FALCON-S: Fixed-wing ground-effect Aerodynamics Simulator and Flight Control Learning Suite

๐Ÿ“… 2026-09-05
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๐Ÿ“ Abstract
We present a modular, high-fidelity simulation framework for the development and benchmarking of flight control strategies in fixed-wing aerial robots operating near the ground. Unlike existing simulators that rely on simplified or hover-oriented dynamics, our framework models full 6DoF rigid-body physics, semi-empirical ground-effect aerodynamics, actuator dynamics, sensor noise, and environmental disturbances. This physical realism, combined with modular component design, enables systematic analysis of low-altitude flight behavior under realistic conditions. The simulator supports both CPU and GPU backends via Torch and NVIDIA Warp, enabling high-throughput parallel execution suitable for large-scale reinforcement learning training and optimal control rollouts. A unified interface accommodates a range of controllers (both RL and optical control algorithms) across tasks such as altitude regulation and trajectory tracking. Cross-validation with X-Plane and JSBSim is also supported to facilitate engineering integration and visual fidelity.
Problem

Research questions and friction points this paper is trying to address.

fixed-wing aerial robots
ground-effect aerodynamics
high-fidelity simulation
Innovation

Methods, ideas, or system contributions that make the work stand out.

high-fidelity simulation
ground-effect aerodynamics
reinforcement learning
modular design
realistic conditions
M
Matteo El Hariry
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg
P
Pedro Lima
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg
A
Andrej Orsula
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg
Matthieu Geist
Matthieu Geist
Earth Species Project (ex-google, ex-cohere, on leave of Professor, Universitรฉ de Lorraine)
reinforcement learningmachine learning
M
Miguel Olivares-Mendez
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg