Aerospace

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3 pages, 148 KiB

Open AccessEditorial

Guidance, Navigation, and Control for the Moon, Mars, and Beyond

by Marco Sagliano

Aerospace 2024, 11(10), 863; https://doi.org/10.3390/aerospace11100863 - 21 Oct 2024

Viewed by 728

Abstract

The interdisciplinary field known as Guidance, Navigation, and Control (GNC) has been one of the key contributors to the tremendous advancements in space exploration since the inception of the Mercury and Apollo programs [...] Full article

(This article belongs to the Special Issue GNC for the Moon, Mars, and Beyond)

Research

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27 pages, 1906 KiB

Open AccessArticle

Physical Modeling and Simulation of Reusable Rockets for GNC Verification and Validation

by Stefano Farì, Marco Sagliano, José Alfredo Macés Hernández, Anton Schneider, Ansgar Heidecker, Markus Schlotterer and Svenja Woicke

Aerospace 2024, 11(5), 337; https://doi.org/10.3390/aerospace11050337 - 24 Apr 2024

Cited by 1 | Viewed by 1796

Abstract

Reusable rockets must rely on well-designed Guidance, Navigation and Control (GNC) algorithms. Because they are tested and verified in closed-loop, high-fidelity simulators, emphasizing the strategy to achieve such advanced models is of paramount importance. A wide spectrum of complex dynamic behaviors and their [...] Read more.

Reusable rockets must rely on well-designed Guidance, Navigation and Control (GNC) algorithms. Because they are tested and verified in closed-loop, high-fidelity simulators, emphasizing the strategy to achieve such advanced models is of paramount importance. A wide spectrum of complex dynamic behaviors and their cross-couplings must be captured to achieve sufficiently representative simulations, hence a better assessment of the GNC performance and robustness. This paper focuses on of the main aspects related to the physical (acausal) modeling of reusable rockets, and the integration of these models into a suitable simulation framework oriented towards GNC Validation and Verification (V&V). Firstly, the modeling challenges and the need for physical multibody models are explained. Then, the Vertical Landing Vehicles Library (VLVLib), a Modelica-based library for the physical modeling and simulation of reusable rocket dynamics, is introduced. The VLVLib is built on specific principles that enable quick adaptations to vehicle changes and the introduction of new features during the design process, thereby enhancing project efficiency and reducing costs. Throughout the paper, we explain how these features allow for the rapid development of complex vehicle simulation models by adjusting the selected dynamic effects or changing their fidelity levels. Since the GNC algorithms are normally tested in Simulink^®, we show how simulation models with a desired fidelity level can be developed, embedded and simulated within the Simulink^® environment. Secondly, this work details the modeling aspects of four relevant vehicle dynamics: propellant sloshing, Thrust Vector Control (TVC), landing legs deployment and touchdown. The CALLISTO reusable rocket is taken as study case: representative simulation results are shown and analyzed to highlight the impact of the higher-fidelity models in comparison with a rigid-body model assumption. Full article

(This article belongs to the Special Issue GNC for the Moon, Mars, and Beyond)

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23 pages, 916 KiB

Open AccessArticle

Learning Fuel-Optimal Trajectories for Space Applications via Pontryagin Neural Networks

by Andrea D’Ambrosio and Roberto Furfaro

Aerospace 2024, 11(3), 228; https://doi.org/10.3390/aerospace11030228 - 14 Mar 2024

Cited by 3 | Viewed by 1266

Abstract

This paper demonstrates the utilization of Pontryagin Neural Networks (PoNNs) to acquire control strategies for achieving fuel-optimal trajectories. PoNNs, a subtype of Physics-Informed Neural Networks (PINNs), are tailored for solving optimal control problems through indirect methods. Specifically, PoNNs learn to solve the Two-Point [...] Read more.

This paper demonstrates the utilization of Pontryagin Neural Networks (PoNNs) to acquire control strategies for achieving fuel-optimal trajectories. PoNNs, a subtype of Physics-Informed Neural Networks (PINNs), are tailored for solving optimal control problems through indirect methods. Specifically, PoNNs learn to solve the Two-Point Boundary Value Problem derived from the application of the Pontryagin Minimum Principle to the problem’s Hamiltonian. Within PoNNs, the Extreme Theory of Functional Connections (X-TFC) is leveraged to approximate states and costates using constrained expressions (CEs). These CEs comprise a free function, modeled by a shallow neural network trained via Extreme Learning Machine, and a functional component that consistently satisfies boundary conditions analytically. Addressing discontinuous control, a smoothing technique is employed, substituting the sign function with a hyperbolic tangent function and implementing a continuation procedure on the smoothing parameter. The proposed methodology is applied to scenarios involving fuel-optimal Earth−Mars interplanetary transfers and Mars landing trajectories. Remarkably, PoNNs exhibit convergence to solutions even with randomly initialized parameters, determining the number and timing of control switches without prior information. Additionally, an analytical approximation of the solution allows for optimal control computation at unencountered points during training. Comparative analysis reveals the efficacy of the proposed approach, which rivals state-of-the-art methods such as the shooting technique and the adaptive Gaussian quadrature collocation method. Full article

(This article belongs to the Special Issue GNC for the Moon, Mars, and Beyond)

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23 pages, 1430 KiB

Open AccessArticle

Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System

by Joshua J. R. Critchley-Marrows, Xiaofeng Wu, Yosuke Kawabata and Shinichi Nakasuka

Aerospace 2024, 11(2), 153; https://doi.org/10.3390/aerospace11020153 - 14 Feb 2024

Cited by 2 | Viewed by 1728

Abstract

In recent years, the number of expected missions to the Moon has increased significantly. With limited terrestrial-based infrastructure to support this number of missions, as well as restricted visibility over intended mission areas, there is a need for space navigation system autonomy. Autonomous [...] Read more.

In recent years, the number of expected missions to the Moon has increased significantly. With limited terrestrial-based infrastructure to support this number of missions, as well as restricted visibility over intended mission areas, there is a need for space navigation system autonomy. Autonomous on-board navigation systems in the lunar environment have been the subject of study by a number of authors. Suggested systems include optical navigation, high-sensitivity Global Navigation Satellite System (GNSS) receivers, and navigation-linked formation flying. This paper studies the interoperable nature and fusion of proposed autonomous navigation systems that are independent of Earth infrastructure, given challenges in distance and visibility. This capability is critically important for safe and resilient mission architectures. The proposed elliptical frozen orbits of lunar navigation satellite systems will be of special interest, investigating the derivation of orbit determination by non-terrestrial sources utilizing celestial observations and inter-satellite links. Potential orbit determination performances around 100 m are demonstrated, highlighting the potential of the approach for future lunar navigation infrastructure. Full article

(This article belongs to the Special Issue GNC for the Moon, Mars, and Beyond)

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17 pages, 6830 KiB

Open AccessArticle

Filtering Strategies for Relative Navigation in Lunar Scenarios Using LCNS

by Marco Sabatini and Giovanni B. Palmerini

Aerospace 2024, 11(1), 59; https://doi.org/10.3390/aerospace11010059 - 8 Jan 2024

Cited by 1 | Viewed by 1346

Abstract

This paper investigates the performance of the forthcoming lunar navigation satellite systems for estimating not only the position of an onboard receiver in a lunar inertial reference frame but also, and with a consistent accuracy, the relative position between two or more spacecraft [...] Read more.

This paper investigates the performance of the forthcoming lunar navigation satellite systems for estimating not only the position of an onboard receiver in a lunar inertial reference frame but also, and with a consistent accuracy, the relative position between two or more spacecraft in proximity. This could be the case of two spacecraft performing a rendezvous, of a lander released by an orbiter, or the case of the permanent relative navigation service for a formation of satellites around the Moon. The considered observables are the pseudorange and pseudorange-rate measurements provided by the upcoming lunar communication and navigation system (LCNS), expected to support lunar missions. A single-stage Kalman filter is implemented, and its performance is demonstrated through error statistics, which are then compared to what can be achieved with sequential filtering. Full article

(This article belongs to the Special Issue GNC for the Moon, Mars, and Beyond)

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20 pages, 908 KiB

Open AccessArticle

Assessment of Asteroid Classification Using Deep Convolutional Neural Networks

by Victor Bacu, Constantin Nandra, Adrian Sabou, Teodor Stefanut and Dorian Gorgan

Aerospace 2023, 10(9), 752; https://doi.org/10.3390/aerospace10090752 - 25 Aug 2023

Cited by 2 | Viewed by 2542

Abstract

Near-Earth Asteroids represent potential threats to human life because their trajectories may bring them in the proximity of the Earth. Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass in [...] Read more.

Near-Earth Asteroids represent potential threats to human life because their trajectories may bring them in the proximity of the Earth. Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass in the Earth’s vicinity. Additionally, there is also the problem of distinguishing asteroids from other objects in the night sky, which implies sifting through large sets of telescope image data. Within this context, we believe that employing machine learning techniques could greatly improve the detection process by sorting out the most likely asteroid candidates to be reviewed by human experts. At the moment, the use of machine learning techniques is still limited in the field of astronomy and the main goal of the present paper is to study the effectiveness of deep convolutional neural networks for the classification of astronomical objects, asteroids in this particular case, by comparing some of the well-known deep convolutional neural networks, including InceptionV3, Xception, InceptionResNetV2 and ResNet152V2. We applied transfer learning and fine-tuning on these pre-existing deep convolutional networks, and from the results that we obtained, the potential of using deep convolutional neural networks in the process of asteroid classification can be seen. The InceptionV3 model has the best results in the asteroid class, meaning that by using it, we lose the least number of valid asteroids. Full article

(This article belongs to the Special Issue GNC for the Moon, Mars, and Beyond)

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26 pages, 5974 KiB

Open AccessArticle

Trajectory Optimization for the Nonholonomic Space Rover in Cluttered Environments Using Safe Convex Corridors

by Yiqun Li, Shaoqiang Liang, Jiahui Gao, Zong Chen, Siyuan Qiao and Zhouping Yin

Aerospace 2023, 10(8), 705; https://doi.org/10.3390/aerospace10080705 - 11 Aug 2023

Cited by 3 | Viewed by 2788

Abstract

Due to the limitation of space rover onboard computing resources and energy, there is an urgent need for high-quality drive trajectories in complex environments, which can be provided by delicately designed motion optimization methods. The nonconvexity of the collision avoidance constraints poses a [...] Read more.

Due to the limitation of space rover onboard computing resources and energy, there is an urgent need for high-quality drive trajectories in complex environments, which can be provided by delicately designed motion optimization methods. The nonconvexity of the collision avoidance constraints poses a significant challenge to the optimization-based motion planning of nonholonomic vehicles, especially in unstructured cluttered environments. In this paper, a novel obstacle decomposition approach, which swiftly decomposes nonconvex obstacles into their constituent convex substructures while concurrently minimizing the proliferation of resultant subobstacles, is proposed. A safe convex corridor construction method is introduced to formulate the collision avoidance constraints. The numerical approximation methods are applied to transfer the resulting continuous motion optimization problem to a nonlinear programming problem (NLP). Simulation experiments are conducted to illustrate the feasibility and superiority of the proposed methods over the rectangle safe corridor method and the area method. Full article

(This article belongs to the Special Issue GNC for the Moon, Mars, and Beyond)

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14 pages, 649 KiB

Open AccessArticle

Application of Pulsar-Based Navigation for Deep-Space CubeSats

by Andrea Malgarini, Vittorio Franzese and Francesco Topputo

Aerospace 2023, 10(8), 695; https://doi.org/10.3390/aerospace10080695 - 5 Aug 2023

Cited by 6 | Viewed by 1879

Abstract

This paper investigates the use of pulsar-based navigation for deep-space CubeSats. A novel approach for dealing with the onboard computation of navigational solutions and timekeeping capabilities of a spacecraft in a deep-space cruise is shown, and the related implementation and numerical simulations are [...] Read more.

This paper investigates the use of pulsar-based navigation for deep-space CubeSats. A novel approach for dealing with the onboard computation of navigational solutions and timekeeping capabilities of a spacecraft in a deep-space cruise is shown, and the related implementation and numerical simulations are discussed. The pulsar’s signal detection, processing, and exploitation are simulated for navigation onboard a spacecraft, thus showing the feasibility of autonomous state estimation in deep space even for miniaturized satellites. Full article

(This article belongs to the Special Issue GNC for the Moon, Mars, and Beyond)

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24 pages, 14736 KiB

Open AccessArticle

Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control

by Riccardo Santoro, Marco Pustorino and Mauro Pontani

Aerospace 2023, 10(8), 670; https://doi.org/10.3390/aerospace10080670 - 27 Jul 2023

Cited by 1 | Viewed by 963

Abstract

This study considers the problem of injecting a spacecraft into an elliptic, repeating-ground-track orbit about Mars, starting from a 4-sol highly elliptical orbit, which is a typical Martian capture orbit, entered at the end of the interplanetary transfer. The final operational orbit has [...] Read more.

This study considers the problem of injecting a spacecraft into an elliptic, repeating-ground-track orbit about Mars, starting from a 4-sol highly elliptical orbit, which is a typical Martian capture orbit, entered at the end of the interplanetary transfer. The final operational orbit has apoares corresponding to the maximum (or minimum) latitude, and nine nodal periods are flown in 5 Martian nodal days. The orbit at hand is proven to guarantee coverage properties similar to the Molniya orbit about Earth; therefore, it is especially suitable for satellites that form constellations. Low-thrust nonlinear orbit control is proposed as an affordable and effective option for orbit injection, capable of attaining significant propellant reduction if compared to alternative strategies based on chemical propulsion. This work introduces a new, saturated feedback law for the low-thrust direction and magnitude that is capable of driving the spacecraft of interest toward the operational orbit. Remarkable stability properties are proven to hold using the Lyapunov stability theory. Because no reference path is to be identified a priori, this technique represents a viable autonomous guidance strategy, even in the case of temporary unavailability of the low-thrust propulsion system or in the presence of widely dispersed initial conditions and errors on estimating orbit perturbations. Monte Carlo simulations prove that the feedback guidance strategy at hand is effective and accurate for injecting a spacecraft into the desired, repeating-ground-track operational orbit without requiring any reference transfer path. Full article

(This article belongs to the Special Issue GNC for the Moon, Mars, and Beyond)

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