Intelligent Multi-Agent Systems for Advanced Space Applications

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 614

Special Issue Editors


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Guest Editor
Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: multi-agent systems; finite-time control; satellite formation

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Guest Editor
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
Interests: autonomous navigation and control of aerospace vehicles; intelligent cooperative perception and autonomous decision-making of unmanned swarms; construction and smart operation & maintenance of off-earth bases

E-Mail Website
Guest Editor
Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: distributed optimization; extremum seeking; flight control
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Special Issue Information

Dear Colleagues,

The rapid expansion of space activities, including satellite constellations, on-orbit servicing, in-orbit assembly, and deep-space exploration, has generated a strong demand for intelligent multi-agent systems in space. By leveraging cooperation among multiple spacecraft, robots, and heterogeneous platforms, multi-agent systems offer new possibilities for enhancing mission robustness, flexibility, and efficiency under harsh space environments and stringent resource constraints. This Special Issue aims to gather recent advances in theories, algorithms, and experimental demonstrations of multi-agent systems for space applications. Topics of interest include, but are not limited to, cooperative guidance and control for satellite formations and constellations, spacecraft swarms, distributed estimation and relative navigation, collaborative on-orbit servicing and inspection, in-orbit assembly and manufacturing, space-based sensor networks, fault-tolerant and resilient cooperative control under communication and energy limitations, and autonomy for planetary exploration and cislunar operations. Both fundamental theoretical contributions and application-oriented studies are welcome, with particular emphasis on bridging rigorous multi-agent methodologies with realistic space mission requirements and technology constraints. The goal of this Special Issue is to provide a forum for researchers and practitioners to share new ideas, identify open problems, and promote collaborations toward the next generation of cooperative, autonomous, and resilient space systems.

Dr. Kai Zhang
Prof. Dr. Zexu Zhang
Dr. Xuefei Yang
Guest Editors

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Keywords

  • multi-agent systems
  • spacecraft swarms
  • formation flying
  • distributed guidance and control
  • on-orbit servicing
  • space autonomy

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Published Papers (1 paper)

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Research

29 pages, 3179 KB  
Article
A Convex Optimization Framework for 6-DOF Lunar Powered Descent with a Normalized Finite Rotation Parameterization
by Yandi Qiao and Zexu Zhang
Aerospace 2026, 13(4), 300; https://doi.org/10.3390/aerospace13040300 - 24 Mar 2026
Viewed by 349
Abstract
There has been increasing interest in the Moon for deep space exploration missions in the last few decades. To accommodate fuel-optimal lunar landing missions, it is essential to develop a fast trajectory planning algorithm considering constrained six-degree-of-freedom (6-DOF) dynamics. On the one hand, [...] Read more.
There has been increasing interest in the Moon for deep space exploration missions in the last few decades. To accommodate fuel-optimal lunar landing missions, it is essential to develop a fast trajectory planning algorithm considering constrained six-degree-of-freedom (6-DOF) dynamics. On the one hand, the trajectory planning problem involves a coordination of the optimal fuel consumption and the vehicle’s position, velocity, and attitude, which requires computational efficiency. On the other hand, the initialization setup of the existing sequential convex optimization method provides the linear reference trajectory, which slows down the convergence of the iterative process. In this manuscript, an improved sequential convex programming algorithm is proposed to solve the minimum-fuel 6-DOF powered descent problem. Firstly, we suggest a trajectory planning method based on a normalized finite rotation formulation, which improves the efficiency of the computational processes. Secondly, we present an initial guess method that computes the projection-analogous gradient with respect to the terminal value, accelerating the convergence of the algorithm. The simulation results show that the proposed method improves computational efficiency, indicating the potential for future applications in autonomous landing missions. Full article
(This article belongs to the Special Issue Intelligent Multi-Agent Systems for Advanced Space Applications)
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