Astrodynamics最新文献

In this work, we presents a novel transformer-based spacecraft pose estimation network, SPTN, for space-object tracking. SPTN consists of a transformer-based backbone with the proposed WBlock module, an innovative neck structure, LBiFPN, and a multitask head. Such a framework will be more effective in feature extraction and fusion while maintaining a lightweight structure compared to CNN-based methods. The proposed WBlock is embedded with window partitioning and hierarchical attention mechanisms to enhance feature extraction. The novel LBiFPN neck module is designed to fuse features at different levels, facilitating a deeper feature integration. Extensive experiments are conducted on the SPEED+ and SHIRT datasets to evaluate the performance of the proposed method. The results show that our SPTN model achieved competitive detection accuracy compared to current state-of-the-art methods while maintaining minimum parameters.

This paper conducts a comprehensive study on the multi-constrained two-on-one impulsive orbital pursuit–evasion game (OPEG). Firstly, considering constraints such as maneuverability, fuel reserves, and mission duration, a mathematical game model for the two-on-one impulsive OPEG is established, which transforms the two-on-one impulsive OPEG, where cooperation and competition coexist, into a multi-constrained three-party optimization problem suitable for solving with multi-agent deep reinforcement learning. Then, an intelligent solution method for cooperative game strategies based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is proposed. In the reward function design section, a reward function based on fixed-time triggering is introduced to address the information loss problem caused by long impulse intervals. To ensure good convergence of the algorithm and guide the spacecraft to learn effective cooperative strategies during training, an immediate reward function is designed, incorporating outcome rewards, guidance rewards, and cooperative rewards. Numerical simulations validate the feasibility and effectiveness of the proposed method. To further analyze the cooperative mechanisms learned by the spacecraft during algorithm training, a comparative experiment with the one-on-one impulsive OPEG is designed. The experimental results demonstrate that the two pursuers in the two-on-one impulsive OPEG not only develop various strategies such as “pre-emptive interception”, “pincer interception”, and “trailing pursuit” during training, but also improve mission success rates and reduce mission durations through coordinated efforts. Additionally, this paper reveals the impact of the relative initial state distribution between the two pursuing spacecraft and the evading spacecraft on the effectiveness of cooperation.

Modular robots can adapt to various task scenarios and environments by rearranging their structural components and dimensions. However, their potential for versatility has not been fully explored in nonlaboratory environments, particularly on unstructured planetary terrains. This difficulty lies in the fact that the morphology and behavior of modular robots are highly intertwined with the terrain on which they stand. Achieving a concurrent design of robot configuration and motion strategy is essential to preserve the optimality of the reconfiguration schemes, as the completeness of the solution space can only be guaranteed if both are considered simultaneously. However, it is also challenging owing to the enormous joint candidate space. Existing research based on evolutionary algorithms, machine learning, or hybrid methods suffer from a range of limitations such as low goal-orientation and inadequate feature utilization. To this end, we incorporate a terrain-guided module and the spatio-temporal graph convolutional network architecture into the co-optimization framework to guide the optimization using agent features in both the spatial and temporal dimensions, which further accelerates the search and enhances the adaptability of modular robots. We conducted simulations using the Webots platform to validate our proposed method. Comparative studies showed that our framework produced reconfiguration schemes that exhibit highly efficient and appropriate morphology and behavioral adaptations toward several terrains.

Orbit insertion uncertainties can significantly affect the configuration stability of heliocentric gravitational wave (GW) observatories, necessitating valid configuration uncertainty propagation techniques. Current configuration uncertainty propagation methods suffer from drawbacks related to their high computational complexity. To this end, this study proposes a novel configuration uncertainty propagation method for heliocentric GW observatories to reduce the computational complexity. First, the angular momentum and phase angle were found to be the two core variables for the orbit propagation of heliocentric GW observatories, the analytical solutions of which were derived using a perturbation-averaging technique. Subsequently, a first-order sensitivity matrix of the configuration stability index with respect to the initial states was derived based on the analytical solutions of the angular momentum and phase angle. Semi-analytically sensitive directions were obtained based on the derived sensitivity matrix, which was further employed to reduce the terms of configuration uncertainty propagation. The performance of the proposed method was validated using the example of a Laser Interferometer Space Antenna (LISA) project by comparing it with several competitive methods. The numerical results show that the proposed reduced-order method has a relative error close to that of the conventional full-state method and reduces the computational complexity by more than 46.

Singular perturbation theory is applied for analytically estimating the effects of small acceleration terms on spacecraft orbital dynamics, which are representative of the action of a drag force or of an electric low-thrust propulsion system. The effects of density variation with altitude and thrust magnitude as a function of distance from the primary body are included in the analysis. Comparisons with results obtained from numerical integration and other analytical and semianalytical methods demonstrate the validity of the approach in predicting the secular variation of orbit parameters in planar motion, with advantages in terms of accuracy and/or computational cost with respect to other approximations.

This paper investigates the tracking control problem of satellite swarm reconstruction, where relative motion information in unknown and coexisting perturbations in both the observer and controller is addressed. Considering the multisource complex disturbances due to the space environment effect, a planned trajectory of a member satellite is given to satisfy the obstacle avoidance and dynamic constraints. Then, considering the coexistence of additive and multiplicative perturbations, a so-called dual hybrid nonfragile controller, i.e., a hybrid nonfragile state observer-based hybrid nonfragile tracking controller, is developed to achieve the tracking control performance of member satellites along the planned trajectory. Lyapunov stability analysis is performed to demonstrate the system stability via linear matrix inequalities (LMIs); thus, the satellite swarm can achieve the optimal trajectory reconstruction from its initial position to the desired position with topological constraints. Finally, numerical simulations are performed to demonstrate the effectiveness and superiority of the developed dual hybrid nonfragile control approach.

Europa’s plume eruption activity provides a unique opportunity to explore its subsurface ocean and potential biological activity. Based on in-situ detection data from the Galileo spacecraft, it has been concluded that Europa’s plumes have an inclined ejection structure. On this basis, in this study, large-scale simulations of dust dynamics were performed, and the influence of Europa’s plume structures and parameters on dust dynamics was analyzed. Compared with the nearly circular deposition area from vertical eruptions, the surface deposition of plume particles from an inclined eruption exhibits an offset in the tilt direction. Additionally, the plume particle spatial distribution from inclined eruptions is sparser at higher altitudes than that from vertical eruptions. The size of the deposition area is significantly influenced by the gas velocity, whereas the degree of deposition concentration is affected by the critical grain radius and the size distribution exponent, which is similar to that of Enceladus. Lower gas velocity and smaller critical grain radius also result in a sparser distribution at high altitudes. Furthermore, the surface deposition and spatial distribution of the double eruption locations are also considered.

This study discusses the relative motion among Earth–Moon resonant solar-sail halo orbits of the same period, which are promising candidates for formation-flying missions. Because the restricted multibody problem admits no closed-form solutions, this study proposes a semi-analytic method for the bound analysis of relative motion using the jet transport technique. In particular, the bounds on the coordinate components and inter-spacecraft distance are explicitly specified as high-order Taylor polynomials in terms of solar-sail parameters that characterize the families of resonant halo orbits. These semi-analytic closed-form solutions allow rapid evaluations of the maximum, minimum, and mean distances among nearby solar-sail halo orbits, which are crucial for formation configuration design and control. Based on illustrative examples, we verify the effectiveness of the proposed semi-analytic bound-analysis method.

Aiming at the problem of efficient collaborative decision-making among multiple satellites in multi-satellite orbital interception mission, a multi-satellite cooperative orbital interception strategy based on attention mechanism and deep reinforcement learning is proposed. Firstly, considering the orbital dynamics, maneuverability, mission duration, and collision avoidance constraints faced in orbital interception mission, a Markov decision process is designed, and a multi-satellite game strategy solution framework based on the actor–critic network is built; then, a guided reward function is designed to effectively guide the pursuit satellite to approach and intercept the escaping satellite to accelerate the convergence speed of the algorithm; finally, the attention mechanism is used to capture the potential relationship between satellites and generate coded information with biased attention effect, which helps satellites form an efficient collaborative interception strategy. The simulation experiments show that the trained satellites can conduct autonomous learning and decision-making in a dynamic and uncertain environment. In orbital interception mission, the pursuit satellite can adopt an effective collaborative strategy to use the advantage of quantity to make up for the disadvantage of speed, maintain a high mission success rate, and a series of intelligent game behaviors emerge.

The coordination of multiple Earth-observing satellites presents a significant scheduling challenge. This paper introduces a fully distributed autonomous scheduling solution that utilizes a learning-based mechanism through an independent proximal policy optimization (IPPO) algorithm. Each satellite independently makes decisions regarding tasks, such as imaging, desaturation, and charging, while adapting to dynamic environmental changes to enhance its real-time constellation scheduling performance. The proposed fully distributed strategy enables individual satellites to update their policies based solely on their observations. The only requirement is the unidirectional broadcast of a completion flag upon target observation. This approach distinguishes itself from traditional centralized methods, thus enhancing the overall robustness and security of the system. In simulations, our strategy exhibited effective observational mission planning results for major cities worldwide. The results show that the proposed method addresses both autonomous scheduling and significantly improves constellation performance and reliability.