arXiv subjects

Leonard Felicetti

Publications and source records attributed to Leonard Felicetti.

6 recordsLinked to original sources

arXiv2026

Drift-Aware Multi-Target Space Tug Logistics Using Natural Orbital Precession

Multi-rendezvous missions for active debris removal and in-orbit servicing incur prohibitive propellant costs when their targets differ in the orientation of their orbital planes, and classical mission design treats the nodal precession induced by Earth's oblateness as a perturbation to be cancelled. This paper presents a drift-aware trajectory optimisation framework that instead exploits that precession as a mission-design resource, benchmarked against the published winning solution to the European Space Agency's Kessler Run trajectory-optimisation competition as a trusted reference. Three methodological contributions are made. First, an enhanced drift orbit shapes the size, shape, and inclination of an intermediate orbit to tune the differential precession rate, substantially cheaper than the altitude-only designs of prior work in the Sun-synchronous regime, where inclination is the dominant lever. Second, a global per-leg time-budget optimiser makes explicit and resolves the coupling between drift-time allocation and downstream nodal geometry that defeats greedy allocation. Third, a sequence-recovery test independently re-derives a removal order consistent with the published reference from the debris catalogue alone. Applied to the full debris campaign on the published mission partition under the competition's transfer-time rules, the framework closely matches the published benchmark cost using only analytical transfer models and minutes of single-core computation. A cross-validation anchored to a widely used flight-dynamics tool quantifies the fidelity limits of the analytic model and bounds the cost of literal execution, and the constructive nature of the algorithm supports rapid in-orbit replanning after a missed manoeuvre.

eess.SY↗

arXiv2026

Path Planning and Reinforcement Learning-Driven Control of On-Orbit Free-Flying Multi-Arm Robots

This paper presents a hybrid approach that integrates trajectory optimization (TO) and reinforcement learning (RL) for motion planning and control of free-flying multi-arm robots in on-orbit servicing scenarios. The proposed system integrates TO for generating feasible, efficient paths while accounting for dynamic and kinematic constraints, and RL for adaptive trajectory tracking under uncertainties. The multi-arm robot design, equipped with thrusters for precise body control, enables redundancy and stability in complex space operations. TO optimizes arm motions and thruster forces, reducing reliance on the arms for stabilization and enhancing maneuverability. RL further refines this by leveraging model-free control to adapt to dynamic interactions and disturbances. The experimental results validated through comprehensive simulations demonstrate the effectiveness and robustness of the proposed hybrid approach. Two case studies are explored: surface motion with initial contact and a free-floating scenario requiring surface approximation. In both cases, the hybrid method outperforms traditional strategies. In particular, the thrusters notably enhance motion smoothness, safety, and operational efficiency. The RL policy effectively tracks TO-generated trajectories, handling high-dimensional action spaces and dynamic mismatches. This integration of TO and RL combines the strengths of precise, task-specific planning with robust adaptability, ensuring high performance in the uncertain and dynamic conditions characteristic of space environments. By addressing challenges such as motion coupling, environmental disturbances, and dynamic control requirements, this framework establishes a strong foundation for advancing the autonomy and effectiveness of space robotic systems.

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arXiv2025

Stereovision Image Processing for Planetary Navigation Maps with Semi-Global Matching and Superpixel Segmentation

Mars exploration requires precise and reliable terrain models to ensure safe rover navigation across its unpredictable and often hazardous landscapes. Stereoscopic vision serves a critical role in the rover's perception, allowing scene reconstruction by generating precise depth maps through stereo matching. State-of-the-art Martian planetary exploration uses traditional local block-matching, aggregates cost over square windows, and refines disparities via smoothness constraints. However, this method often struggles with low-texture images, occlusion, and repetitive patterns because it considers only limited neighbouring pixels and lacks a wider understanding of scene context. This paper uses Semi-Global Matching (SGM) with superpixel-based refinement to mitigate the inherent block artefacts and recover lost details. The approach balances the efficiency and accuracy of SGM and adds context-aware segmentation to support more coherent depth inference. The proposed method has been evaluated in three datasets with successful results: In a Mars analogue, the terrain maps obtained show improved structural consistency, particularly in sloped or occlusion-prone regions. Large gaps behind rocks, which are common in raw disparity outputs, are reduced, and surface details like small rocks and edges are captured more accurately. Another two datasets, evaluated to test the method's general robustness and adaptability, show more precise disparity maps and more consistent terrain models, better suited for the demands of autonomous navigation on Mars, and competitive accuracy across both non-occluded and full-image error metrics. This paper outlines the entire terrain modelling process, from finding corresponding features to generating the final 2D navigation maps, offering a complete pipeline suitable for integration in future planetary exploration missions.

astro-ph.IM↗

arXiv2025

A causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers

This paper discusses an approach to inertial parameter estimation for the case of cargo carrying spacecraft that is based on causal learning, i.e. learning from the responses of the spacecraft, under actuation. Different spacecraft configurations (inertial parameter sets) are simulated under different actuation profiles, in order to produce an optimised time-series clustering classifier that can be used to distinguish between them. The actuation is comprised of finite sequences of constant inputs that are applied in order, based on typical actuators available. By learning from the system's responses across multiple input sequences, and then applying measures of time-series similarity and F1-score, an optimal actuation sequence can be chosen either for one specific system configuration or for the overall set of possible configurations. This allows for both estimation of the inertial parameter set without any prior knowledge of state, as well as validation of transitions between different configurations after a deployment event. The optimisation of the actuation sequence is handled by a reinforcement learning model that uses the proximal policy optimisation (PPO) algorithm, by repeatedly trying different sequences and evaluating the impact on classifier performance according to a multi-objective metric.

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arXiv2024

Spacecraft inertial parameters estimation using time series clustering and reinforcement learning

This paper presents a machine learning approach to estimate the inertial parameters of a spacecraft in cases when those change during operations, e.g. multiple deployments of payloads, unfolding of appendages and booms, propellant consumption as well as during in-orbit servicing and active debris removal operations. The machine learning approach uses time series clustering together with an optimised actuation sequence generated by reinforcement learning to facilitate distinguishing among different inertial parameter sets. The performance of the proposed strategy is assessed against the case of a multi-satellite deployment system showing that the algorithm is resilient towards common disturbances in such kinds of operations.

astro-ph.IM↗

arXiv2024

Optimal path planning and weighted control of a four-arm robot in on-orbit servicing

This paper presents a trajectory optimization and control approach for the guidance of an orbital four-arm robot in extravehicular activities. The robot operates near the target spacecraft, enabling its arm's end-effectors to reach the spacecraft's surface. Connections to the target spacecraft can be established by the arms through specific footholds (docking devices). The trajectory optimization allows the robot path planning by computing the docking positions on the target spacecraft surface, along with their timing, the arm trajectories, the six degrees of freedom body motion, and the necessary contact forces during docking. In addition, the paper introduces a controller designed to track the planned trajectories derived from the solution of the nonlinear programming problem. A weighted controller formulated as a convex optimization problem is proposed. The controller is defined as the optimization of an objective function that allows the system to perform a set of tasks simultaneously. Simulation results show the application of the trajectory optimization and control approaches to an on-orbit servicing scenario.

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