arXiv subjects

Clement Fortin

Publications and source records attributed to Clement Fortin.

3 recordsLinked to original sources

arXiv2026

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

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.

cs.RO↗

arXiv2026

Adaptive SINDy: Residual Force System Identification Based UAV Disturbance Rejection

The stability and control of Unmanned Aerial Vehicles (UAVs) in a turbulent environment is a matter of great concern. Devising a robust control algorithm to reject disturbances is challenging due to the highly nonlinear nature of wind dynamics, and modeling the dynamics using analytical techniques is not straightforward. While traditional techniques using disturbance observers and classical adaptive control have shown some progress, they are mostly limited to relatively non-complex environments. On the other hand, learning based approaches are increasingly being used for modeling of residual forces and disturbance rejection; however, their generalization and interpretability is a factor of concern. To this end, we propose a novel integration of data-driven system identification using Sparse Identification of Non-Linear Dynamics (SINDy) with a Recursive Least Square (RLS) adaptive control to adapt and reject wind disturbances in a turbulent environment. We tested and validated our approach on Gazebo harmonic environment and on real flights with wind speeds of up to 2 m/s from four directions, creating a highly dynamic and turbulent environment. Adaptive SINDy outperformed the baseline PID and INDI controllers on several trajectory tracking error metrics without crashing. A root mean square error (RMSE) of up to 12.2 cm and 17.6 cm, and a mean absolute error (MAE) of 13.7 cm and 10.5 cm were achieved on circular and lemniscate trajectories, respectively. The validation was performed on a very lightweight Crazyflie drone under a highly dynamic environment for complex trajectory tracking.

cs.RO↗

arXiv2025

YawSitter: Modeling and Controlling a Tail-Sitter UAV with Enhanced Yaw Control

Achieving precise lateral motion modeling and decoupled control in hover remains a significant challenge for tail-sitter Unmanned Aerial Vehicles (UAVs), primarily due to complex aerodynamic couplings and the absence of welldefined lateral dynamics. This paper presents a novel modeling and control strategy that enhances yaw authority and lateral motion by introducing a sideslip force model derived from differential propeller slipstream effects acting on the fuselage under differential thrust. The resulting lateral force along the body y-axis enables yaw-based lateral position control without inducing roll coupling. The control framework employs a YXZ Euler rotation formulation to accurately represent attitude and incorporate gravitational components while directly controlling yaw in the yaxis, thereby improving lateral dynamic behavior and avoiding singularities. The proposed approach is validated through trajectory-tracking simulations conducted in a Unity-based environment. Tests on both rectangular and circular paths in hover mode demonstrate stable performance, with low mean absolute position errors and yaw deviations constrained within 5.688 degrees. These results confirm the effectiveness of the proposed lateral force generation model and provide a foundation for the development of agile, hover-capable tail-sitter UAVs.

cs.RO↗