GustPilot: A Hierarchical DRL-INDI Framework for Wind-Resilient Quadrotor Navigation

2026-03-20

AI Summary

This work addresses the challenge of reliable autonomous navigation for lightweight quadrotors under strong wind disturbances by proposing a hierarchical wind-resilient navigation framework. The upper layer employs deep reinforcement learning (DRL) to generate wind-robust velocity reference trajectories in the inertial frame, while the lower layer utilizes a geometric incremental nonlinear dynamic inversion (INDI) controller for rapid disturbance rejection and high-precision tracking. By incorporating randomized fan-generated wind fields during training, the system generalizes effectively to complex dynamic wind environments without requiring retraining. Flight experiments demonstrate that, under 3.5 m/s wind disturbances, a 50 g quadrotor achieves stable flight at 1.34 m/s, with mission success rate improving from 55.0% to 94.7% and trajectory tracking RMSE reduced by up to 50%.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Wind disturbances remain a key barrier to reliable autonomous navigation for lightweight quadrotors, where the rapidly varying airflow can destabilize both planning and tracking. This paper introduces GustPilot, a hierarchical wind-resilient navigation stack in which a deep reinforcement learning (DRL) policy generates inertial-frame velocity reference for gate traversal. At the same time, a geometric Incremental Nonlinear Dynamic Inversion (INDI) controller provides low-level tracking with fast residual disturbance rejection. The INDI layer achieves this by providing incremental feedback on both specific linear acceleration and angular acceleration rate, using onboard sensor measurements to reject wind disturbances rapidly. Robustness is obtained through a two-level strategy, wind-aware planning learned via fan-jet domain randomization during training, and rapid execution-time disturbance rejection by the INDI tracking controller. We evaluate GustPilot in real flights on a 50g quad-copter platform against a DRL-PID baseline across four scenarios ranging from no-wind to fully dynamic conditions with a moving gate and a moving disturbance source. Despite being trained only in a minimal single-gate and single-fan setup, the policy generalizes to significantly more complex environments (up to six gates and four fans) without retraining. Across 80 experiments, DRL-INDI achieves a 94.7% versus 55.0% for DRL-PID as average Overall Success Rate (OSR), reduces tracking RMSE up to 50%, and sustains speeds up to 1.34 m/s under wind disturbances up to 3.5 m/s. These results demonstrate that combining DRL-based velocity planning with structured INDI disturbance rejection provides a practical and generalizable approach to wind-resilient autonomous flight navigation.

Problem

• wind disturbance
• autonomous navigation
• quadrotor
• flight stability
• aerial robotics

Innovation

• Deep Reinforcement Learning
• Incremental Nonlinear Dynamic Inversion
• Wind-Resilient Navigation
• Domain Randomization
• Quadrotor Control