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

Amir Atef Habel; Roohan Ahmed Khan; Fawad Mehboob; Clement Fortin; Dzmitry Tsetserukou

cs.ROMar 20, 2026

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

Authors: Amir Atef Habel, Roohan Ahmed Khan, Fawad Mehboob, Clement Fortin, Dzmitry Tsetserukou

Organizations: Intelligent Space Robotics Laboratory, Center for Digital Engineering, Skolkovo Institute of Science and Technology, Moscow, Russia.

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.

Figures & tables

Fig. 1: GustPilot during real-flight gate traversal under distributed and concentrated fan-generated wind disturbances.

Fig. 2: Overview of the GustPilot hierarchical guidance-control architecture.

Parameter	Value
$K_{v}$ (outer)	$[3.519,\;3.519,\;31.481]~\mathrm{s}^{-1}$
$K_{i}$ (outer)	$[0.037,\;0.037,\;5.556]~\mathrm{s}^{-2}$
$K_{\xi}$ (inner)	$[4.643,\;4.643,\;46.08]\times 10^{9}~\mathrm{s}^{-2}$
$K_{\omega}$ (inner)	$[7.857,\;7.857,\;5.530]\times 10^{8}~\mathrm{s}^{-1}$
$\alpha_{\text{Outer}}$ , $\alpha_{\text{Inner}}$	$1.0$
Torque saturation	$[0.0,\;0.000949]~\mathrm{N\cdot m}$

TABLE I: Controller Tuning Parameters and Filter Cutoffs.

Category	Parameter	Range/Value
Domain Rand	$d_{\text{min}}$ , $d_{\text{max}}$ (m)	1.0, 5.0
Jet Geometry	$R_{\text{tube}}$ (m)	$\mathcal{U}(0.25,1.00)$
Jet Geometry	$L_{\text{tube}}$ (m)	$\mathcal{U}(0.2,1.5)$
Wind Model	$u_{0}$ (m/s) (jet strength)	1.0–10.0
	$x_{0}$ (m) (virtual origin)	0.20
	$\sigma_{0}$ (m) (initial jet width)	0.10

TABLE II: Simulation Environment Parameters

Fig. 3: Reward colormap for drone positions relative to the gate.

Parameter	Value
Algorithm	PPO
Total training steps	$4.5\times 10^{7}$
Number of environments	16
Rollout length	2048
Batch size	256
Discount factor ( $\gamma$ )	0.99

TABLE III: Training Parameters

Panel of Figure 7 — Fig. 4: Training metrics for policies learned with INDI and PID low-level controllers.

Panel of Figure 8 — Fig. 5: 2-D trajectory comparisons for the four real-flight scenarios (DRL–INDI vs. DRL–PID).

Scenario	Controller	OSR (%)	$P$	$h$	$f$	Max Speed	Time	Closest Gate	Closest Fan	Airspeed	RMSE	MAE	Max Abs	RMSE Vel.
						(m/s)	(s)	(m)	(m)	(m/s)	(m)	(m)	(m)	(m/s)
I	INDI	98.8	1	1	10	1.337	14	0.065	—	—	0.127	0.121	0.180	0.170
I	PID	88.9	6	8	9	1.000	24	0.096	—	—	0.190	0.220	0.210	0.200
II	INDI	97.2	2	3	10	1.347	14	0.040 a	0.700	2.44	0.119	0.111	0.184	0.163
II	PID	0.0	20	20	0	—	—	—	—	—	1.700 b	—	—	0.400 c
III	INDI	92.5	4	5	10	1.122	9	0.077 d	0.450	3.5	0.110	0.097	0.173	0.177

TABLE IV: Comprehensive Experimental Results: OSR, Missed Gates (

P

), Gate Hits (

h

), Completed Trials (

f

), Closest Approaches, and Tracking Errors.

UAV Navigation

Unmanned aerial vehicle (UAV) navigation develops methods for locating and guiding drones safely through known, unknown, and changing environments.

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WAND: Learning Robust Navigation under Complex Wind Disturbances and Dense Obstacles for Quadrotors

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Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China · Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China · Northwestern Polytechnical University, Xi’an 710072, China+1

Disturbance Rejection Robot Navigation Quadrotor Control Robust Control Robust RL

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Robust navigation in cluttered environments remains a fundamental challenge for quadrotors, particularly when strong wind disturbances arise, which perturb vehicle dynamics, limit control authority, and substantially increase collision risk. Existing learning-based navigation policies typically rely on obstacle perception and proprioceptive observations, requiring the policy to infer time-varying disturbance effects implicitly and thereby limiting robustness under partial observability. This paper proposes WAND (Wind-Aware Navigation with Disturbance Estimation), a reinforcement learning framework for navigation under time-varying wind disturbances in dense obstacle fields. Specifically, WAND estimates wind-induced disturbance acceleration from historical proprioceptive states using a Temporal Convolutional Network (TCN). This estimation is integrated into the policy via a zero-initialized residual module, WindAdapter, while simultaneously providing feedforward compensation for low-level control. The dual use of the estimate couples disturbance-conditioned navigation with feedforward disturbance rejection. Across 12 wind-disturbed simulation settings, WAND improved the observed success rate by 8.3 percentage points on average relative to feedforward compensation alone. Controlled opposite-crosswind experiments further showed wind-direction-dependent trajectory adaptation. In indoor fan-induced flight tests, WAND succeeded in 18 of 20 trials, demonstrating the feasibility of real-time onboard navigation.

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