Wind-Aware Reinforcement Learning Control of a Small Quadrotor Using Learned Onboard Wind Estimation in Simulated Atmospheric Turbulence
arXiv:2607.01528v1 Announce Type: new Abstract: Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's airspeed degrade trajectory tracking and can defeat conventional feedback control. This work illustrates a two-stage learning pipeline that first estimates the local wind from onboard kinematics and dynamics and then exploits that estimate inside a reinforcement learning (RL) flight controller. The wind estimator, an attention-augmented gated recurrent network trained on thousands of simulated flig
The continuous advancements in AI and reinforcement learning are enabling more sophisticated autonomous systems capable of operating in complex, real-world conditions like atmospheric turbulence.
This development is crucial for expanding the operational envelopes of small autonomous aircraft, allowing them to perform critical tasks reliably in challenging environments.
The ability of small drones to effectively operate in turbulent wind conditions without human intervention improves their utility, safety, and potential for widespread adoption across various applications.
- · Drone manufacturers
- · Logistics and delivery companies
- · Defence sector
- · AI/ML developers
- · Conventional feedback control systems
Improved reliability and expanded use cases for small multirotor aircraft in dynamic weather conditions.
Increased demand for advanced sensors and processing power on drones, driving innovation in embedded AI.
Drone-based services become more resilient and pervasive, potentially displacing some traditional human-operated tasks in hazardous or remote areas.
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