Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds

Abstract

Executing safe and precise flight maneuvers in dynamic high-speed winds is important for the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the relationship between various wind conditions and its effect on aircraft maneuverability is not well understood, it is challenging to design effective robot controllers using traditional control design methods. We present Neural-Fly, a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly builds on two key observations that aerodynamics in different wind conditions share a common representation and that the wind-specific part lies in a low-dimensional space. To that end, Neural-Fly uses a proposed learning algorithm, domain adversarially invariant meta-learning (DAIML), to learn the shared representation, only using 12 minutes of flight data. With the learned representation as a basis, Neural-Fly then uses a composite adaptation law to update a set of linear coefficients for mixing the basis elements. When evaluated under challenging wind conditions generated with the Caltech Real Weather Wind Tunnel, with wind speeds up to 43.6 kilometers/hour (12.1 meters/second), Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers. In addition to strong empirical performance, the exponential stability of Neural-Fly results in robustness guarantees. Last, our control design extrapolates to unseen wind conditions, is shown to be effective for outdoor flights with only onboard sensors, and can transfer across drones with minimal performance degradation.

This is the accepted version of Science Robotics Vol. 7, Issue 66, eabm6597 (2022). Video: https://youtu.be/TuF9teCZX0U

References in corpus (5)

• Learning High-Speed Flight in the Wild
• Time-Optimal Planning for Quadrotor Waypoint Flight
• Spectrally-normalized margin bounds for neural networks
• Meta-Adaptive Nonlinear Control: Theory and Algorithms
• Linear Representation Meta-Reinforcement Learning for Instant Adaptation

Cited by in corpus (19)

• Collaborative Target Search with a Visual Drone Swarm: An Adaptive Curriculum Embedded Multistage Reinforcement Learning Approach
• Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control
• Back to Newton's Laws: Learning Vision-based Agile Flight via Differentiable Physics
• Learning Multi-Pursuit Evasion for Safe Targeted Navigation of Drones
• Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control
• Neural Operators for PDE Backstepping Control of First-Order Hyperbolic PIDE with Recycle and Delay
• Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning
• A Learning-based Quadcopter Controller with Extreme Adaptation
• Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
• Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects
• MOB-Net: Limb-modularized Uncertainty Torque Learning of Humanoids for Sensorless External Torque Estimation
• RGBlimp-Q: Robotic Gliding Blimp With Moving Mass Control Based on a Bird-Inspired Continuum Arm
• Coordinated Control of Deformation and Flight for Morphing Aircraft via Meta-Learning and Coupled State-Dependent Riccati Equations
• Incremental Nonlinear Dynamic Inversion based Optical Flow Control for Flying Robots: An Efficient Data-driven Approach
• Design and Implementation of a High-Precision Wind-Estimation UAV with Onboard Sensors
• Low-Latency Event-Based Velocimetry for Quadrotor Control in a Narrow Pipe
• Physics-infused Learning for Aerial Manipulator in Winds and Near-Wall Environments
• Dynamics-Invariant Quadrotor Control using Scale-Aware Deep Reinforcement Learning
• A Fast Initialization Method for Neural Network Controllers: A Case Study of Image-based Visual Servoing Control for the multicopter Interception