Astrodynamics最新文献

With the rapid changes of the flight environment and situation, there will be various unexpected situations while multiple missiles are performing the missions. To fast cope with the various situations in mission executions, the conventional sequential convex programming algorithm and the parallel-based sequential convex programming algorithm for multiple missiles fast trajectory replanning are proposed in this paper. The originally non-convex trajectory optimization problem is reformulated into a series of convex optimization subproblems based on the sequential convex programming method. The conventional sequential convex programming algorithm is developed through linearization, successive convexification, and relaxation techniques to solve the convex optimization subproblems iteratively. However, multiple missiles are related through various cooperative constraints. When the trajectory optimization of multiple missiles is formulated as an optimal control problem to solve, the complexity of the problem will increase dramatically as the number of missiles increases. To alleviate the coupled effect caused by multiple aerodynamically controlled missiles, the parallel-based sequential convex programming algorithm is proposed to solve the trajectory optimization problem for multiple missiles in parallel, reducing the complexity of the trajectory optimization problem and significantly shortening the computation time. Numerical simulations are provided to verify the convergence and effectiveness of the conventional sequential convex programming algorithm and the parallel-based sequential convex programming algorithm to cope with the trajectory optimization problem with various constraints. Furthermore, the optimality and the real-time performance of the proposed algorithms are discussed in comparative simulation examples.

Kinetic impact is an effective approach for studying and defending against asteroids. Impact missions have focused on asteroids with diameters larger than 100 m, whereas smaller missions have not been explored. Terminal guidance and control algorithms for small asteroids have received limited attention. China plans to conduct its first asteroid defense demonstration mission around 2025 on a 30-m-diameter asteroid. This paper presents the guidance and control algorithms for the terminal phase of this mission. The guidance formulas for impact missions are derived in this study using predictive and proportional guidance laws. Three maneuver criteria are proposed to determine the optimal timing for orbit correction, considering fuel consumption, impact accuracy, and computational cost. A continuous thrust control strategy was introduced to achieve incremental changes in velocity based on the relative motion of the impactor and target. The performance of the guidance and control algorithms was evaluated using Monte Carlo simulation, which demonstrated their effectiveness in handling uncertainties and achieving a high success rate. The results indicate that the proposed algorithm can be applied to future impact missions targeting small asteroids.

Advancements in space technology are enabling more sophisticated spacecraft designs with time-varying spacecraft configurations to account for varying power considerations. For example, articulating solar arrays that rotate about an axis fixed with respect to the hub can track the Sun at all times. Electric thrusters also have become omnipresent in modern spacecraft designs. Because these thrusters operate over long time windows, and the thrust vector must at all times be aligned with a specific inertial orientation, the optimal spacecraft attitude reference needs to accommodate the thruster too. Multiple pointing constraints pose a challenge to the attitude reference generation problem, because the attitude is characterized by three degrees of freedom, whereas the different constraints are often described by overdetermined systems of multiple equations. This paper leverages a range of attitude parameterizations to provide a mathematical description of the solution spaces of the constraints outlined above. When the intersection space is nonzero, it is possible to compute a solution that satisfies multiple constraints simultaneously. Conversely, an ordered list of pointing priorities is required in order to enforce the most important requirements, and reformulate the subsequent ones in terms of constrained optimization problems. The attitude guidance formulation is written in a general manner, which makes it applicable to a broad range of mission configurations.

Nowadays, the use of Machine Learning (ML) onboard Earth Observation (EO) satellites has been investigated for a plethora of applications relying on multispectral and hyperspectral imaging. Traditionally, these studies have heavily relied on high-end data products, subjected to extensive pre-processing chains natively designed to be executed on the ground. However, replicating such algorithms onboard EO satellites poses significant challenges due to their computational intensity and need for additional metadata, which are typically unavailable on board. Because of that, current missions exploring onboard ML models implement simplified but still complex processing chains that imitate their on-ground counterparts. Despite these advancements, the potential of ML models to process raw satellite data directly remains largely unexplored. To fill this gap, this paper investigates the feasibility of applying ML models directly to Sentinel-2 raw data to perform thermal hotspot classification. This approach significantly limits the processing steps to simple and lightweight algorithms to achieve real-time processing of data with low power consumption. To this aim, we present an end-to-end (E2E) pipeline to create a binary classification map of Sentinel-2 raw granules, where each point suggests the absence/presence of a thermal anomaly in a square area of 2.5 km. To this aim, lightweight coarse spatial registration is applied to register three different bands, and an EfficientNet-lite0 model is used to perform the classification of the various bands. The trained models achieve an average Matthew’s correlation coefficient (MCC) score of 0.854 (on 5 seeds) and a maximum MCC of 0.90 on a geographically tripartite dataset of cropped images from the THRawS dataset. The proposed E2E pipeline is capable of processing a Sentinel-2 granule in 1.8 s and within 6.4 W peak power on a combination of Raspberry PI 4 and CogniSat-XE2 board, demonstrating real-time performance.

The influence of a disturbing gravity field on the impact points of long-range vehicles (LRVs) has become increasingly prominent, which is an important factor affecting the accuracy of impact point prediction (IPP). To achieve high-accuracy and fast IPP for LRVs under the influence of a disturbing gravity field, a data-driven multi-level IPP method is proposed to balance the prediction accuracy and real-time performance. At the first level, the impact point of the current flight state is predicted based on elliptical trajectory theory, and the impact deviation of the elliptical trajectory (ID-ET) is calculated. At the second and third levels, a neural network (NN) model is established to learn the ID-ET caused by the J₂ term and re-entry aerodynamic drag as well as that caused by the disturbing gravity field. To improve the NN prediction performance, an auxiliary circle is applied to decouple the ID-ET. To reduce the difficulty of NN learning, a training strategy is designed based on the idea of curriculum learning, which improves training accuracy. At the same time, a hybrid sample generation strategy is proposed to improve the NN generalization ability. A detailed simulation experiment is designed to analyze the advantages and computational complexity of the proposed method. The simulation results showed that the proposed model has a high prediction accuracy, strong generalization ability, and good real-time performance under the influence of the disturbing gravity field and re-entry aerodynamic drag. Among the 317,360 samples contained in the training and test sets, the 3σ prediction error was 6.21 m. On an STM32F407 single-chip microcomputer, the IPP required 3.415 ms. The proposed method can provide support for the design of guidance algorithms and is applicable to engineering practice.

Escalating concerns about climate change and the limitations of alternative energy sources have renewed interest in space-based solar power. Among numerous concepts proposed for space-based solar power, the modular flat-plane sandwich configuration has emerged as a promising candidate, owing to its structural simplicity that lends itself well to recent advancements in wireless power transmission and on-orbit robotic assembly. As a consequence of its simple structure, there are also new challenges with respect to attitude design due to the coupling of sunlight collection and power beaming on opposing sides of the flat plane. This paper develops a versatile attitude trajectory optimization approach that maximizes power-beaming efficiency for modular space-based solar power configurations in Molniya orbits while minimizing the attitude control effort. The developed optimization approach employs a genetic algorithm to study two attitude design strategies. The first attitude design strategy investigates initially spinning configurations about the ecliptic normal and compares the power-beaming efficiency against solutions using near-optimal attitude and spin axis parameters for a one-year period determined through optimization. The second attitude design strategy employs multiple runs of a genetic algorithm discretized at different time of the year, each determining an inertially fixed attitude optimized for a one-month period. These attitudes are then used to design attitude maneuvers, each with an axis and rate of actuation designed analytically. The outcomes of this study determined several viable attitude trajectory optimization and design strategies for multiple space-based solar power system configurations, which generate attitude trajectories that maximize power beaming in Molniya orbits while minimizing attitude control effort.

Operations in proximity of minor bodies demands high levels of autonomy to achieve cost-effective safe and reliable solutions. Autonomous trajectory and operations planning capability plays a pivotal role in this. A goal-oriented guidance strategy for on-board implementation is presented in this paper to achieve high level mission objectives with impulsive control capability. The methodology is based on abstract reachability analysis performed on the control domain combining model predictive control theory with artificial potential fields algorithms. The formulation of the optimization problem in a general and flexible way allows to target different goals while being compliant with an arbitrary number of mission constraints. In particular, two main contributions to the field are proposed in this work: a way of embedding non-uniform observation constraints in the formulation to deal with challenging illumination conditions, and the inclusion of specific operational constraints to be compliant both with ground operations and on-board replanning. The methodology is applied to the Milani mission, one of the two Hera’s CubeSats, targeting a global coverage of the main attractor, Didymos, and detailed observations of specific features on the secondary asteroid, Dimorphos. The authors, being involved in the mission analysis, image processing, and GNC design of the platform, believe that this methodology also represents a viable tool for efficiently generating flight dynamics references during operations. Different metrics are investigated to achieve mission objectives leading to four application scenarios that are discussed in this work. Results are compared in terms of computational cost, convergence properties and efficiency. These results represent a step forward in enabling autonomous guidance capability for CubeSats proximity operations.

A lunar global positioning–navigation–timing (PNT) and communication system can greatly support the exploration and exploitation of the Moon. In this study, the application of the stable orbits of the L₁ and L₂ halo families and the unstable orbits of L₃ in the Earth–Moon system is analyzed, and a design is proposed. L₃ halo orbits are considered for a continuous line-of-sight satellite infrastructure for the Earth–Moon communication, thereby providing an opportunity for ground stations on the Earth to participate in lunar missions even if they do not directly see the Moon. In this study, a constellation of 26 satellites distributed over a lunar segment, made of four halo orbits of L₁ and L₂, and a terrestrial segment, made of two halo orbits of L₃, is designed; this constellation facilitates global and continuative Earth–Moon communication and provides accurate and continuous lunar PNT service. According to a station-keeping analysis in the framework of the elliptical restricted three-body problem, the maintenance cost for approximately 60 d was 0.76 m/s for the lunar segment and 0.02 m/s for the terrestrial segment.

The increasing population of resident space objects is currently fostering many space surveillance initiatives. In this framework, on-ground multireceiver radars allow to reconstruct the target angular track, but the array configuration may cause the presence of multiple solutions and, if no pass prediction is available, the ambiguity cannot be solved a-priori. This work proposes an evolution of the Music Approach for Track Estimate and Refinement (MATER) algorithm. Given two different signals reflected by the same target, at each observation epoch their Direction Of Arrival (DOA) is estimated from the signal Covariance Matrix (CM) through the MUltiple SIgnal Classification (MUSIC) algorithm. Then, the possible ambiguous estimations are solved through the delta-k technique: the correct DOA is considered as the one featuring the smallest angular deviation comparing the two CM results. This process is repeated for all the epochs, and the DOAs are clustered according to the RANdom SAmple Consensus (RANSAC) algorithm. Finally, the most populated cluster is considered as the correct one, and the angular track is computed through a time regression of the two angular coordinates. The evolution of MATER algorithm is tested through numerical simulations. The algorithm converges to the correct solution in 100% of the cases, with an angular accuracy in the order of 1–10 mdeg.

Conjunctions between spacecraft are increasingly common across orbital regimes, demanding reliable and efficient collision avoidance (COLA) strategies. The typical solution to the COLA problem is to compute a maneuver that reduces the collision risk while minimizing fuel expenditure. If the spacecraft is in a continuously propelled phase, this approach must be modified since the thrust profile is determined a priori, aiming to reach a final orbit. This work proposes using convex optimization to solve the short-term encounter COLA problem in such conditions. The optimization problem is two-fold: (i) the collision risk must be reduced below a certain threshold; (ii) after the conjunction, the spacecraft must be rerouted into the nominal trajectory. By casting the problem as a sequential convex program, the original nonlinear optimal control problem is solved iteratively, recovering an optimal solution. Within the second-order cone program framework, three strategies are proposed to address the problem: (i) determining the optimal switch-off time to avoid the collision while minimizing deviation from the nominal trajectory; computing a new thrust profile, deviating as little as possible from the original one in terms of (ii) vector or (iii) angular difference. The three strategies are tested on practical operational scenarios, using the nominal thrust profile from a low-thrust geostationary transfer orbit and conjunction details from a conjunction data message.