arXiv subjects

Francesco Topputo

Publications and source records attributed to Francesco Topputo.

At least 19 recordsLinked to original sources

arXiv2026

Analytical Confidence Boundaries for Non-Gaussian Uncertainty in Perturbed Spacecraft Dynamics

This work investigates nonlinear uncertainty propagation in perturbed astrodynamics, focusing on the rapid characterization of non-Gaussian distributions and the construction of three-dimensional "banana-shaped" confidence boundaries. To bridge the gap between computationally intensive high-fidelity methods and inaccurate linear approximations, this paper introduces a fully analytical, sample-free framework for higher-order moments extraction. Leveraging Differential Algebra to bypass repeated numerical integration, statistical moments are extracted analytically via Isserlis' theorem and a monomial-to-Hermite basis transformation. A pair-product projection strategy is exploited to overcome the severe computational bottleneck of full fourth-order tensor contractions and compute only relevant terms via efficient polynomial algebra. The extracted skewness and kurtosis components directly parameterize non-elliptical confidence geometries that capture spatial bending and out-of-plane coupling of typical non-Gaussian distributions in astrodynamics. The approach is validated in high-fidelity environments including a cislunar Near-Rectilinear Halo Orbit and close-proximity trajectories around Apophis during Earth's flyby, where the analytical approach achieves geometric accuracy comparable to expensive Monte Carlo simulations while reducing computational runtime by orders of magnitude.

eess.SY↗

arXiv2026

Pretrained Approximators for Low-Thrust Trajectory Cost and Reachability

Low-thrust trajectory design relies heavily on repeated evaluations of fuel consumption and transfer feasibility, which require expensive optimal control solutions. In this work, we show these quantities can be accurately approximated by machine learning surrogates, enabling fast and scalable evaluation across a wide range of scenarios. By increasing both dataset size and model capacity, we observe that low-thrust trajectory optimization follows a scaling law, with performance improving linearly with the logarithm of training data and network parameters, and no evidence of saturation within the explored regime. Guided by this observation, we construct a large-scale dataset using the proposed homotopy-ray strategy tailored to mission design requirements. A key is the introduction of a self-similar transformation, which allows generalization across semi-major axes, inclinations, and central bodies avoiding retraining. As a result, the same neural approximator can be applied to diverse orbital environments and mission classes. The proposed models accurately predict optimal fuel consumption and minimum transfer time for single- and multi-revolution transfers. Their performance and generalization are demonstrated on a public dataset, a multi-asteroid flyby problem from the Global Trajectory Optimization Competition, and an asteroid rendezvous mission design. The models and datasets are released as open-source to support the space community.

cs.LG↗

arXiv2026

Pulsar Selection Criteria and Performance Evaluation of Autonomous X-ray Pulsar Navigation Systems

Current space missions primarily depend on Earth-based Guidance, Navigation, and Control (GNC) systems involving human-in-the-loop operations. X-ray pulsar-based navigation offers a promising alternative by using the very precise periodic X-ray emissions from pulsars for fully autonomous state estimation. This study presents a comprehensive analysis of pulsar selection criteria that significantly influence overall navigation performance. Observational data from the NICER mission is used to derive realistic estimates of measurement noise. Key mission-level constraints, including pulsed flux, pulsar visibility, geometric configuration, and long-term timing stability, are integrated into the pulsar selection process, addressing limitations of existing studies. An extended Kalman filter (EKF) is used for onboard spacecraft state estimation. The proposed system is evaluated in two scenarios: a Low Earth Orbit (LEO) satellite at 600 km altitude and an interplanetary transfer from Earth to Jupiter. Simulation results show that including the Crab pulsar yields position errors below 7 km in LEO and 20 km during interplanetary transfer with an instrument effective area of 200~cm$^2$; however, the Crab's limited timing stability leads to filter divergence after 20 days without timing model updates. In contrast, more stable pulsars enable long-term autonomy but with reduced accuracy. These results highlight the trade-offs involved in pulsar selection for autonomous navigation and the need to balance competing objectives. Overall, this study demonstrates the feasibility of X-ray pulsar-based navigation and marks a key step towards fully autonomous spacecraft operations.

astro-ph.IM↗

arXiv2025

Global Optimality in Multi-Flyby Asteroid Trajectory Optimization: Theory and Application Techniques

Designing optimal trajectories for multi-flyby asteroid missions is scientifically critical but technically challenging due to nonlinear dynamics, intermediate constraints, and numerous local optima. This paper establishes a method that approaches global optimality for multi-flyby trajectory optimization under a given sequence. The original optimal control problem with interior-point equality constraints is transformed into a multi-stage decision formulation. This reformulation enables direct application of dynamic programming in lower dimensions, and follows Bellman's principle of optimality. Moreover, the method provides a quantifiable bound on global optima errors introduced by discretization and approximation assumptions, thus ensuring a measure of confidence in the obtained solution. The method accommodates both impulsive and low-thrust maneuver schemes in rendezvous and flyby scenarios. Several computational techniques are introduced to enhance efficiency, including a specialized solution for bi-impulse cases and an adaptive step refinement strategy. The proposed method is validated through three problems: 1) an impulsive variant of the fourth Global Trajectory Optimization competition problem (GTOC4), 2) the GTOC11 problem, and 3) the original low-thrust GTOC4 problem. Each case demonstrates improvements in fuel consumption over the best-known trajectories. These results give evidence of the generality and effectiveness of the proposed method in global trajectory optimization.

math.OC↗

arXiv2025

Neural Approximators for Low-Thrust Trajectory Transfer Cost and Reachability

In trajectory design, fuel consumption and trajectory reachability are two key performance indicators for low-thrust missions. This paper proposes general-purpose pretrained neural networks to predict these metrics. The contributions of this paper are as follows: Firstly, based on the confirmation of the Scaling Law applicable to low-thrust trajectory approximation, the largest dataset is constructed using the proposed homotopy ray method, which aligns with mission-design-oriented data requirements. Secondly, the data are transformed into a self-similar space, enabling the neural network to adapt to arbitrary semi-major axes, inclinations, and central bodies. This extends the applicability beyond existing studies and can generalize across diverse mission scenarios without retraining. Thirdly, to the best of our knowledge, this work presents the current most general and accurate low-thrust trajectory approximator, with implementations available in C++, Python, and MATLAB. The resulting neural network achieves a relative error of 0.78% in predicting velocity increments and 0.63% in minimum transfer time estimation. The models have also been validated on a third-party dataset, multi-flyby mission design problem, and mission analysis scenario, demonstrating their generalization capability, predictive accuracy, and computational efficiency.

cs.LG↗

arXiv2025

Optimal Control and Neural Porkchop Analysis for Low-Thrust Asteroid Rendezvous Mission

This paper presents a comparative study of the applicability and accuracy of optimal control methods and neural network-based estimators in the context of porkchop plots for preliminary asteroid rendezvous mission design. The scenario considered involves a deep-space CubeSat equipped with a low-thrust engine, departing from Earth and rendezvousing with a near-Earth asteroid within a three-year launch window. A low-thrust trajectory optimization model is formulated, incorporating variable specific impulse, maximum thrust, and path constraints. The optimal control problem is efficiently solved using Sequential Convex Programming (SCP) combined with a solution continuation strategy. The neural network framework consists of two models: one predicts the minimum fuel consumption ($\Delta v$), while the other estimates the minimum flight time ($\Delta t$) which is used to assess transfer feasibility. Case results demonstrate that, in simplified scenarios without path constraints, the neural network approach achieves low relative errors across most of the design space and successfully captures the main structural features of the porkchop plots. In cases where the SCP-based continuation method fails due to the presence of multiple local optima, the neural network still provides smooth and globally consistent predictions, significantly improving the efficiency of early-stage asteroid candidate screening. However, the deformation of the feasible region caused by path constraints leads to noticeable discrepancies in certain boundary regions, thereby limiting the applicability of the network in detailed mission design phases. Overall, the integration of neural networks with porkchop plot analysis offers an effective decision-making tool for mission designers and planetary scientists, with significant potential for engineering applications.

math.OC↗

arXiv2025

Convex Trajectory Optimization via Monomial Coordinates Transcription for Cislunar Rendezvous

This paper proposes a nonlinear guidance algorithm for fuel-optimal impulsive trajectories for rendezvous operations close to a reference orbit. The approach involves overparameterized monomial coordinates and a high-order approximation of the dynamic flow precomputed using differential algebra, which eliminates the need for real-time integration. To address non-convexity in the monomial coordinate formulation of the guidance problem, sequential convex programming is applied. Using the methodology presented in this paper, repeatedly evaluating the nonlinear dynamics is not necessary, as in shooting or collocation methods. Instead, only the monomial equations require updating between iterations, drastically reducing computational burden. The proposed algorithm is tested in the circular restricted three-body problem framework with the target spacecraft on a near-rectilinear halo orbit. The results demonstrate stability, efficiency, and low computational demand while achieving minimal terminal guidance errors. Compared to linear methods, this nonlinear convex approach exhibits superior performance in open-loop propagation of impulsive maneuvers in cislunar space, particularly in terms of accuracy. These advantages make the algorithm an attractive candidate for autonomous onboard guidance for rendezvous operations in the cislunar domain.

math.OC↗

arXiv2024

Modular pipeline for small bodies gravity field modeling: an efficient representation of variable density spherical harmonics coefficients

Proximity operations to small bodies, such as asteroids and comets, demand high levels of autonomy to achieve cost-effective, safe, and reliable Guidance, Navigation and Control (GNC) solutions. Enabling autonomous GNC capabilities in the vicinity of these targets is thus vital for future space applications. However, the highly non-linear and uncertain environment characterizing their vicinity poses unique challenges that need to be assessed to grant robustness against unknown shapes and gravity fields. In this paper, a pipeline designed to generate variable density gravity field models is proposed, allowing the generation of a coherent set of scenarios that can be used for design, validation, and testing of GNC algorithms. The proposed approach consists in processing a polyhedral shape model of the body with a given density distribution to compute the coefficients of the spherical harmonics expansion associated with the gravity field. To validate the approach, several comparison are conducted against analytical solutions, literature results, and higher fidelity models, across a diverse set of targets with varying morphological and physical properties. Simulation results demonstrate the effectiveness of the methodology, showing good performances in terms of modeling accuracy and computational efficiency. This research presents a faster and more robust framework for generating environmental models to be used in simulation and hardware-in-the-loop testing of onboard GNC algorithms.

cs.RO↗

arXiv2024

Gradient based reachability analysis for goal-oriented guidance. Application to Eros proximity operations

The recent growing interest in small solar system bodies such as asteroids and comets for scientific inspection, exploitation of resources, and planetary defense reasons is pushing the development of innovative engineering solutions to better investigate these celestial bodies. Current approaches for autonomous proximity operations often relies on tracking a reference trajectory previously designed and optimized on-ground. An innovative concept developed in recent years is proposing a paradigm shift towards autonomous goal-oriented strategies, providing the probe with high-level tasks and enabling replanning capability on-board. A gradient base reachability exploration scheme is introduced in this work to efficiently compute high reward paths with limited computational resource. The methodology is tested in a case study involving proximity operations to asteroid (433) Eros, comparing the proposed approach with other exploration techniques and showing promising results.

eess.SY↗

arXiv2024

Comparison of uncertainty propagation techniques in small-body environment

Close-proximity exploration of small celestial bodies is crucial for the comprehensive and accurate characterization of their properties. However, the complex and uncertain dynamical environment around them contributes to a rapid dispersion of uncertainty and the emergence of non-Gaussian distributions. Therefore, to ensure safe operations, a precise understanding of uncertainty propagation becomes imperative. In this work, the dynamical environment is analyzed around two asteroids, Apophis, which will perform a close flyby to Earth in 2029, and Eros, which has been already explored by past missions. The performance of different uncertainty propagation methods (Linear Covariance Propagation, Unscented Transformation, and Polynomial Chaos Expansion) are compared in various scenarios of close-proximity operations around the two asteroids. Findings are discussed in terms of propagation accuracy and computational efficiency depending on the dynamical environment. By exploring these methodologies, this work contributes to the broader goal of ensuring the safety and effectiveness of spacecraft operations during close-proximity exploration of small celestial bodies.

astro-ph.EP↗

arXiv2024

RETINA: a hardware-in-the-loop optical facility with reduced optical aberrations

The increasing interest in spacecraft autonomy and the complex tasks to be accomplished by the spacecraft raise the need for a trustworthy approach to perform Verification & Validation of Guidance, Navigation, and Control algorithms. In the context of autonomous operations, vision-based navigation algorithms have established themselves as effective solutions to determine the spacecraft state in orbit with low-cost and versatile sensors. Nevertheless, detailed testing must be performed on ground to understand the algorithm's robustness and performance on flight hardware. Given the impossibility of testing directly on orbit these algorithms, a dedicated simulation framework must be developed to emulate the orbital environment in a laboratory setup. This paper presents the design of a low-aberration optical facility called RETINA to perform this task. RETINA is designed to accommodate cameras with different characteristics (e.g., sensor size and focal length) while ensuring the correct stimulation of the camera detector. A preliminary design is performed to identify the range of possible components to be used in the facility according to the facility requirements. Then, a detailed optical design is performed in Zemax OpticStudio to optimize the number and characteristics of the lenses composing the facility's optical systems. The final design is compared against the preliminary design to show the superiority of the optical performance achieved with this approach. This work presents also a calibration procedure to estimate the misalignment and the centering errors in the facility. These estimated parameters are used in a dedicated compensation algorithm, enabling the stimulation of the camera at tens of arcseconds of precision. Finally, two different applications are presented to show the versatility of RETINA in accommodating different cameras and in simulating different mission scenarios.

astro-ph.IM↗

arXiv2024

Rapid nonlinear convex guidance using a monomial method

This paper addresses the challenge of accommodating nonlinear dynamics and constraints in rapid trajectory optimization, envisioned for use in the context of onboard guidance. We present a novel framework that uniquely employs overparameterized monomial coordinates and pre-computed fundamental solution expansions to facilitate rapid optimization while minimizing real-time computational requirements. The fundamental solution expansions are pre-computed using differential algebra. Unlike traditional approaches that repeatedly evaluate the nonlinear dynamics and constraints as part of complex shooting or collocation-based schemes, this method replaces the nonlinearity inherent to dynamics and constraint functions entirely with a computationally simpler manifold constraint. With this approach, trajectory optimization is posed efficiently as a path planning problem on the manifold. This problem is entirely convex except for the manifold constraint, readily lending itself to solution via sequential convex programming. We demonstrate the effectiveness of our approach in computing fast and accurate delta-V optimal solutions for long-range spacecraft rendezvous, including problems with nonlinear state constraints.

math.OC↗

arXiv2023

High-accuracy Vision-Based Attitude Estimation System for Air-Bearing Spacecraft Simulators

Air-bearing platforms for simulating the rotational dynamics of satellites require highly precise ground truth systems. Unfortunately, commercial motion capture systems used for this scope are complex and expensive. This paper shows a novel and versatile method to compute the attitude of rotational air-bearing platforms using a monocular camera and sets of fiducial markers. The work proposes a geometry-based iterative algorithm that is significantly more accurate than other literature methods that involve the solution of the Perspective-n-Point problem. Additionally, auto-calibration procedures to perform a preliminary estimation of the system parameters are shown. The developed methodology is deployed onto a Raspberry Pi 4 micro-computer and tested with a set of LED markers. Data obtained with this setup are compared against computer simulations of the same system to understand and validate the attitude estimation performances. Simulation results show expected 1-sigma accuracies in the order of $\sim$ 12 arcsec and $\sim$ 37 arcsec for about- and cross-boresight rotations of the platform, and average latency times of 6 ms.

cs.RO↗

arXiv2023

Characterization of Singular Arcs in Spacecraft Trajectory Optimization

Low-thrust engines for interplanetary spacecraft transfers allow cost-effective space missions with flexible launch and arrival dates. To find fuel-optimal trajectories, an optimal control problem is to be solved. Pontryagin's Maximum Principle shows that the structure of the optimal control is bang-bang with the possibility of singular arcs. Even though the latter have been heuristically shown to rarely appear in practical applications, a full theoretical characterization does not exist in the literature. As a growing number of missions are expected to adopt low-thrust engines in the near future, such study is required to have a comprehensive understanding of the problem. This work presents analytical necessary conditions for the existence of singular arcs that only depend on three physical variables and not on the costates. Moreover, it provides an analytical expression of the singular control that depends on a limited set of physical variables. This is a fundamental feature, as simple evaluation of the necessary condition and of the singular control can be performed. Finally, it provides insightful information on the reasons why singular arcs are rare and it quantifies the possibility of their occurrence.

math.OC↗

arXiv2023

An Autonomous Vision-Based Algorithm for Interplanetary Navigation

The surge of deep-space probes makes it unsustainable to navigate them with standard radiometric tracking. Self-driving interplanetary satellites represent a solution to this problem. In this work, a full vision-based navigation algorithm is built by combining an orbit determination method with an image processing pipeline suitable for interplanetary transfers of autonomous platforms. To increase the computational efficiency of the algorithm, a non-dimensional extended Kalman filter is selected as state estimator, fed by the positions of the planets extracted from deep-space images. An enhancement of the estimation accuracy is performed by applying an optimal strategy to select the best pair of planets to track. Moreover, a novel analytical measurement model for deep-space navigation is developed providing a first-order approximation of the light-aberration and light-time effects. Algorithm performance is tested on a high-fidelity, Earth--Mars interplanetary transfer, showing the algorithm applicability for deep-space navigation.

cs.CV↗

arXiv2023

Synthesis of Ballistic Capture Corridors at Mars via Polynomial Chaos Expansion

The space sector is experiencing a flourishing growth and evidence is mounting that the near future will be characterized by a large amount of deep-space missions. In the last decade, CubeSats have granted affordable access to space due to their reduced manufacturing costs compared to traditional missions. At the present-day, most miniaturized spacecraft have thus far been deployed into near-Earth orbits, but soon a multitude of interplanetary CubeSats will be employed for deep-space missions as well. Nevertheless, the current paradigm for deep-space missions strongly relies on ground-based operations. Although reliable, this approach will rapidly cause saturation of ground slots, thereby hampering the current momentum in space exploration. At the actual pace, human-in-the-loop, flight-related operations for deep-space missions will soon become unsustainable. Self-driving spacecraft are challenging the current paradigm under which spacecraft are piloted in interplanetary space. They are intended as machines capable of traveling in deep space and autonomously reaching their destination. In EXTREMA, these systems are used to engineer ballistic capture (BC), thereby proving the effectiveness of autonomy in a complex scenario. The key is to accomplish low-thrust orbits culminating in BC. For this, a bundle of BC orbits named ballistic capture corridor (BCC) can be targeted far away from a planet. To achieve BC at Mars without any a priori instruction, an inexpensive and accurate method to construct BCC directly on board is required. Therefore, granting spacecraft the capability to manipulate stable sets in order to self-compute a BCC is crucial. The goal of the paper is to numerically synthesize a corridor exploiting the polynomial chaos expansion (PCE) technique, thereby applying a suited uncertainty propagation technique to BC orbit propagation.

astro-ph.EP↗

arXiv2023

Initial Trajectory Assessment of the RAMSES Mission to (99942) Apophis

(99942) Apophis is a potentially hazardous asteroid that will closely approach the Earth on April 13, 2029. Although the likelihood of an impact has been ruled out, this close encounter represents a unique opportunity for planetary science and defense. By investigating the physical and dynamical changes induced by this interaction, valuable insights into asteroid cohesion, strength, and internal structure can be obtained. In light of these circumstances, a fast mission to Apophis holds great scientific importance and potential for understanding potentially hazardous asteroids. To this aim, ESA proposed the mission RAMSES (Rapid Apophis Mission for SEcurity and Safety) to reach Apophis before its close encounter. In this context, the paper focuses on the reachability analysis of (99942) Apophis, examining thousands of trajectories departing from Earth and reaching the asteroid before the fly-by, using a low-thrust spacecraft. A two-layer approach combining direct sequential convex programming and an indirect method is employed for fast and reliable trajectory optimization. The results reveal multiple feasible launch windows and provide essential information for mission planning and system design.

astro-ph.EP↗

arXiv2023

Qualitative study of ballistic capture at Mars via Lagrangian descriptors

Lagrangian descriptors reveal the dynamical skeleton governing transport mechanisms of a generic flow. In doing so, they unveil geometrical structures in the phase space that separate regions with different qualitative behavior. This work investigates to what extent Lagrangian descriptors provide information about non-Keplerian motion in Mars proximity, which is modeled under the planar elliptic restricted three-body problem. We propose a novel technique to reveal ballistic capture orbits extracting separatrices of the phase space highlighted by Lagrangian descriptor scalar fields. The Roberts' operator to approximate the gradient is used to detect the edges in the fields. Results demonstrate the chaos indicator ability to distinguish sets of initial conditions exhibiting different dynamics, including ballistic capture ones. Separatrices are validated against reference weak stability boundary derived on similar integration intervals. Compared to other techniques, Lagrangian descriptors provide dynamics insight bypassing the propagation of the variational equations.

math.DS↗