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Aerospace
Special Issues
Spacecraft Trajectory Design
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Spacecraft Trajectory Design

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Astronautics & Space Science".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 169

Special Issue Editors

Prof. Dr. Mingying Huo

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: dynamics and control of electric solar wind sail; shape-based trajectory optimization method
Prof. Dr. Gang Zhang

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: orbit dynamics and control; trajectory optimization; mission planning; collaborative guidance and control
Dr. Zichen Fan

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: deep space exploration trajectory optimization and intelligent mission decision-making

Special Issue Information

Dear Colleagues,

Spacecraft trajectory design represents a pivotal aspect of aerospace engineering, focusing on the development of optimal orbital trajectories that satisfy the multifaceted requirements and constraints of space missions. As aerospace technology advances, the complexity of trajectory design has escalated, carving out the need for multidisciplinary approaches that integrate celestial mechanics, dynamics and control, optimization algorithms, and propulsion technology. This research must address not only the feasibility of orbital trajectories but also critical factors such as fuel efficiency, mission duration, orbit accuracy, and the autonomous navigation capabilities of spacecraft. In recent years, the increasing prevalence of complex missions, including deep space exploration, small celestial body exploration, and constellation deployment, has introduced new challenges in the field of trajectory design. These challenges encompass multi-objective optimization, trajectory generation under intricate constraints and the integration of novel propulsion technologies. Moreover, concurrently, artificial intelligence and machine learning have emerged as powerful tools, offering innovative solutions and enhancing the efficiency with which complex problems might be addressed.

Looking ahead, spacecraft trajectory design will remain a cornerstone of successful space mission implementations, driven by both technological innovation and evolving mission requirements. Continued advancements in this field are expected to further optimize mission performance and expand the boundaries of space exploration. This Special Issue focuses on the recent advances and novel algorithms of spacecraft-trajectory-related research. Authors are invited to submit full research articles and review manuscripts addressing (but not limited to) the following topics:

  • Efficient trajectory design for deep space exploration missions;
  • Spacecraft game-based orbit design;
  • Space situation awareness;
  • Spacecraft formation trajectories;
  • Space manipulator trajectory design.

Prof. Dr. Mingying Huo
Prof. Dr. Gang Zhang
Dr. Zichen Fan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • efficient trajectory design for deep space exploration missions
  • spacecraft game-based orbit design
  • space situation awareness
  • spacecraft formation trajectories
  • space manipulator trajectory design

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Published Papers (2 papers)

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Research

22 pages, 3669 KiB  
Article
Fuel-Optimal In-Track Satellite Formation Trajectory with J2 Perturbation Using Pontryagin Neural Networks
by Morgan Choi and Seonho Lee
Aerospace 2025, 12(4), 360; https://doi.org/10.3390/aerospace12040360 - 21 Apr 2025
Abstract
Satellite formation flying faces significant challenges in maintaining its desired configurations due to various orbital perturbations, particularly in low-Earth-orbit environments. This paper presents a novel approach to generating fuel-optimal reference trajectories for in-track satellite formations by incorporating both the Earth’s oblateness ( [...] Read more.
Satellite formation flying faces significant challenges in maintaining its desired configurations due to various orbital perturbations, particularly in low-Earth-orbit environments. This paper presents a novel approach to generating fuel-optimal reference trajectories for in-track satellite formations by incorporating both the Earth’s oblateness (J2 perturbation) and the inherent nonlinearity of the two-body problem. The resulting indirect optimal control problem is solved using Pontryagin Neural Networks (PoNNs). The proposed method transforms the conventional two-point boundary value problem into a mathematical programming problem, enabling the efficient computation of optimal trajectories. The effectiveness of our approach is validated through extensive numerical simulations at different inclinations of the chief satellite (0–90°) and cross-track separation distances (1–400 km), demonstrating significant reductions in annual fuel consumption compared to conventional approaches. The feasibility of these optimal trajectories is verified through closed-loop simulations using a PD controller, confirming their practical applicability in realistic mission scenarios. This research contributes to enhancing the long-term sustainability of satellite formation flying missions by optimizing fuel efficiency while maintaining precise formations. Full article
(This article belongs to the Special Issue Spacecraft Trajectory Design)
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25 pages, 1565 KiB  
Article
Space Trajectory Planning with a General Reinforcement-Learning Algorithm
by Andrea Forestieri and Lorenzo Casalino
Aerospace 2025, 12(4), 352; https://doi.org/10.3390/aerospace12040352 - 16 Apr 2025
Viewed by 102
Abstract
Space trajectory planning is a complex combinatorial problem that requires selecting discrete sequences of celestial bodies while simultaneously optimizing continuous transfer parameters. Traditional optimization methods struggle with the increasing computational complexity as the number of possible targets grows. This paper presents a novel [...] Read more.
Space trajectory planning is a complex combinatorial problem that requires selecting discrete sequences of celestial bodies while simultaneously optimizing continuous transfer parameters. Traditional optimization methods struggle with the increasing computational complexity as the number of possible targets grows. This paper presents a novel reinforcement-learning algorithm, inspired by AlphaZero, designed to handle hybrid discrete–continuous action spaces without relying on discretization. The proposed framework integrates Monte Carlo Tree Search with a neural network to efficiently explore and optimize space trajectories. While developed for space trajectory planning, the algorithm is broadly applicable to any problem involving hybrid action spaces. Applied to the Global Trajectory Optimization Competition XI problem, the method achieves competitive performance, surpassing state-of-the-art results despite limited computational resources. These results highlight the potential of reinforcement learning for autonomous space mission planning, offering a scalable and cost-effective alternative to traditional trajectory optimization techniques. Notably, all experiments were conducted on a single workstation, demonstrating the feasibility of reinforcement learning for practical mission planning. Moreover, the self-play approach used in training suggests that even stronger solutions could be achieved with increased computational resources. Full article
(This article belongs to the Special Issue Spacecraft Trajectory Design)
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