Awesome Autonomous Drone Racing

A curated personal list of resources for autonomous drone racing competitions, including AI Grand Prix, A2RL, and AlphaPilot.

Contents

• Competitions
• Simulators
• Frameworks & Libraries
• State Estimation
• Datasets
• Papers
• Tutorials & Courses
• Hardware
• Community
• AI Research
• Experimental & Emerging

Competitions

Active

• AI Grand Prix - $500K autonomous drone race conceived by Anduril's Palmer Luckey, run with the Drone Champions League on identical Neros Technologies drones (software only, no human pilots). Inaugural 2026 season: virtual qualifiers (May–July), a Southern California physical qualifier (September), and finals in Ohio (November).
• A2RL - Abu Dhabi's ongoing autonomous racing league (ASPIRE), spanning autonomous cars and a vision-only drone championship (single forward-facing RGB camera + IMU; LiDAR/stereo/GPS prohibited). Its 2025 drone race was the first event where AI beat human champions (won by TU Delft); the most recent edition, the Jan 2026 Drone Championship (UMEX), saw FPV world champion Minchan Kim narrowly beat the autonomous drone while TII Racing set the fastest AI lap (12.032s).

Historical

• Purdue Autonomous Drone Racing - Indoor fixed-wing pylon racing with simulation-to-real format. Competition held December 2025.
• 27th Roboracer Autonomous Grand Prix @ ICRA 2026 - 1:10 scale autonomous racing, held at ICRA 2026 (VIECON, Vienna, June 1–5 2026).
• AlphaPilot - Lockheed Martin AI drone racing challenge (2019). Over $2M in prizes, won by TU Delft MAVLab.
• Game of Drones - NeurIPS 2019 competition using Microsoft AirSim. Framework still available for benchmarking.
• IROS Autonomous Drone Racing - IROS 2018 competition won by UZH-RPG.

Simulators

GPU-Parallel & Differentiable

• Aerial Gym Simulator - Isaac Gym with GPU-parallelized geometric controllers supporting 100,000+ parallel drones. IEEE RA-L 2025.
• Crazyflow - GPU-accelerated, differentiable Crazyflie simulator in JAX with MuJoCo (MJX) physics and rendering for massively-batched single-drone and swarm RL and sim-to-real.
• DiffAero - GPU-accelerated, fully differentiable quadrotor simulator with parallel physics/rendering, depth and LiDAR sensors, RL and differentiable-physics training, and a dedicated time-optimal gate-racing task.
• Isaac Drone Racer - RL framework for autonomous drone racing on NVIDIA Isaac Lab with rotor/drag dynamics, attitude/rate control, a simulated fisheye camera and IMU, dynamic track generation, and parallel training via skrl.
• Isaac Gym - NVIDIA's GPU-accelerated physics simulation for RL training. Now legacy; superseded by Isaac Lab (built on Isaac Sim).
• OmniDrones - Isaac Sim 4.1.0 with multi-rotor RL environments. IEEE RA-L 2024. No longer actively maintained.
• Pegasus Simulator - Isaac Sim extension with native PX4/ArduPilot and ROS2 support. Photorealistic environments.

Traditional Physics Engines

• Agilicious - Complete quadrotor hardware/software stack from UZH-RPG. Demonstrated 5g maneuvers at 70 km/h.
• AirSim - Microsoft's Unreal Engine-based simulator with drone racing environments. Development frozen; succeeded by the commercial Project AirSim.
• AirSim Drone Racing Lab - Competition framework built on AirSim for the NeurIPS Game of Drones. Archived (read-only) since June 2026.
• Cosys-AirSim - AirSim fork updated for Unreal Engine 5.5 with GPU-LiDAR and new sensors. No longer actively maintained.
• CrazySim - First proper software-in-the-loop simulator for Crazyflie with Gazebo Sim/ROS2 integration. ICRA 2024.
• Elodin AI Grand Prix Harness - Open-source practice simulator for the AI Grand Prix, coupling deterministic GPU 6-DOF physics with a real Betaflight SITL flight controller over UDP, a spec-matched 640×360 FPV camera, and a 3-gate course with lap tracking.
• FalconGym - Photorealistic NeRF/Gaussian-splatting simulation for zero-shot sim-to-real vision-based racing-gate navigation; FalconGym 2.0 adds editable Gaussian-splat tracks. IROS 2025.
• Flightmare - UZH-RPG flexible simulator with Unity rendering, 200kHz physics, and OpenAI Gym API.
• FlightForge - UE5-based simulator with procedural environment generation for Sprin-D Autonomous Flight Challenge 2024.
• gym_multirotor - MuJoCo-based quadrotor environments compatible with stable-baselines3.
• gym-pybullet-drones - Gymnasium-compatible RL environments with Crazyflie dynamics.
• KestrelFPV - Unity3D FPV racing simulator with realistic aerodynamics and multiplayer support.
• RotorS - ETH Zurich MAV simulation framework for Gazebo.
• VisFly - Efficient simulator for training vision-based quadrotor flight, built on Habitat-Sim with differentiable dynamics, a Gym-style API, and importable real-world scenes (SJTU).

Frameworks & Libraries

Flight Control

• ArduPilot - Open-source autopilot supporting multi-copters, planes, and rovers.
• Betaflight - Flight controller firmware popular in FPV racing, increasingly used for autonomous research.
• Indiflight - TU Delft research firmware with incremental nonlinear dynamic inversion control.
• Paparazzi UAV - Complete open-source autopilot system from TU Delft MAVLab.
• PX4 Autopilot - Professional open-source flight control stack with extensive documentation.

Middleware & Communication

• MAVSDK - MAVLink SDK for Python, C++, and other languages.
• MAVROS - ROS interface for MAVLink-based autopilots.
• ROS 2 - Robot Operating System for building drone autonomy stacks.

Control & Planning

• acados - Real-time nonlinear MPC solver with Python interface.
• acmpc_public - Actor-Critic MPC combining RL performance with MPC robustness.
• Control Toolbox - Production-grade C++ MPC with quadrotor model. iLQR, Gauss-Newton, IPOPT/SNOPT/HPIPM solvers. 1.7k stars.
• Fast-Planner - Real-time UAV replanning using gradient-based B-spline optimization with ESDF collision avoidance. 3.2k+ stars.
• mav_trajectory_generation - Minimum-snap trajectory generation based on Mellinger & Kumar. Gold standard for aggressive flight.
• rpg_mpc - Model Predictive Control for quadrotors from UZH-RPG.
• rpg_time_optimal - CPC trajectory planning for time-optimal quadrotor paths.
• TinyMPC - High-speed MPC on microcontrollers using ADMM. Best Paper Award in Automation at ICRA 2024.
• TOPP-RA - Time-optimal path parameterization with 100% success rate in milliseconds.
• uav_geometric_control - Reference SE(3) tracking control in C++, MATLAB, and Python for extreme attitude maneuvers.

Reinforcement Learning

• CleanRL - Single-file RL implementations including PPO, ideal for learning.
• rl-tools - Ultra-fast RL training (~4s for pendulum) with microcontroller deployment. Includes quadrotor examples.
• skrl - Modular RL library supporting Isaac Gym for parallel training.
• Stable-Baselines3 - Reliable RL algorithm implementations in PyTorch.

Racing Implementations

• CRL-Drone-Racing - SJTU ViSYS code for vision-based curriculum-RL drone racing, training a single controller for high-speed gate traversal and obstacle avoidance (companion to arXiv:2602.24030).
• CRUISE - RL framework for scalable multi-drone racing using decentralized independent learning, a difficulty curriculum, and iterative self-play, with training/eval scripts and pretrained models.
• DeepPilot - End-to-end CNN racing from camera images to flight commands using temporal mosaic. 25 FPS.
• GateNet - Shallow CNN for gate detection at 60 Hz on Jetson TX2 with fish-eye support and AU-DR dataset.
• Learning to Fly - Sim-to-real transfer for direct RPM control after only 18 seconds of training. NYU ARPL.
• lsy_drone_racing - Complete educational framework with progressive difficulty and sim-to-real to Crazyflie. University course.

State Estimation

• LARVIO - Lightweight, Accurate and Robust monocular VIO.
• msckf_vio - Robust stereo VIO using Multi-State Constraint Kalman Filter.
• OpenVINS - Winner of the IROS 2019 FPV VIO Competition on the UZH-FPV racing dataset (ground-truth speeds up to 23.4 m/s). Supports online calibration.
• ORB-SLAM3 - Visual-Inertial SLAM supporting monocular, stereo, and RGB-D cameras.
• VINS-Fusion - Multi-sensor fusion for robust state estimation.

Datasets

• AirSim Drone Racing Dataset - Synthetic data from NeurIPS 2019 Game of Drones with multiple environments and sensor modalities. Archived (read-only) since June 2026.
• Blackbird Dataset - MIT aggressive flight dataset with ground truth from motion capture.
• EuRoC MAV Dataset - ETH Zurich visual-inertial datasets for benchmarking.
• MILUV - Multi-UAV indoor localization dataset: 217 minutes across 36 experiments with UWB, stereo vision, and motion-capture ground truth (indoor localization, not high-speed racing).
• TII-RATM Dataset - Multimodal racing data including autonomous and human-piloted flights (>21 m/s). RA-L 2024.
• UZH-FPV Drone Racing Dataset - High-speed racing data with event cameras, standard cameras, and IMU.
• VAPAR - UZH-RPG dataset of flight trajectories, RGB, and human eye-gaze for visual-attention-aware drone racing (Pfeiffer et al., PLOS ONE 2022).

Papers

Foundational

• A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight - Systematic comparison of learned control policies for agile quadrotor flight. ICRA 2022.
• Autonomous Drone Racing: A Survey - Comprehensive survey of perception, planning, control, and state estimation for autonomous drone racing (Hanover et al., UZH-RPG). IEEE T-RO 2024.
• Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing - UZH-RPG's hybrid approach combining perception with optimal control.
• Deep Drone Racing: From Simulation to Reality with Domain Randomization - Key paper on sim-to-real transfer for drone racing.
• Learning High-Speed Flight in the Wild - Science Robotics paper on agile flight through complex environments.

Competition Winners

• AlphaPilot: Autonomous Drone Racing - UZH-RPG's vision-based full-stack racing system for the 2019 Lockheed Martin AlphaPilot Challenge (Foehn et al.).
• Champion-level drone racing using deep reinforcement learning - Nature 2023. UZH's Swift system beating human champions.
• Guidance & Control Networks for Time-Optimal Quadcopter Flight - TU Delft MAVLab's neural-network approximation of time-optimal control (G&CNets) for aggressive quadrotor flight.

Sim-to-Real & Learning

• Agile Flight Emerges from Multi-Agent Competitive Racing - Multi-agent competitive RL in which agile high-speed flight and racing tactics emerge from the objective of winning, with transfer to real hardware (Loquercio et al., UPenn).
• Curriculum Reinforcement Learning for Quadrotor Racing with Random Obstacles - Vision-based curriculum RL combining staged curricula, domain randomization, and multi-scene training for joint gate traversal and obstacle avoidance, validated in sim and real flight (SJTU).
• Learning Agile Quadrotor Flight in the Real World - Self-adaptive framework that evolves a slow base policy into high-speed agile flight through real-world flight alone, removing offline sim-to-real transfer (UZH-RPG).
• Learning Generalizable Policy for Obstacle-Aware Autonomous Drone Racing - Deep RL with domain randomization achieving 70 km/h in cluttered environments.
• Learning to Fly in Seconds - Sim-to-real transfer after only 18 seconds of training. Asymmetric actor-critic with curriculum learning. RA-L 2024.
• On Your Own: Pro-level Autonomous Drone Racing in Uninstrumented Arenas - Vision-based autonomy matching professional human pilots in both motion-capture-instrumented and fully uninstrumented arenas (Bosello et al.). IEEE RA-L 2026.
• Precise Aggressive Aerial Maneuvers with Sensorimotor Policies - End-to-end RL mapping onboard vision and proprioception to low-level control to fly through narrow, steeply-tilted gaps via sim-to-real distillation.
• Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning - League-based self-play multi-agent RL that beats a champion human pilot in multiplayer racing while reducing collisions (UZH-RPG with Google DeepMind).
• Time-Optimal Planning for Long-Range Quadrotor Flights - Polynomial-based optimal synthesis validated at 8.86 m/s peak velocity.
• Unlocking Aerobatic Potential of Quadcopters - Science Robotics (ZJU FAST Lab, 2025). Autonomous generation and execution of professional-level freestyle aerobatics.

Perception

• Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing - Continual-learning gate detector robust to changing illumination for onboard racing perception. IJCNN 2024.
• Drift-Corrected Monocular VIO and Perception-Aware Planning for Autonomous Drone Racing - Pipeline that corrects monocular-VIO drift by fusing a YOLO gate detector through a Kalman filter, with a perception-aware planner that keeps gates in view (KAIST).
• MonoRace: Winning Champion-Level Drone Racing with Robust Monocular AI - TU Delft MAVLab onboard system using neural gate segmentation and a learned drone model for robust state estimation from a single rolling-shutter camera and IMU; won the 2025 A2RL Autonomous Drone Race.
• Robust Tightly-Coupled Monocular Visual-Inertial State Estimation for Autonomous Drone Racing - Error-state Kalman filter tightly coupling gate-corner reprojection with monocular VIO, updating from as few as two visible corners (KAIST).
• Vision-only UAV State Estimation for Fast Flights Without External Localization Systems - Onboard monocular-camera and IMU state estimation for high-speed GNSS-denied flight; CTU Prague MRS approach, an A2RL 2025 finalist.

Control

• MPCC++: Model Predictive Contouring Control for Time-Optimal Flight with Safety Constraints - UZH-RPG MPC adding track/terminal safety constraints and residual aerodynamics for crash-free flight at 80+ km/h.
• NeuroBEM: Hybrid Aerodynamic Quadrotor Model - Hybrid neural-network/blade-element-momentum model with ~50% lower dynamics-prediction error for aggressive flight. RSS 2021.
• Perception-Aware Time-Optimal Planning for Quadrotor Waypoint Flight - Time-optimal planning that jointly enforces full dynamics, single-rotor thrust/body-rate limits, gate geometry, and camera field-of-view/state-estimation quality for high-speed gate flight with onboard VIO.
• TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers - Best Paper in Automation ICRA 2024. Real-time MPC on Crazyflie.

Tutorials & Courses

University Courses

• Aerial Robotics (Penn, Coursera) - Fundamentals of quadrotor dynamics and control.
• MIT Visual Navigation for Autonomous Vehicles - State-of-the-art visual navigation and SLAM by Luca Carlone.
• University of Maryland ENAE788M - Complete videos, slides, and ROS-based assignments. Most comprehensive free aerial robotics course.

Professional Training

• ICRA 2026 Tutorial: Learning Agile Vision-based Quadrotor Flight - Scaramuzza, Reiter, and Geles on the full learning-based agile-flight pipeline (differentiable sim, RL, MPC, perception-aware training, sim-to-real), with drone racing as a core application. Held June 2026.
• IEEE RAS Summer School on Multi-robot Systems - Hands-on aerial robotics at CTU Prague.
• Udacity Flying Car Nanodegree - Planning, controls, and estimation with real drone labs. 4 months.

Self-Study

• awesome-dronecraft - Learning roadmap from programmer to drone engineer.
• awesome-RL-for-UAVs - Curated RL papers and code for UAV control including racing and sim-to-real.
• Intelligent Quads (YouTube) - Beginner-to-advanced coverage of Ardupilot, MAVlink, ROS, and Gazebo.
• MIT Beaver Works UAV Racing - Four-week summer program with public course materials.
• PX4 Developer Guide - Official documentation for PX4 development.
• ROS 2 Tutorials - Official ROS 2 learning path.

Hardware

Compute Platforms

• Khadas VIM4 - ARM-based SBC with NPU for edge inference.
• NVIDIA Jetson Orin NX - Up to 157 TOPS AI performance. De facto standard for championship racing (A2RL). YOLOv8-Pose at 62 Hz.
• NVIDIA Jetson Orin Nano Super - $249 entry-level edge kit delivering up to 67 INT8 TOPS; a low-cost onboard option for racing perception and RL inference.
• NVIDIA Jetson Thor - NVIDIA's flagship Jetson module for high-end onboard AI inference and robotics.
• NVIDIA Jetson Xavier NX - Previous generation, common in research platforms.

Flight Controllers

• Foxeer H7 - Used in A2RL/TII racing specification with Betaflight 4.4.0.
• Holybro Pixhawk 6X - Latest Pixhawk standard with dual IMUs.
• mRobotics Control Zero H7 - Compact PX4-compatible flight controller.
• SpeedyBee F405 V4 - Popular Betaflight/ArduPilot compatible FC.

Cameras

• iniVation mDAVIS 346 - Event camera with 130% VIO accuracy improvement over standard frames at high speeds.
• Intel RealSense D435i - Depth + IMU for indoor navigation. (RealSense is now an independent company at realsenseai.com.)
• Leopard Imaging IMX264 - Global shutter camera used in AlphaPilot.
• Prophesee GenX320 - Ultra-compact 320×320 event sensor (>140 dB dynamic range, microsecond latency, mW-scale power); available as a Raspberry Pi 5 starter kit for low-latency onboard perception.

Reference Platforms

• A2RL/TII Open Design - ~966g carbon fiber frame achieving 100+ km/h with 4:1 thrust ratio.
• Bitcraze Crazyflie 2.1+ - Nano quadrotor with AI-deck and Lighthouse positioning. Standard for indoor RL research.
• Bitcraze Crazyflie Brushless - 41g brushless variant with 7-10 min flight time.
• Holybro X500 V2 - PX4 development platform.
• Langostino - Complete open-source autonomous drone platform with hardware BOM, ROS2 stack, and RL-based autopilot using gym-pybullet-drones. Raspberry Pi companion computer with LiDAR and GPS.
• ModalAI Starling 2 Max - Ready-to-fly autonomous drone with VOXL compute.

Community

Discord & Chat

• Drone Community Discord - Large general drone community with FPV and autonomous channels.
• Open Robotics Discord - Official ROS and Gazebo community.
• Reinforcement Learning Discord - RL research discussions including robotics applications.

Forums & Q&A

• PX4 Discuss - Official PX4 community forum.
• Robotics Stack Exchange - Q&A for robotics including drones.
• ROS Discourse - Official ROS community forum.

Subreddits

• r/drones - General drone community.
• r/fpv - FPV racing and freestyle community.
• r/reinforcementlearning - RL research and applications.
• r/robotics - Robotics projects and research.
• r/ROS - Robot Operating System community.

Research Labs

• ETH Zurich Autonomous Systems Lab - RotorS, MAV research.
• HKUST Aerial Robotics Group - Fast-Planner, VINS-Fusion.
• MIT Karaman Group - Aggressive/agile flight and the Blackbird dataset (Sertac Karaman, MIT LIDS).
• NTNU Autonomous Robots Lab - Aerial Gym simulator.
• NYU Agile Robotics and Perception Lab - Learning to Fly in Seconds.
• TU Delft MAVLab - A2RL and AlphaPilot champions, Paparazzi UAV, G&CNets.
• UZH Robotics and Perception Group - Flightmare, Agilicious, Swift. The leading academic lab in drone racing.
• UTIAS Dynamic Systems Lab / LSY - gym-pybullet-drones, lsy_drone_racing, and Crazyflow research; code now hosted under the learnsyslab (Learning Systems & Robotics Lab) GitHub org.

AI Research

Note: The following resources were generated by AI and may contain inaccuracies. See disclaimers within each document.

• Winning at Autonomous Drone Racing - Technical analysis of competition-winning approaches including G&CNets, PPO training, and sim-to-real transfer strategies.

Experimental & Emerging

• Biological & Wetware Computing - Organoid intelligence, cultured neurons, and brain-computer interfaces for drone control.

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