating within the 7.5–12 GHz band, entering the centimeter-wave (SHF) domain rarely used by small and medium-class drones. Field reports confirm analog video transmitters above 8 GHz, marking a significant departure from the standard 2.4 GHz and 5.8 GHz bands. Operating higher enables avoidance of interference and greater data throughput, especially for HD and 4K video with minimal latency. This, however, demands high RF precision and antenna stability, as even minor detuning degrades link performance. Frequencies above 7 GHz mean shorter wavelengths, faster attenuation, limited obstacle penetration, and strict line-of-sight requirements. Maintaining stable connections requires high-gain directional antennas, increased transmitter power, or airborne relay UAVs to sustain long-range links despite terrain masking. Operation in the 8–12 GHz range allows wider bandwidth and lower latency but requires advanced RF filtering, thermal stabilization, and high-linearity amplification (LNA/PA). This raises system complexity while reducing detectability. Most current detection and counter-UAS (C-UAS) systems cover up to ~7 GHz. Thus, new UAVs may operate beyond detection. Analog modulation at these frequencies generates non-standard spectral signatures not recognized by common RF classification algorithms. To adapt, infrastructures must expand spectrum monitoring to at least 12 GHz, update RF signature libraries, upgrade analyzer firmware, and test jamming effectiveness in the 8–12 GHz range. The ongoing upward shift in UAV frequencies marks a new phase in unmanned architecture, emphasizing adaptability, dynamic channel allocation, and resilience in contested electromagnetic environments. The spectrum itself has become a battlefield — one where superiority depends on intelligence, agility, and precise spectrum management.

Flight dynamics in Python with Archimedes! In a new series we walk through implementing 6dof flight dynamics using the subsonic F-16 benchmark. The implementation uses hierarchical, multi-fidelity modeling and the spatial mechanics primitives for rigid body dynamics. The trajectory in the gif has tabulated aerodynamics, NASA turbofan model, rate-limited control surfaces, USSA1976 atmosphere, and... constant gravity. No sensor models (yet).  It runs on a laptop at ~8000x realtime using the SUNDIALS interface for adaptive ODE solving. The whole thing is implemented in Archimedes + NumPy, so the entire model (plus controllers and filters, coming soon) is also compatible with C code generation for real-time simulation, HIL testing, and embedded deployment using CasADi's computational graphs. An RK4 step takes ~380 µs on a Cortex M7, enabling 1+ kHz hard real-time running on bare metal. Check out the tutorials and the source code on GitHub! https://lnkd.in/e_74JVU4 (tutorial series) https://lnkd.in/ecV6nHdk (source code) #Archimedes #CasADi #ControlSystems #EmbeddedSystems #Python #OpenSource #Aerospace

What happens when a stealthy flying wing meets tight stability margins, and flight controls built on #imperfect #models⁉️ By the mid-1990s, unmanned aircraft were already well understood from a flight mechanics standpoint, with programs such as the D-21 having demonstrated high-altitude unmanned flight decades earlier. What RQ-3 #DarkStar set out to explore was more demanding: the integration of low observability 📡, long endurance, and a high degree of automation within a flying-wing configuration, where stealth, aerodynamic coupling and #limited #control #authority dominate the design space. Technically, DarkStar relied on an unconventional airframe optimized for radar signature reduction, supported by fully automatic flight from takeoff to landing, satellite command and control data links, and sensor integration under severe size and weight constraints. These choices pushed the vehicle into take off regime where aerodynamic #model #fidelity and #robust #control law design were not just important, but mission-critical. The loss of the prototype in April 1996 exposed the consequences of getting that balance wrong. Shortly after rotation, the aircraft pitched up aggressively, entered a stall, and crashed, not due to structural or propulsion failure, but because the flight control laws, modified shortly before flight, were insufficiently robust to modeling errors in the low-speed, high-angle-of-attack regime of a flying wing, leading to an unstable airframe–control interaction. Although later redesigns improved stability, the program was cancelled in 1999 as costs rose, performance fell short, and alternative platforms such as Global Hawk demonstrated greater robustness and operational margin. DarkStar’s lasting value lies in its technical lessons: controllability and model accuracy are as decisive as stealth, with early failures often shaping the control and verification philosophies of aeronautical programs. #avgeek #control #modelbaseddesign

I recently reviewed a detailed technical report analyzing Iranian Unmanned Aerial Vehicles (UAVs) and guided munitions being used by Russia in the ongoing war. The document covers key systems, including the Shahed-129, Shahed-191, Mohajer-6, Mohajer-4, and the Shahed-136 "kamikaze" drone, as well as their associated precision-guided munitions. The report provides: ·       In-depth specifications and operational roles of each UAV ·       Strengths and vulnerabilities, including limitations in weather and susceptibility to air defenses ·       Methods for detection and counteraction, from electronic warfare to anti-aircraft systems ·       A technical teardown of the Shahed-131 (precursor to the Shahed-136), highlighting its construction, navigation, and control systems ·       Visual guides and comparison tables for field identification Significance: The last few days have seen a higher-than-average deployment of some of these systems against Ukrainian cities. The report outlines the capabilities and limitations of these Iranian drones but also outlines practical countermeasures, supporting efforts to protect critical infrastructure and adapt to evolving threats on the modern battlefield. If you work in defense, security, or related fields, this document is a must-read for understanding the rapidly changing landscape of UAV warfare and the specific challenges posed by Iranian-supplied systems.

What if we’ve been optimising drones in the wrong direction? For years, the logic was simple: Add weight → lose efficiency. Lose efficiency → lose range. Then EPFL built 𝐑𝐀𝐕𝐄𝐍. It's a 620g fixed-wing drone. And 230g of that weight is legs. Legs that walk, hop over obstacles, and jump into flight without a runway or catapult. The part that forces a reset is:  Jump takeoff is reported to be 𝟏𝟎𝐱 𝐦𝐨𝐫𝐞 𝐞𝐧𝐞𝐫𝐠𝐲 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐭𝐡𝐚𝐧 𝐚 𝐬𝐭𝐚𝐭𝐢𝐜 𝐥𝐚𝐮𝐧𝐜𝐡. The legs aren’t extra mass. They’re stored energy, released intelligently. Most UAV design has optimised for airborne purity such as lighter frames, cleaner aerodynamics, longer uninterrupted flight. But real environments aren’t pure. Forests don’t offer launch strips. Urban debris doesn’t provide smooth clearings. Disaster zones don’t cooperate with aerodynamics. RAVEN signals something bigger: It forces us to think optimising only for flight. And start focusing on transition. A drone that can: Land anywhere Reposition by walking instead of hovering Conserve battery while stationary Relaunch without external systems isn’t just an aircraft. It’s an adaptable mobility platform. And that matters. Because the next frontier of autonomy is about environmental versatility. Future operations from search and rescue to infrastructure inspection to defense deployments, will demand systems that operate across surfaces, not just above them. A swarm that can perch, move on ground, conserve power, and relaunch behaves differently from one forced to stay airborne. We’ve treated weight as inefficiency. Maybe some weight is capability. The breakthrough won’t always come from removing mass. Sometimes it comes from giving mass a purpose. #Drones #Robotics #AerospaceEngineering #AutonomousSystems #Innovation #FutureTech

⚠️ NOT EVERY UAV IS BUILT FOR THE SAME WAR... One of the biggest misconceptions in the drone debate is treating all UAVs as if they solve the same problem. In reality, different platforms exist because operational requirements are fundamentally different. 🛩️ FIXED WING SYSTEMS PRIORITIZE RANGE AND ENDURANCE. They are optimized for ISR, surveillance, mapping, border monitoring, and long duration missions. Their strength is efficiency over distance, but they usually require more space, infrastructure, and operational planning. 🚁 MULTIROTOR PLATFORMS PRIORITIZE FLEXIBILITY. They dominate inspection, logistics, tactical reconnaissance, urban operations, and short range precision tasks. They are highly maneuverable and easy to deploy, but limited in endurance and range. ⚙️ VTOL HYBRID SYSTEMS TRY TO COMBINE BOTH WORLDS. These systems are becoming increasingly important because they combine vertical takeoff capabilities with the efficiency of fixed wing flight. Especially in logistics, military mobility, and remote area operations, this category is gaining significant relevance. 🔥 FPV SYSTEMS CHANGED THE MODERN BATTLEFIELD. Originally rooted in racing communities, FPV drones have evolved into highly agile and low cost tactical systems. Their speed, maneuverability, and adaptability created entirely new operational dynamics in reconnaissance and strike missions. 🧠 THE REAL SHIFT IS HAPPENING AT THE SYSTEM LEVEL. The future is no longer about individual drones alone. It is about autonomous coordination, swarm logic, AI supported mission planning, sensor fusion, and scalable man machine teaming. A single drone can provide information. A connected ecosystem creates operational advantage. 🚀 The important question is no longer whether autonomous systems will shape the future. The question is how fast organizations can adapt their structures, doctrine, training, and decision making to integrate them effectively.

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

The Nature Portfolio journal on Robotics just published our article on estimating and controlling a drone’s flight attitude purely based on vision 👁 In order to fly, both #drones 🚁 and #insects 🐝 have to estimate and control their “attitude”, i.e., the angle they make with respect to gravity. Drones typically estimate their attitude with the help of tiny sensors that measure accelerations. In contrast, in insects no specific sense of acceleration has been found. So how do insects find the gravity direction? In 2022, we showed in an article in Nature that attitude can be estimated by combining #optical #flow with a #motion #model. Optical flow is the motion we perceive visually when moving ourselves or looking at dynamic objects in our environment 👀🏃♀️. A motion model captures what happens when we take actions 🦾. For instance, it can model that to accelerate sideways, a helicopter-like drone has to tilt to the side. At the time, we demonstrated the theory with flying robot experiments 🤖. However, we always needed to include rotation rate measurements from the gyros. There is a rough equivalent for gyros in nature: the “halteres” of Dipterans such as mosquitoes 🦟. However, not all insects have such halteres. Moreover, the theory predicted that gyros are not necessary for attitude estimation. In our new article, “All eyes, no IMU: learning flight attitude from vision alone”, we train neural networks 🧠 to estimate the attitude and rates purely based on vision. In particular, we used event-based cameras 🎥 These cameras do not capture image frames at a fixed rate, but have each pixel transmit an “event” when its brightness changes. This results in a very low latency and high dynamic range. Our study showed that neural networks with memory were able to successfully map incoming events to the drone’s attitude, allowing for closed-loop, onboard attitude control. An investigation of the neural networks revealed that they exploit not only motion cues but also “pictorial” cues 🖼 – in this case the edges of the ground surface in the border of the field of view. Excluding the borders led to slightly lower performance in the environment used for training, but generalized better over different environments. The developed method is promising for insect-sized flying drones (~100mg), as it allows for an even smaller sensory package. Finally, it raises interesting questions about how insects would use and fuse visual and other sensory information for estimating their attitude. Congratulations to Jesse H. and Stein Stroobants for their enormous efforts to make this happen, and co-author Sander Bohté for the great collaboration. A big thank you to the organizers of the special collection at npj Robotics, Nitin J Sanket and Alessio Franci. Finally, we are grateful for the funding by the research Air Force Office of Scientific Research (AFOSR) and the NWO (Dutch Research Council), in the context of the Dutch Research Agenda (NWA).  

How DJI's decade-long drone dominance came from a single research thesis. Here's what stood out from DJI's CEO Frank Wang's thesis: The breakthrough wasn't just academic theory - it solved helicopters' fundamental instability problem. Like balancing a pencil on your fingertip while walking, helicopters require constant correction to stay airborne. Traditional helicopter control demanded unique mathematical models custom-built for each aircraft. Imagine needing separate software for every smartphone instead of one universal OS. Instead, design adaptive feedback loops using real-time sensor data for continuous micro-adjustments. The system checks position 50 times per second and makes immediate corrections. No complex physics calculations—just continuous micro-adjustments based on sensor feedback. This approach resembles how we intuitively balance on bicycles without conscious mathematical thought. The control architecture featured three interlinked systems: • Hovering control (maintaining position) • Semi-auto flight (velocity commands) • Ground station navigation (following waypoints) Real-world results were impressive even by today's standards: • Hovering accuracy within 0.18m • Navigation over 7.8km courses • Velocity deviations under 0.25m/s Perhaps most innovative was the auto-tuning system using frequency analysis. Similar to how smartphones auto-adjust camera settings, the control system calibrates itself. This allows drones to adapt to different aircraft characteristics without user intervention. Safety features became DJI's competitive moat: • Fail-safe protocols for communication loss • Indoor stabilization without GPS • Vibration isolation for sensor protection In my work with autonomous systems at Microsoft, I saw how these principles transformed multiple industries: • Real-time mapping • Aerial photography • Infrastructure inspection DJI leveraged these principles to develop: • RTK modules for precision positioning • Advanced mapping capabilities • Efficient data processing with minimal computational resources Manufacturing expertise amplified the technical advantages. DJI leveraged China's dominance in plastics, small motors, and high-volume electronics production. The combination of advanced control systems with manufacturing scale created a dominant market position. This research marks the inflection point when drones transitioned from specialist military tools to consumer devices. Similar to how graphical interfaces democratized computing beyond programmers to everyday users. Urban air mobility and flying taxi projects now build directly on these control principles. They've evolved with better hardware and additional redundancies for human transport. The most successful technologies become invisible infrastructure we take for granted. What began as research on helicopter stability created accessible flying devices for everyone. More insights on AI, robotics and aviation on my page.