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Aerospace
Special Issues
Intelligent Perception, Decision and Autonomous Control in Aerospace
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Intelligent Perception, Decision and Autonomous Control in Aerospace

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 402

Special Issue Editors

Prof. Dr. Jianbin Qiu

E-Mail Website
Guest Editor
School of Astronautics, Harbin Institute of Technology, Harbin 150080, China
Interests: intelligent control systems; autonomous control of spacecraft; artificial intelligence
Prof. Dr. Chanying Li

E-Mail Website
Guest Editor
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Interests: intelligent control; game theory; aerospace control
Prof. Dr. Jose de Jesus Rubio

E-Mail Website
Guest Editor
School of Engineering, National Polytechnic Institute, Mexico City 02250, Mexico
Interests: intelligent control; neural network; optimal control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the growing congestion of orbital space has posed a significant challenge to the safe operation of spacecraft. With the increasing complexity of space missions, it is essential to develop intelligent perception, autonomous decision-making, and advanced control strategies that enable spacecraft to navigate with precision and adapt to unpredictable environments without constant human intervention. The integration of machine learning, reinforcement learning, and evolutionary computation into spacecraft systems has thus become a focal point of research.

The aim of this Special Issue is to provide a platform for scientists, engineers, and practitioners throughout the world to present recent research on intelligent perception, autonomous decision-making, orbital game theory, advanced control, mission planning, and the design of spacecraft systems. We particularly welcome the submission of papers that present newly emerging fields and applications.

Finally, I would like to thank Dr. Min Li for his dedication to and assistance with this Special Issue.

Prof. Dr. Jianbin Qiu
Prof. Dr. Chanying Li
Prof. Dr. Jose de Jesus Rubio
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

  • intelligent perception
  • pose estimation
  • intention recognition
  • orbit planning
  • intelligent reasoning
  • orbital game
  • attitude/orbit control
  • mission planning
  • performance optimization
  • resource allocation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

43 pages, 5199 KiB  
Article
An Actor–Critic-Based Hyper-Heuristic Autonomous Task Planning Algorithm for Supporting Spacecraft Adaptive Space Scientific Exploration
by Junwei Zhang and Liangqing Lyu
Aerospace 2025, 12(5), 379; https://doi.org/10.3390/aerospace12050379 - 28 Apr 2025
Viewed by 103
Abstract
Traditional spacecraft task planning has relied on ground control centers issuing commands through ground-to-space communication systems; however, as the number of deep space exploration missions grows, the problem of ground-to-space communication delays has become significant, affecting the effectiveness of real-time command and control [...] Read more.
Traditional spacecraft task planning has relied on ground control centers issuing commands through ground-to-space communication systems; however, as the number of deep space exploration missions grows, the problem of ground-to-space communication delays has become significant, affecting the effectiveness of real-time command and control and increasing the risk of missed opportunities for scientific discovery. Adaptive Space Scientific Exploration requires that spacecraft have the ability to make autonomous decisions to complete known and unknown scientific exploration missions without ground control. Based on this requirement, this paper proposes an actor–critic-based hyper-heuristic autonomous mission planning algorithm, which is used for mission planning and execution at different levels to support spacecraft Adaptive Space Scientific Exploration in deep space environments. At the bottom level of the hyper-heuristic algorithm, this paper uses the particle swarm optimization algorithm, grey wolf optimization algorithm, differential evolution algorithm, and positive cosine optimization algorithm as the basic operators. At the high level, a reinforcement learning strategy based on the actor–critic model is used, combined with the network architecture, to construct a framework for the selection of advanced heuristic algorithms. The related experimental results show that the algorithm can meet the requirements of Adaptive Space Scientific Exploration, and exhibits a quality solution with higher comprehensive evaluation in the test. This study also designs an example application of the algorithm to a space engineering mission based on a collaborative sky and earth control system to demonstrate the usability of the algorithm. This study provides an autonomous mission planning method for spacecraft in the complex and ever-changing deep space environment, which supports the further construction of spacecraft autonomous capabilities and is of great significance for improving the exploration efficiency of deep space exploration missions. Full article
(This article belongs to the Special Issue Intelligent Perception, Decision and Autonomous Control in Aerospace)
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