arXiv subjects

Matteo El Hariry

Publications and source records attributed to Matteo El Hariry.

4 recordsLinked to original sources

arXiv2026

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

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.

cs.RO↗

arXiv2025

Spacecraft Angular Rate Estimation via Event-Based Camera Sensing

This paper presents a method for determining spacecraft angular rates using event-based camera sensing. This is achieved by analyzing the temporal distribution of brightness events triggered by the apparent motion of stars. The location and polarity of the events are used to infer the apparent motion field of the stars, which is, in turn, employed to estimate the observer angular velocity in the camera frame. This can be converted to the spacecraft angular rates provided an attitude reference. The method is validated through numerical simulation for a synthetic dataset of event streams generated on random spacecraft pointing and rates conditions. The accuracy of the method is assessed, demonstrating its potential to complement or replace conventional rate sensors in spacecraft systems using event camera sensing.

eess.SY↗

arXiv2025

Deep Reinforcement Learning Policies for Underactuated Satellite Attitude Control

Autonomy is a key challenge for future space exploration endeavours. Deep Reinforcement Learning holds the promises for developing agents able to learn complex behaviours simply by interacting with their environment. This paper investigates the use of Reinforcement Learning for the satellite attitude control problem, namely the angular reorientation of a spacecraft with respect to an in- ertial frame of reference. In the proposed approach, a set of control policies are implemented as neural networks trained with a custom version of the Proximal Policy Optimization algorithm to maneuver a small satellite from a random starting angle to a given pointing target. In particular, we address the problem for two working conditions: the nominal case, in which all the actuators (a set of 3 reac- tion wheels) are working properly, and the underactuated case, where an actuator failure is simulated randomly along with one of the axes. We show that the agents learn to effectively perform large-angle slew maneuvers with fast convergence and industry-standard pointing accuracy. Furthermore, we test the proposed method on representative hardware, showing that by taking adequate measures controllers trained in simulation can perform well in real systems.

cs.RO↗

arXiv2023

Mobility Strategy of Multi-Limbed Climbing Robots for Asteroid Exploration

Mobility on asteroids by multi-limbed climbing robots is expected to achieve our exploration goals in such challenging environments. We propose a mobility strategy to improve the locomotion safety of climbing robots in such harsh environments that picture extremely low gravity and highly uneven terrain. Our method plans the gait by decoupling the base and limbs' movements and adjusting the main body pose to avoid ground collisions. The proposed approach includes a motion planning that reduces the reactions generated by the robot's movement by optimizing the swinging trajectory and distributing the momentum. Lower motion reactions decrease the pulling forces on the grippers, avoiding the slippage and flotation of the robot. Dynamic simulations and experiments demonstrate that the proposed method could improve the robot's mobility on the surface of asteroids.

cs.RO↗