CubeSat Autonomous Navigation and Guidance for Low-Cost Asteroid Flyby Missions

Machuca, Pablo Martínez-Gijón; Sánchez, J.P.

doi:10.2514/1.a34986

Cited by 3 publications

(1 citation statement)

References 37 publications

(62 reference statements)

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“…Pontani et al [10] found both impulsive and finite thrust, fuel-optimal rendezvous trajectories by using a particle swarm algorithm. Other works use alternative deterministic and heuristic methods for rendezvous optimization and guidance, such as the glideslope multipulse technique [11], optimally timed trajectory correction maneuvers [12], H ∞ and µ-synthesis techniques [13]. However, in actual operational scenarios, rendezvous trajectories are affected by orbit perturbations, which make both the HCW linear equations and the Keplerian nonlinear equations relatively inaccurate for precise orbit rendezvous.…”

Section: Introductionmentioning

confidence: 99%

An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats

et al. 2022

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Modern space missions often require satellites to perform guidance, navigation, and control tasks autonomously. Despite their limited resources, small satellites are also involved in this trend, as in-orbit rendezvous and docking maneuvers and formation flying have become common requirements in their operational scenarios. A critical aspect of these tasks is that these algorithms are very much intertwined with each other, although they are often designed completely independently of one another. This paper describes the design and simulation of a guidance and relative navigation architecture for the rendezvous of two cooperating CubeSats. The integration of the two algorithms provides robustness to the solution, by simulating realistic levels of noise and uncertainty in the guidance law implementation. The proposed guidance law is derived based on the linearized equations of orbital motion, written in terms of spherical coordinates. The trajectory is iteratively corrected at a fixed time step, so that errors from the navigation and the initial orbital condition can be recovered. The navigation algorithm processes the bearing and range measurements from a camera and an intersatellite link through an unscented filter to provide the information required from the guidance law. A Monte Carlo campaign based on a 3-DOF simulation demonstrates the effectiveness of the proposed solution.

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Section: Introductionmentioning

confidence: 99%

An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats

et al. 2022

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Reinforcement-Learning-Based Robust Guidance for Asteroid Approaching

Yang,

Hu,

et al. 2024

Journal of Guidance, Control, and Dynamics

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This paper presents a reinforcement-learning (RL)-based robust low-thrust guidance method for asteroid approaching under process uncertainties. Markov decision processes with stochastic dynamics are formulated for RL. To overcome the problem of low terminal accuracy in RL-based transfer trajectory design, robust zero-effort-miss/zero-effort-velocity (R-ZEM/ZEV) guidance is proposed. Originally, an eigenvalue-related term is defined according to the stability conditions of the ZEM/ZEV feedback system and chosen as the learning parameter, which can significantly improve the robustness of the agent to process uncertainties under the low-thrust magnitude constraint. Moreover, the navigation performance of the asteroid approaching is modeled via the Fisher information matrix and incorporated in the reward function design, which enables optimizing the optical observation performance together with the propellant cost. Thereafter, the proximal policy optimization is adopted to train an RL agent that can efficiently deal with the uncertainties. The effectiveness and efficiency of the proposed method are validated through simulations of a low-thrust spacecraft approaching the asteroid Bennu. The promising results indicate that the proposed method can not only deal with various uncertainties efficiently and autonomously but can also guarantee high terminal accuracy.

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A Robust Star Identification Algorithm Based on a Masked Distance Map

Yuan

Wang

2022

Remote Sensing

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The authors of this paper propose a robust star identification algorithm for a ‘Lost-In-Space’-mode star tracker for lost-cost CubeSat missions. A two-step identification framework and an embedded validation mechanism were designed to accelerate the process. In the first step, a masked distance map is designed to provide a shortlist of stars, and the embedded fast validation process enables the direct output of validated stars before the second step. In the second step, local similarity is utilized to select a set of stars from those shortlisted, and the final validation procedure rejects all unsatisfactory stars. This algorithm can provide reliable and robust recognition even when the captured star images include severe star positioning errors, missing stars and false stars. The proposed algorithm was verified by a simulation study under various conditions. As low-cost star sensors face harsh and unknown environments during deep space CubeSat missions such as asteroid exploration, the proposed algorithm with high robustness will provide an important function.

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CubeSat Autonomous Navigation and Guidance for Low-Cost Asteroid Flyby Missions

Cited by 3 publications

References 37 publications

An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats

An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats

Reinforcement-Learning-Based Robust Guidance for Asteroid Approaching

A Robust Star Identification Algorithm Based on a Masked Distance Map

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