arXiv subjects

Gregory Lantoine

Publications and source records attributed to Gregory Lantoine.

4 recordsLinked to original sources

arXiv2026

Station-Keeping Approach for Extremely Low Lunar Orbits with Solar Sailing

Renewed interest in cislunar space has created opportunities for sustained operations in extremely low-lunar orbits (eLLOs), where altitudes below 50~km enable close surface proximity. However, these orbits are strongly perturbed by the irregular lunar gravity field, leading to rapid eccentricity growth, high station-keeping costs or even surface impact. Recent advances in our understanding of the lunar `translation theorem' have revealed predictable behavior in the eccentricity vector, offering new opportunities for efficient control. This paper introduces a two-stage framework for solar sail station-keeping in eLLOs. First, a mixed-integer second-order cone programming (MISOCP) approach leverages the translational behavior of the eccentricity vector to identify orbit and sail configurations favorable for station-keeping. Second, a lightweight sequential convex programming (SCP) formulation refines these into high-fidelity trajectories, enabled by a recently developed lossless convexification of solar sail dynamics. A case study inspired by the Lunar Reconnaissance Orbiter (LRO) mission demonstrates that a realistic solar sail spacecraft can be maintained within the eLLO regime for at least 1~year without propellant expenditure, suggesting that longer-duration, or even indefinite station-keeping, may be feasible. The approach remains effective at reduced control update frequencies (down to monthly) and exhibits low sensitivity to uncertainties.

astro-ph.IM↗

arXiv2026

Convex Block-Cholesky Approach to Risk-Constrained Low-thrust Trajectory Design under Operational Uncertainty

Designing robust trajectories under uncertainties is an emerging technology that may represent a key paradigm shift in space mission design. As we pursue more ambitious scientific goals (e.g., multi-moon tours, missions with extensive components of autonomy), it becomes more crucial that missions are designed with navigation (Nav) processes in mind. The effect of Nav processes is statistical by nature, as they consist of orbit determination (OD) and flight-path control (FPC). Thus, this mission design paradigm calls for techniques that appropriately quantify statistical effects of Nav, evaluate associated risks, and design missions that ensure sufficiently low risk while minimizing a statistical performance metric; a common metric is Delta-V99: worst-case (99%-quantile) Delta-V expenditure including statistical FPC efforts. In response to the need, this paper develops an algorithm for risk-constrained trajectory optimization under operational uncertainties due to initial state dispersion, navigation error, maneuver execution error, and imperfect dynamics modeling. We formulate it as a nonlinear stochastic optimal control problem and develop a computationally tractable algorithm that combines optimal covariance steering and sequential convex programming (SCP). Specifically, the proposed algorithm takes a block-Cholesky approach for convex formulation of optimal covariance steering, and leverages a recent SCP algorithm, SCvx*, for reliable numerical convergence. We apply the developed algorithm to risk-constrained, statistical trajectory optimization for exploration of dwarf planet Ceres with a Mars gravity assist, and demonstrate the robustness of the statistically-optimal trajectory and FPC policies via nonlinear Monte Carlo simulation.

eess.SY↗

arXiv2025

Extensive Database of Spatial Ballistic Captures with Application to Lunar Trailblazer

For low-energy missions to the Moon and beyond, Ballistic Capture has proven to be a valuable technique for enabling orbital insertion while alleviating propulsion system requirements. This approach offers two key advantages. First, it extends the insertion window, allowing multiple maneuver opportunities to mitigate potential failures at the nominal insertion point. Second, it enables the required insertion maneuver to be distributed across multiple revolutions, reducing propulsion system constraints in terms of single-burn thrust. Prior research introduced the concept of Energy Transition Domain to support the creation of a comprehensive database of Ballistic Captures in the planar Circular Restricted Three-Body Problem. However, to apply these trajectories to a real mission scenario, a three-dimensional, spatial analysis and transition to an ephemeris model are necessary. This paper first extends the Energy Transition Domain framework to the spatial case, constructing an extensive database of spatial Ballistic Captures. Then, using Lunar Trailblazer as a case study, a subset of the trajectories is filtered using a mission-specific distance metric, and transitioned into an ephemeris model. Finally, interesting features of this subset are analyzed, and sample high-fidelity trajectories are selected as potential backup options for Lunar Trailblazer.

math.NA↗

arXiv2024

Autonomy in the Real-World: Autonomous Trajectory Planning for Asteroid Reconnaissance via Stochastic Optimization

This paper presents the development and evaluation of an optimization-based autonomous trajectory planning algorithm for the asteroid reconnaissance phase of a deep-space exploration mission. The reconnaissance phase is a low-altitude flyby to collect detailed information around a potential landing site. Although such autonomous deep-space exploration missions have garnered considerable interest recently, state-of-the-practice in trajectory design involves a time-intensive ground-based open-loop process that forward propagates multiple trajectories with a range of initial conditions and parameters to account for uncertainties in spacecraft knowledge and actuation. In this work, we introduce a stochastic trajectory optimization-based approach to generate trajectories that satisfy both the mission and spacecraft safety constraints during the reconnaissance phase of the Deep-space Autonomous Robotic Explorer (DARE) mission concept, which seeks to travel to and explore a near-Earth object autonomously, with minimal ground intervention. We first use the Multi-Spacecraft Concept and Autonomy Tool (MuSCAT) simulation framework to rigorously validate the underlying modeling assumptions for our trajectory planner and then propose a method to transform this stochastic optimal control problem into a deterministic one tailored for use with an off-the-shelf nonlinear solver. Finally, we demonstrate the efficacy of our proposed algorithmic approach through extensive numerical experiments and show that it outperforms the state-of-the-practice benchmark used for representative missions.

physics.space-ph↗