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Simone D'Amico

Publications and source records attributed to Simone D'Amico.

At least 19 recordsLinked to original sources
arXiv2026

Robust Closed-form Framework for Drag-Propulsive Control of Formation Flight

To conserve propellant, Distributed Space Systems (DSS) can leverage environmental forces to produce low cost relative forces, such as atmospheric drag in Low Earth Orbit (LEO). This work maximizes this strategy by developing robust, closed-form algorithms for optimal "hybrid" control, defined here as complementing the magnitude and directionality deficiencies of differential drag with the superior control authority of propulsive maneuvers within the same control window. The use of drag introduces a significant source of dynamic uncertainty to the control problem, primarily from inaccurate atmospheric density modelling. This paper develops a robust control architecture that periodically re-solves the remaining drag maneuver plan in a diminishing horizon policy, rejecting operational and navigation errors while retaining the cost savings offered by differential drag. The approach is built upon reachable set theory, a control analysis technique that provides conditions for optimality for Linear Time Variant (LTV) systems with norm-like cost functions and optimally decouples hybrid control into separate propulsive and differential drag sub-problems. The optimality conditions are leveraged to prove the asymptotic Lyapunov stability of ideal reachable set-based control solutions and derive error bounds on this stability. A covariance analysis on the primary sources of error in hybrid control determines that the dominating source of uncertainty is the atmospheric density model. This conclusion allows the bounds on Lyapunov stability to be completely met by simply conditioning the atmospheric density value used in the control solutions. The derived Lyapunov stability bounds and performance of the algorithm are demonstrated experimentally in high fidelity full-force orbital simulations of the upcoming Space Weather Reconfigurable Multiscale Experiment (SWARM-EX) mission.

math.OC↗
arXiv2026

Fast Fuel-Optimal Constrained Impulsive Control with Application to Distributed Spacecraft

This work extends a reachable set theory approach for fuel-optimal control to incorporate convex and non-convex state, time, and magnitude constraints. The solver is designed from a new set of optimality conditions, derived by reformulating a single reachable set into multiple time-ordered, step-wise reachable set waypoint sub-problems, where a waypoint is the state reached after a given maneuver. Non-convex constraints are satisfied at each waypoint, preserving convexity at each sub-problem without needing to convexify the full control problem, enabling the computationally efficient identification of constrained optimal maneuvers, and guaranteeing a user-defined bound on constrained cost. To produce an optimal maneuver plan, the solver rolls out constrained trajectories until the remaining unconstrained solution adheres to the problem constraints. The fuel-optimal control problem considered by this work is broadly applicable to many fields and particularly significant to Distributed Space Systems (DSS). Multi-agent guidance and propulsive control techniques, often for small spacecraft at close separations, require guarantees of collision avoidance, efficient maneuvering to preserve a restricted fuel payload, and numerically-simple algorithms functional under on-board computational processing limitations. The proposed solver offers both user-defined bounds on constrained maneuver optimality and enhanced computational efficiency to directly address these challenges, and these capabilities are demonstrated comparatively against baseline solvers from the literature through Monte Carlo analysis on a variety of relevant DSS scenarios. Overall, this work enables the benefits of reachable set theory, namely computational efficiency and provable optimality, to be extended to control problems of higher fidelity.

math.OC↗
arXiv2026

Closed-form Optimal Solutions for Propulsive-Differential Drag Control of Spacecraft Swarms

This paper presents a novel optimal control approach for Distributed Space Systems (DSS) to minimize the total delta-v cost for large, time-constrained reconfigurations. Reconfiguration cost is decreased by blending differential drag and propulsive control within the same control window. By parameterizing the problem with Relative Orbital Elements (ROE) and leveraging reachable set theory, a maneuver planning methodology is derived to produce provably-optimal, full roto-translational control solutions in closed-form, such that they can be implemented on hardware-limited spacecraft.

math.OC↗
arXiv2026

Closed-form Optimal Propulsive-Differential Drag Control for Large Reconfigurations of Spacecraft Swarms

Distributed Space Systems offer unique capabilities but have significant hardware limitations on computational processing and fuel when employing small satellites. These constraints drive interest in deterministic maneuver planning that incorporates orbital perturbations to improve final state accuracy and decrease reconfiguration delta-v cost. This paper proposes a novel closed-form, provably optimal framework to generate complete roto-translational solutions for propulsive-differential drag control of multiple satellites. The approach leverages relative orbit elements and reachable set theory to characterize all control with a single methodology that can be readily generalized to generic impulsive-continuous control. The algorithm is robust to large reconfigurations and extended control windows where differential drag is most effective.

math.OC↗
arXiv2026

Ultraviolet Reflected-Light Spectroscopy of Modern Earth Analogs with Starshade Observations

Characterizing rocky, habitable zone planets in reflected light is a key science objective for future direct imaging missions, and the ultraviolet (UV) is uniquely suited for supporting the characterization of Earth-like atmospheres. The Hartley-Huggins ozone band at $0.25 ~μ\mathrm{m}$ remains detectable at low spectral resolution down to $0.1\%$ PAL $\mathrm{O}_2$, providing a sensitive proxy for oxygen-bearing chemistry even for Proterozoic-like atmospheres where the visible and near-infrared $\mathrm{O}_2$ features become inaccessible. This work uses an end-to-end framework for UV starshade spectroscopy of modern Earth analogs that integrates high-fidelity observation simulation, conservative post-processing, and Bayesian spectral retrieval to quantify ozone detection significance. The framework is applied across 108 simulated scenes spanning a range of planet phase angles, system inclinations, and exozodiacal dust densities. The results establish that robust ozone detection with UV-only, $R=7$ reflected light spectroscopy requires a photometric $\mathrm{SNR} \sim 30$. UV starshade observations with the Habitable Worlds Observatory achieve this threshold within 3-day integrations for solar-type systems at 10 parsecs with inclinations up to $60^\circ$ at favorable phases ($\leq 90^\circ$). Above this SNR threshold, ozone detections are robust to systematic bias from imperfect exozodi calibration within $\sim30\%$, and the median retrieved ozone abundance remains accurate to within one order of magnitude of the true value.

astro-ph.EP↗
arXiv2026

PLUTO: An Agentic AI Tool for Interactive Spacecraft Rendezvous Trajectory Design

Modern space missions require trajectory design methods capable of efficiently adapting to diverse mission objectives and operational constraints. Despite the maturity of non-convex trajectory optimization techniques, their broader adoption remains limited by the substantial domain expertise required to tailor problem formulations across varying mission scenarios. Recent advances in large language model (LLM) reasoning and agentic AI coding capabilities offer a promising pathway to improving the accessibility of advanced trajectory optimization techniques. This paper introduces PLUTO (Plain Language Understanding for Trajectory Optimization), an interactive trajectory design framework that integrates agentic AI coding tools within a sequential convex programming (SCP) architecture. The proposed system maps high-level semantic inputs to well-defined mathematical constraints while preserving a structured representation suitable for SCP. These constraints are processed through an auto-convexification pipeline and incorporated directly into executable optimal control formulations, thereby systematically connecting user intent and trajectory generation. Visual constraint verification and scenario parameterization further support an intuitive environment for trajectory design across a wide range of application scenarios. Within an evaluation of 50 natural-language rendezvous mission prompts spanning 10 constraint types, PLUTO generated a trajectory for every prompt, and 94% of the resulting trajectories satisfied both the quantitative and qualitative requirements of the mission intent. Ultimately, this work explores the potential of agentic AI to bridge the gap between advanced optimization algorithms and engineer-focused trajectory design workflows.

math.OC↗
arXiv2026

Unified Relative Orbital Element Framework for Eccentric Formation Flying

Quasi-nonsingular relative orbital elements (qnsROE) are among the most widely used state representations for formation flying and rendezvous in near-circular inclined orbits. They admit closed-form perturbed state transition matrices (STMs) and a direct geometric mapping, which underpins eccentricity/inclination (E/I)-vector separation for passive safety. Existing eccentric extensions of the qnsROE resolve this geometry in the radial--tangential--normal frame, where a residual semimajor-axis difference makes the E/I separation margin phase dependent. This paper introduces eccentric ROE that reduce to the qnsROE as \(e\to0\) and whose first-order geometry is resolved in the velocity--normal--binormal (VNB) frame, where the secular drift is confined to the velocity direction. Their first-order position and velocity maps yield a closed-form, phase-independent binormal--normal separation bound with an explicit margin for semimajor-axis residuals. Closed-form Keplerian and secular-\(J_2\) STMs and a decoupled impulsive control input matrix are derived for the same elements, and the elements are recovered as the physically normalized integration constants of the Cartesian equations of relative motion in the VNB frame. Together, these results provide a unified ROE framework for formation flying in general elliptic orbits.

math.DS↗
arXiv2026

Monocular Navigation Relative to Unknown Spacecraft Using a Transformer-Aided Kalman Filter

This work presents a novel learning-based pipeline for pose estimation of unknown spacecraft using only monocular images from a single servicer. The approach combines a transformer-based neural network with a Multi-State Constraint Kalman Filter (MSCKF) to estimate the pose (i.e., position and orientation of the target spacecraft relative to the camera) throughout rendezvous and proximity operations. Unlike existing vision-based methods that require prior knowledge of the target shape or inertia properties, rely on additional sensing modalities such as depth, lidar, or stereo, or only recover translation up to scale, the proposed pipeline generalizes to previously unseen spacecraft using a single monocular camera. The transformer network estimates the odometry, the change in pose between images up to scale, from SuperPoint features matched by LightGlue. The MSCKF uses these pseudo-measurements along with an orbit and attitude kinematics model to estimate the pose of the target. In particular, the relative orbit elements, the target's attitude with respect to the servicer's camera, and the associated angular velocity are estimated directly by the filter. Given the monocular approach and short distance to the target, the full observability of the range to the target is recovered via attitude maneuvers by the servicer. The method is trained and evaluated on a re-rendered high-resolution version of the SPE3R dataset, which includes synthetic images of 103 spacecraft. Eleven of these spacecraft are held out during training to evaluate the generalization to unseen targets. Monte Carlo simulations are then used to evaluate the navigation pipeline on rendered trajectories of the held out spacecraft. The results demonstrate that learned vision pipelines as a front-end for Kalman filters provide median errors of 3.7° in attitude and 2.2% of range in ROE when navigating about unknown targets.

cs.RO↗
arXiv2026

OrbitTAMP: Grounding Language Models for Task and Motion Planning in Spacecraft Rendezvous

Spacecraft rendezvous and proximity operations (RPO) are currently planned through an expertise-intensive process in which engineers translate high-level operational intent into safe, dynamically feasible trajectories, creating a bottleneck to scalable operations. Large language model (LLM)-based agents could offer an intuitive interface for this process, although their outputs are not inherently grounded in orbital dynamics, operational constraints, or the structure of admissible spacecraft maneuvers. To exploit their semantic reasoning while ensuring the generated plan's physical validity, this paper presents a hierarchical framework for spacecraft task-and-motion planning (TAMP) that grounds LLM reasoning in a graph of reusable behaviors and domain-specific planning modules. Within this framework, a pretrained LLM maps a natural-language command to a partial mission specification. The associated planners then resolve unspecified decisions within the admissible operational space. Finally, trajectory optimization converts the completed mission specification into a dynamically feasible trajectory. Numerical experiments demonstrate that this architecture substantially improves intent recovery over direct LLM generation, achieving 98% exact recovery of partial mission specifications across all evaluated splits when backed by frontier LLMs. Additional test-time-compute experiments show that, for a compact 9B model, verifier-guided revision increases exact recovery from 75% to 88%, while broader behavior-plan search independently improves selection among admissible trajectory realizations. Overall, these results establish a scalable and auditable foundation for language-driven agentic planning of spacecraft RPO.

cs.RO↗
arXiv2026

Transformer-Informed Trajectory Optimization for Relative Motion in Cislunar Orbits

Autonomous spacecraft guidance and control requires a fast solution to non-convex trajectory optimization, which can be accelerated by providing a near-optimal initial guess to an optimization protocol, i.e., warm-starting. A robust warm starting method is especially useful for rendezvous, proximity operations, and docking (RPOD) in cislunar space, where the underlying dynamics become severely nonlinear and chaotic compared to those in Earth orbit, especially at perilune. This paper extends the Autonomous Rendezvous Transformer (ART), a transformer-based warm-start trajectory generation method, to cislunar RPOD scenarios for the first time. To accurately and reliably solve the nonconvex optimal control problems (OCPs) posed by these scenarios, a new and enhanced version of ART, ART-TWIN (Two-Way INference), is introduced. Inspired by forward-backward shooting methods used in other trajectory design applications, ART-TWIN autoregressively generates two arcs, one from the initial state and one from the desired terminal state, that are patched together at the midpoint of the timeseries. When evaluated on a set of simulated rendezvous scenarios that are initialized at perilune, ART-TWIN is demonstrated to substantially accelerate convergence and increase feasibility guarantees when used as a warm-start to sequential convex programming (SCP), compared to convex relaxations and the original ART. These results illustrate the necessity of ART-TWIN's dual-arc generation to enable the viability of and gain benefits from using transformer-based warm-start methods in the most challenging areas of the cislunar dynamical regime.

math.OC↗
arXiv2026

Transformer-Based Warm-Starting for Feasible and Optimal Terminal Approach to Tumbling Objects with Space Manipulators

Autonomous capture of a tumbling object with a space manipulator requires trajectory generation under real-time onboard constraints. However, coupled bus-arm dynamics, rotating safety geometry, and visibility requirements make the underlying optimization severely nonconvex, hence expensive to solve and sensitive to initialization. This paper develops a causal-transformer warm-start for the computationally dominant attitude-manipulator stage of a two-stage sequential convex programming (SCP) framework. Across 300 held-out scenarios evaluated on embedded hardware, the learned warm-start reduces the iterations of the second-stage SCP by up to 32% and runtime by 16% while maintaining numerical feasibility and matching the final control cost. Furthermore, linear and flow matching action decoders are compared across action-chunk lengths and training dataset sizes, where the flow matching decoder exhibits stronger training-data efficiency than the linear decoder. When SCP is terminated based on feasibility, the runtime is further reduced while the learned prior suppresses the severe high-cost tail observed with heuristic initialization. These results demonstrate that learned warm-starts can reduce onboard optimization cost while steering SCP toward basins containing feasible, low-cost solutions.

cs.RO↗
arXiv2026

Stellar Observation Scheduling Optimization for Distributed Space Interferometry Missions

Growing interest in space interferometry for detecting bio-signatures of exoplanets has led to the development of several low-cost mission concepts involving Earth-orbiting formation flying techniques to perform nulling interferometry of incoming light from exoplanetary systems. Pursuing these developments, this work proposes a multi-objective dynamic programming scheme to optimize the balance between fuel expenditure and scientific outcomes of low-cost formation flying space interferometry missions by leveraging several insights from mission design. This scheme accounts for nominal concept of operations constraints, fuel expenditure, and observability conditions to produce a globally optimal Pareto front policy for exoplanetary system observation, and represents one of the first applications of multi-objective optimization to distributed space telescope astronomy.

astro-ph.EP↗
arXiv2026

Semantic Constraint Synthesis for Adaptive Trajectory Optimization via Large Language Models

Trajectory optimization is a critical component for enabling safe and reliable autonomous operations in space exploration. As space missions increase in frequency, complexity, and scope, there is a growing need to rapidly formulate mathematically sound trajectory optimization problems that accurately reflect mission objectives and operational constraints. However, translating mission intent into tractable analytical formulations for trajectory optimization requires substantial domain expertise. This paper presents a framework that leverages large language models (LLMs) to translate natural language descriptions of mission requirements and constraints into executable trajectory optimization code and corresponding mathematical formulations. Experiments in spacecraft rendezvous scenarios demonstrate a high success rate in reconditioning a convex trajectory optimization problem from semantic mission requirements. Ultimately, this work highlights the potential of LLMs to bridge high-level intent and formal optimization models, enabling more flexible and efficient trajectory design of spacecraft.

math.OC↗
arXiv2026

Intent-aligned Autonomous Spacecraft Guidance via Reasoning Models

Future spacecraft operations require autonomy that can interpret high-level mission intent while preserving safety. However, existing trajectory optimization still relies heavily on expert-crafted formulations and does not support intent-conditioned decision-making. This paper proposes an intent-aligned spacecraft guidance framework that links high-level reasoning and safe trajectory optimization through explicit intermediate abstractions, based on behavior sequences and waypoint constraints. A foundation model first predicts an intent-aligned behavior plan, a waypoint generation model then converts it into waypoint constraints, and the safe trajectory is computed via optimization. This decomposition enables scalable supervision without sacrificing safety. Numerical experiments in close-proximity operation scenarios demonstrate that the proposed pipeline achieves over 90\% SCP convergence and yields a $1.5\times$ higher rate of generating trajectories that satisfy the top intent-prioritized performance criteria than heuristic decision-making. These results support the use of intermediate behavior abstraction as a practical interface between foundation-model reasoning and safety-critical onboard spacecraft autonomy.

eess.SY↗
arXiv2026

Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous

Reliable real-time trajectory generation is essential for future autonomous spacecraft. While recent progress in nonconvex guidance and control is paving the way for onboard autonomous trajectory optimization, these methods still rely on extensive expert input (e.g., waypoints, constraints, mission timelines, etc.), which limits operational scalability in complex missions such as rendezvous and proximity operations. This paper introduces SAGES (Semantic Autonomous Guidance Engine for Space), a trajectory-generation framework that translates natural-language commands into spacecraft trajectories that reflect high-level intent while respecting nonconvex constraints. Experiments in two settings (fault-tolerant proximity operations with continuous-time constraint enforcement and a free-flying robotic platform) demonstrate that SAGES reliably produces trajectories aligned with human commands, achieving over 90% semantic-behavioral consistency across diverse behavior modes. Ultimately, this work marks an initial step toward language-conditioned, constraint-aware spacecraft trajectory generation, enabling operators to interactively guide both safety and behavior through intuitive natural-language commands with reduced expert burden. Project Website: https://semantic-guidance4space.github.io/

cs.RO↗
arXiv2026

Efficient Input-Constrained Impulsive Optimal Control of Linear Systems with Application to Spacecraft Relative Motion

This work presents a novel algorithm for impulsive optimal control of linear time-varying systems with the inclusion of input magnitude constraints. Impulsive optimal control problems, where the optimal input solution is a sum of delta functions, are typically formulated as an optimization over a normed function space subject to integral equality constraints and can be efficiently solved for linear time-varying systems in their dual formulation. In this dual setting, the problem takes the form of a semi-infinite program which is readily solvable in online scenarios for constructing maneuver plans. This work augments the approach with the inclusion of magnitude constraints on the input over time windows of interest, which is shown to preserve the impulsive nature of the optimal solution and enable efficient solution procedures via semi-infinite programming. The resulting algorithm is demonstrated on the highly relevant problem of relative motion control of spacecraft in Low Earth Orbit (LEO).

math.OC↗
arXiv2026

Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers

Spacecraft rendezvous enables on-orbit servicing, debris removal, and crewed docking, forming the foundation for a scalable space economy. Designing such missions requires rapid exploration of the tradespace between control cost and flight time across multiple candidate targets. However, multi-objective optimization in this setting is challenging, as the underlying constraints are often nonconvex, and mission designers must balance accuracy (e.g., solving the full problem) with efficiency (e.g., convex relaxations), slowing iteration and limiting design agility. To address these challenges, this paper proposes an AI-powered framework that enables agile and generalized rendezvous mission design. Given the orbital information of the target spacecraft, boundary conditions of the servicer, and a range of flight times, a transformer model generates a set of near-Pareto optimal trajectories across varying flight times in a single parallelized inference step, thereby enabling rapid mission trade studies. The model is further extended to accommodate variable flight times and perturbed orbital dynamics, supporting realistic multi-objective trade-offs. Validation on chance-constrained rendezvous problems in Earth orbits with passive safety constraints demonstrates that the model generalizes across both flight times and dynamics, consistently providing high-quality initial guesses that converge to superior solutions in fewer iterations. Moreover, the framework efficiently approximates the Pareto front, achieving runtimes comparable to convex relaxation by exploiting parallelized inference. Together, these results position the proposed framework as a practical surrogate for nonconvex trajectory generation and mark an important step toward AI-driven trajectory design for accelerating preliminary mission planning in real-world rendezvous applications.

math.OC↗
arXiv2025

Improved 3D Gaussian Splatting of Unknown Spacecraft Structure Using Space Environment Illumination Knowledge

This work presents a novel pipeline to recover the 3D structure of an unknown target spacecraft from a sequence of images captured during Rendezvous and Proximity Operations (RPO) in space. The target's geometry and appearance are represented as a 3D Gaussian Splatting (3DGS) model. However, learning 3DGS requires static scenes, an assumption in contrast to dynamic lighting conditions encountered in spaceborne imagery. The trained 3DGS model can also be used for camera pose estimation through photometric optimization. Therefore, in addition to recovering a geometrically accurate 3DGS model, the photometric accuracy of the rendered images is imperative to downstream pose estimation tasks during the RPO process. This work proposes to incorporate the prior knowledge of the Sun's position, estimated and maintained by the servicer spacecraft, into the training pipeline for improved photometric quality of 3DGS rasterization. Experimental studies demonstrate the effectiveness of the proposed solution, as 3DGS models trained on a sequence of images learn to adapt to rapidly changing illumination conditions in space and reflect global shadowing and self-occlusion.

cs.CV↗