arXiv subjects

Mohamed Khalil Ben-Larbi

Publications and source records attributed to Mohamed Khalil Ben-Larbi.

5 recordsLinked to original sources

arXiv2026

Safe Deep Reinforcement Learning for Spacecraft Reorientation with Pointing Keep-Out Constraint

This paper implements deep reinforcement learning (DRL) with a safety filter for spacecraft reorientation control with a single pointing keep-out zone. A new state space representation is designed which includes a compact representation of the attitude constraint zone. A reward function is formulated to achieve the control objective while enforcing the attitude constraint. The soft actor-critic (SAC) algorithm is adopted to handle continuous state and action space. A curriculum learning approach is implemented for agent training. To guarantee the compliance of the attitude constraint, a control barrier function (CBF)-based safety filter is implemented for agent deployment. Simulation results demonstrate the effectiveness of the proposed state space presentation and the designed reward function. Monte Carlo simulations underscore that reward shaping alone cannot guarantee the safety during reorientation maneuver. In contrast, with the CBF-based safety filter, the constraint can be guaranteed during maneuvers.

eess.SY↗

arXiv2025

A Dual Quaternion based RRT* Path Planning Approach for Satellite Rendezvous and Docking

This paper proposes a sampling-based motion planner that employs a dual quaternion representation to generate smooth, collision-free six-degree-of-freedom pose trajectories for satellite rendezvous and docking under keep-out zone constraints. The proposed planner integrates the dual quaternion algebra directly into an RRT* framework, thereby enabling natural screw motion interpolation in SE(3). The dual quaternion-based RRT* has been implemented in Python and demonstrated on a representative multi-obstacle scenario. A comparison with a standard RRT* using separate translation and quaternion steering highlights the enhanced pose continuity and obstacle avoidance of the proposed method. The present approach is purely kinematic in nature and does not take into account relative orbital dynamics. Consequently, the resulting path provides a preliminary estimate for a subsequent optimisation-based trajectory planner, which will refine the motion with dynamic constraints for the purpose of practical satellite rendezvous and docking missions.

cs.RO↗

arXiv2025

Deep reinforcement learning-based spacecraft attitude control with pointing keep-out constraint

This paper implements deep reinforcement learning (DRL) for spacecraft reorientation control with a single pointing keep-out zone. The Soft Actor-Critic (SAC) algorithm is adopted to handle continuous state and action space. A new state representation is designed to explicitly include a compact representation of the attitude constraint zone. The reward function is formulated to achieve the control objective while enforcing the attitude constraint. A curriculum learning approach is used for the agent training. Simulation results demonstrate the effectiveness of the proposed DRL-based method for spacecraft pointing-constrained attitude control.

eess.SY↗

arXiv2025

Forecasting Thermospheric Density with Transformers for Multi-Satellite Orbit Management

Accurate thermospheric density prediction is crucial for reliable satellite operations in Low Earth Orbits, especially at high solar and geomagnetic activity. Physics-based models such as TIE-GCM offer high fidelity but are computationally expensive, while empirical models like NRLMSIS are efficient yet lack predictive power. This work presents a transformer-based model that forecasts densities up to three days ahead and is intended as a drop-in replacement for an empirical baseline. Unlike recent approaches, it avoids spatial reduction and complex input pipelines, operating directly on a compact input set. Validated on real-world data, the model improves key prediction metrics and shows potential to support mission planning.

physics.space-ph↗

arXiv2024

Region of Attraction Estimation for Free-Floating Systems under Time-Varying LQR Control

Future Active Debris Removal (ADR) and On Orbit Servicing (OOS) missions demand for elaborate closed loop controllers. Feasible control architectures should take into consideration the inherent coupling of the free floating dynamics and the kinematics of the system. Recently, Time-Varying Linear Quadratic Regulators (TVLQR) have been used to stabilize underactuated systems that exhibit a similar kinodynamic coupling. Furthermore, this control approach integrates synergistically with Lyapunov based region of attraction (ROA) estimation, which, in the context of ADR and OOS, allows for reasoning about composability of different sub-maneuvers. In this paper, TVLQR was used to stabilize an ADR detumbling maneuver in simulation. Moreover, the ROA of the closed loop dynamics was estimated using a probabilistic method. In order to demonstrate the real-world applicability for free floating robots, further experiments were conducted onboard a free floating testbed.

eess.SY↗