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

TL;DR

GustPilot combines hierarchical deep reinforcement learning with a geometric INDI controller to enable lightweight quadrotors to navigate reliably in wind disturbances, demonstrating superior success rates and disturbance rejection in real-world tests.

Contribution

This work introduces a novel hierarchical DRL-INDI framework that generalizes wind-resilient navigation for quadrotors without retraining in complex environments.

Findings

Achieved 94.7% success rate across diverse wind conditions.

Reduced tracking RMSE by up to 50%.

Maintained high speeds up to 1.34 m/s under wind disturbances.

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.