flyby racing

🦉SPRIND Project Demo Days - Stage 1 Demonstration We are a team from TU Wien’s Cyber-Physical Systems Group working on our drone interception project, NoctuaNet, with members of Team Flyby also contributing to the project. Today, we demonstrated our system in front of the jury for Stage 1 of the Anti-Drone Response Project. We were the last team of the day, flying between 17:00 and 18:00, and definitely had some of the most challenging weather conditions. Wind reports showed around 6 m/s continuous wind and gusts up to 18 m/s, which was a real challenge for our drones. In the video, you can clearly see them fighting against the wind throughout much of the flight. On top of that, we also had rain during the demonstration. But even with the wind and rain, our interceptor drone was able to complete its mission. We are happy to share a video of our work and hope to continue developing NoctuaNet in the future. #SPRIND #AntiDrone #CounterUAS #Drones #Robotics #AutonomousSystems #TUWien #CyberPhysicalSystems #TeamFlyby

🛡️ What does Team Flyby work on when there are no races coming up? As it turns out: still fast autonomous drones, just with a different objective. We are currently developing a system for SPRIND’s Anti-Drone Response 2.0 challenge, and next week we will demonstrate our progress during the Stage 1 evaluation. The video shows an intercept performed by our system during development tests. Our approach combines portable ground stations with PTZ cameras and an autonomous interceptor drone. Once an aerial object is detected, the cameras zoom in and classify it. If a drone is identified, observations from multiple cameras are combined to estimate its position and velocity, even for a target measuring approximately 20 × 20 cm. The interceptor is then guided towards the target while remaining below it and searching with its onboard camera. Once it detects the drone, the final stage of the intercept is performed autonomously using only onboard perception and processing. There is still plenty of work ahead, but we have implemented the core capabilities we set out to demonstrate during Stage 1. We hope to continue developing the system and push it towards higher speeds, longer ranges, and more demanding scenarios. We are looking forward to next week’s demonstration of the project 𝐍𝐨𝐜𝐭𝐮𝐚𝐍𝐞𝐭! #SPRIND #AntiDrone #CounterUAS #AutonomousSystems #ComputerVision #Robotics

🏁 Roboracer at ICRA 2026 - race week is over 🏁 Team Flyby finished 6th overall (of 36 teams) at the Roboracer competition at ICRA 2026 in Vienna. For us, this result means a lot: to our knowledge, it is the highest placement so far for a reinforcement learning team in the competition, and the first entry using a fully end-to-end RL approach. We kept improving until the final day, collecting data on the real track, refining our simulation, and updating the model parameters step by step. Along the way, we also improved our inference pipeline, with our feedforward controllers now running in just 28 microseconds on the CPU. We are especially proud of our recurrent policies, which learned to estimate opponent position and speed relative to the ego vehicle. This led to much better obstacle avoidance and overtaking behavior, including the overtake shown towards the end of the video, although we still have to tune down the agents aggressive driving style.... It was an intense, and very rewarding week for the whole team consisting of: Joel Klimont, Jakob Buchsteiner, Moritz Taferner, and three students from the robotics club robo4you. A huge thank you to the organizers for putting together such a great event, and to our professors Radu Grosu and Ezio Bartocci for their continued support! This is only the beginning for our end-to-end RL racing stack! 🚀 ⚡ Fun fact: getting these agents race-ready took around 100.4 kWh of GPU energy, including testing, failed runs, and all the iterations in between. #Roboracer #F1TENTH #ICRA2026 #AutonomousRacing #ReinforcementLearning #Robotics #TUWien #Autonomy

🏁 One week until Roboracer at ICRA 2026 🏁 With only one week left until the Roboracer competition at ICRA 2026, we are excited to share another update from Team Flyby. Over the past two weeks, our focus has been on closing the gap between simulation and reality. We refined our vehicle model, improved the low-level hardware integration, and continued tuning the motor control stack. At the same time, our RL pipeline also received several important upgrades. One key improvement was the addition of IMU and gyro observations to the simulator. We also started modeling lidar tilt during dynamic maneuvers. This turned out to be important in fast corners, where the lidar can briefly tilt beyond the track boundaries and produce observations that differ significantly from the idealized simulation. The result is an agent that is becoming noticeably more robust at higher speeds. In the video, we show one of our latest policies pushing the current vehicle model close to its limits, including some drifting behavior in several corners. What is especially interesting to us is how compact the final policy still is. The agent uses only a 120-point lidar scan at 10 Hz, gyro measurements from the IMU, and speed feedback from the ESC RPM sensor. There is no particle filter or external state estimator running for this policy. The neural network itself is a small two-layer policy with 64 and 32 neurons. It runs at 100 Hz onboard our Jetson, with an inference time of around 0.2 ms. Training this agent took about eight hours on an RTX 4090, and the drifting behavior only started to emerge after roughly 40 billion environment steps. Of course, there are still limitations. One example is the only right-hand corner on our current test track, where the agent is still less confident and slows down more than expected. Since most corners on the track are left-hand turns, the policy has developed a clear bias. We are now training agents in both driving directions to reduce this left/right imbalance. Next week will show how well this transfers to the competition environment. Stay tuned for the final push towards ICRA 2026! #Roboracer #F1TENTH #ICRA2026 #AutonomousRacing #ReinforcementLearning #Robotics #TUWien #CarRacing #Autonomy

🏁 One week closer to Roboracer at ICRA 2026 🏁 One week after our first end-to-end policy deployment, we have made good progress on both speed and stability. The car is now driving at up to 3.5 m/s on our lab track, and our RL framework CaRL now reaches around 11.5 million simulation steps per second. This week was mainly about improving the existing codebase and tightening the software integration. We fixed several issues in the lidar node and added a more realistic simulation of the lidar scan timing. Our lidar runs at 10 Hz, which means a full scan takes around 100 ms to complete. At higher speeds, this becomes very relevant: the scan is not an instantaneous snapshot of the environment, but is time-distorted as the car moves while the scan is being captured. This mismatch between simulation and reality confused the RL agent, especially in faster sections of the track. To address this, we now simulate the scan timing directly. With simple interpolation at each simulation step, we can efficiently compute each ray using a lookup in the precomputed scan table, without requiring live ray tracing. This keeps the simulation fast while making the observations much closer to what the real car sees. It is exciting to see the system becoming faster, more stable, and more realistic week by week. Stay tuned for more updates from Team Flyby! #Roboracer #F1TENTH #ICRA2026 #AutonomousRacing #ReinforcementLearning #Robotics #TUWien #Autonomy

🏁 Early steps towards Roboracer at ICRA 2026 🏁 While Formula 1 is racing in Miami this weekend, our focus at Team Flyby is on a smaller but fully autonomous racing platform: preparing for the Roboracer competition, formerly F1TENTH, at ICRA 2026 in Vienna. After focusing on autonomous drone racing last year, we are now exploring how our experience with high-speed 3D autonomy can transfer to ground-based racing. Our car is fully assembled and features an LD19 lidar, a Hobbywing XeRun 4268 G3 motor, and an XB8E 2026 chassis. For low-level control, we are using our Betaflight fork “Weitflug”, originally developed for our drone racing work and now adapted for the car. This allows us to reuse parts of our existing tooling, including 1 kHz IMU streaming, RPM filtering and many other features. Last week, we started testing our first full-stack ideas on the real vehicle. Using our reinforcement learning framework, currently called CaRL, we simulate around 3.5 million steps per second on a 5070 Ti GPU, including lidar rays, odometry, and an adapted single track model from TU Munich. We also deployed our first end-to-end policy on the real car, driving at 1 m/s on a complex test track in our lab at TU Wien. This is still an early result, but an important step as we move from hardware integration towards system-level testing. We are looking forward to sharing more insights throughout the coming month. #Roboracer #F1TENTH #ICRA2026 #AutonomousRacing #ReinforcementLearning #Robotics #TUWien #Autonomy

🏆🏆🏆 We did it! First place in the silver multi-drone race goes to flyby racing! After months of long nights and hard work, we were the fastest in our category. Thanks to all people involved and for the support from Technische Universität Wien and Posedio GmbH. Without them our journey wouldn't be possible. Stay tuned for a longer recap of the race and our technology.

Update from the drone race track at the AbuDhabi Autonomous Racing League. We are currently ranked 5th in the speed race and scored 15 points in the multi-drone race (tier 2). Here's a short run from the multi-drone race, where we managed to knock out an opponent. Stay tuned for the final update tomorrow! #A2RL #DroneRacing #TUWien

🚀🚀🚀🏁🏁🏁 We are officially ready for the finale of the AbuDhabi Autonomous Racing League Tomorrow is the final day we've been preparing for over the last year. May the best team win the race! Stay tuned for more updates from the track. 🚀🚀🚀

🏁 Qualified for the Finals 🏁 We’re back from the second A2RL Qualification in Abu Dhabi - and we’re proud to share that we qualified for the finals in January. This round pushed our system harder than ever, and we’re thrilled with the progress our controllers and vision models showed under real racing conditions. Here are a few highlights from the week - moments that capture both the challenges and the breakthroughs on our path to fully autonomous flight. We’re now shifting focus toward preparing for the A2RL Finals in January. Follow us here to stay up to date as we take the next step toward high-speed autonomous racing. A huge thank you to Posedio for making this journey possible. Hashtag #A2RL #DroneRacing #TUWien