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Aerospace
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Advanced AI and Robotic Technologies for Spacecraft Modelling, Optimization, and...
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Advanced AI and Robotic Technologies for Spacecraft Modelling, Optimization, and Decision-Making

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 5521

Special Issue Editors

Prof. Dr. Andrew W. H. Ip

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Saskatchewan, Saskatoon S7N 5A2, Canada.
Interests: radio frequency identification (RFID) and supply chain technologies; wireless sensor networks (WSNs); decision support systems and artificial intelligence (AI); big data analytics; resilience engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhuming Bi

E-Mail Website
Guest Editor
Department of Civil and Mechanical Engineering, Purdue University Fort Wayne, Fort Wayne, IN 46805, USA
Interests: mechanical engineering; robotics; manufacturing engineering; mechatronics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai Leung Yung

E-Mail Website
Guest Editor
Hong Kong Polytechnic University, Department of Industrial and Systems Engineering, Hong Kong, China
Interests: precision engineering; product mechatronics; automatic control system; computer integrated manufacturing and management; computer vision; 3D model retrieval; logistic planning and optimization; deep space exploration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in artificial intelligence (AI) and robotics have provided new opportunities for the enhancement of spacecrafts and deep space exploration. Space missions have grown in complexity due to the interplanetary exploration of large satellite constellations for telecommunications and navigations, space stations in our orbit, the moon, Mars and other planets. This Special Issue aims to explore innovative AI and robotic techniques that could significantly enhance the autonomy of spacecraft by enabling spacecraft to operate with minimal human intervention, autonomously adapt to dynamic space environments, and optimize mission execution. These key technologies have been proven to enhance the efficiency, safety, and adaptability of spacecraft, and enhance their navigation, sensor fusion, energy management, and fault detection systems, providing greater robustness and reliability during missions. They can assist spacecraft in operating autonomously in a challenging unknown environment, improving the success of missions  and their cost-effectiveness.

The scope of this Special Issue includes, but is not limited to, the following topics: AI and robotics-based autonomous navigation and control systems, mission optimization through AI-driven decision-making, fault detection and predictive maintenance for spacecraft systems, and AI-enhanced space situational awareness. This Special Issue will also serve as a platform for sharing innovative research that promotes the future of autonomous deep space exploration missions.

The topics to be covered in this Special Issue include:

  • Autonomous navigation using reinforcement learning and deep neural networks.
  • AI and robot driven autonomous path planning and decision-making for space exploration.
  • Real-time trajectory prediction and optimization for long-duration space missions.
  • Multi-agent systems for spacecraft coordination and navigation in dynamic and uncertain environments.
  • Multi-objective optimization for resource allocation in spacecraft planning and operations.
  • Optimizing spacecraft power and energy management and fuel efficiency
  • AI-agent-based monitoring systems for spacecraft health and performance.
  • Distributed AI algorithms such as Federated Machine Learning for communication and collaboration among spacecraft.
  • Swarm intelligence for autonomous space exploration and data collection.
  • Integration of AI with attitude control and propulsion systems.
  • AI Cyber defense for space missions and operations.
  • LLM for deep space exploration planning, control and execution.

Prof. Dr. Andrew W. H. Ip
Prof. Dr. Zhuming Bi
Prof. Dr. Kai Leung Yung
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 250 words) can be sent to the Editorial Office for assessment.

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

  • space exploration
  • space missions
  • spacecraft planning
  • artificial intelligence

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Published Papers (4 papers)

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Research

18 pages, 5019 KB  
Article
Multi-Step Prediction of Airborne Load Separation Trajectory and Attitude Based on RCF-Transformer
by Xin Zheng, Xutong Zhang, Xue Zhang and Jingjie Li
Aerospace 2025, 12(12), 1086; https://doi.org/10.3390/aerospace12121086 - 4 Dec 2025
Viewed by 377
Abstract
Accurate and efficient data-driven prediction of embedded airborne load separation trajectories and attitudes can not only significantly improve the safety of the separation process but also substantially reduce reliance on costly aerodynamic simulations and wind tunnel testing. This paper proposes a real-time condition [...] Read more.
Accurate and efficient data-driven prediction of embedded airborne load separation trajectories and attitudes can not only significantly improve the safety of the separation process but also substantially reduce reliance on costly aerodynamic simulations and wind tunnel testing. This paper proposes a real-time condition fusion Transformer (RCF-Transformer) model for predicting the trajectory and attitude after load separation. Using wind-tunnel datasets of separation events obtained from Captive Trajectory Simulation (CTS), the model encodes historical sequence information while dynamically injecting real-time input conditions measured at the moment of separation into the decoder. Masked multi-head self-attention and cross-attention mechanisms are employed for collaborative learning, enabling multi-step, multi-output prediction of three-axis position and attitude. Experimental results show that, for a multi-step prediction horizon of up to T=5, the proposed model achieves an overall prediction accuracy of 95.28%. Furthermore, error-structure analyses based on Theil’s inequality coefficient decomposition, confidence intervals, and F-tests of residual variances demonstrate that the residuals are dominated by nonsystematic, high-frequency fluctuations and that the performance gains over the strongest baseline are statistically significant. These results indicate that the proposed method is highly stable and robust, providing an efficient and scalable data-driven solution for safety monitoring and decision support during the initial separation of airborne loads. Full article
(This article belongs to the Special Issue Advanced AI and Robotic Technologies for Spacecraft Modelling, Optimization, and Decision-Making)
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15 pages, 891 KB  
Article
Reinforced Model Predictive Guidance and Control for Spacecraft Proximity Operations
by Lorenzo Capra, Andrea Brandonisio and Michèle Roberta Lavagna
Aerospace 2025, 12(9), 837; https://doi.org/10.3390/aerospace12090837 - 17 Sep 2025
Viewed by 1889
Abstract
An increased level of autonomy is attractive above all in the framework of proximity operations, and researchers are focusing more and more on artificial intelligence techniques to improve spacecraft’s capabilities in these scenarios. This work presents an autonomous AI-based guidance algorithm to plan [...] Read more.
An increased level of autonomy is attractive above all in the framework of proximity operations, and researchers are focusing more and more on artificial intelligence techniques to improve spacecraft’s capabilities in these scenarios. This work presents an autonomous AI-based guidance algorithm to plan the path of a chaser spacecraft for the map reconstruction of an artificial uncooperative target, coupled with Model Predictive Control for the tracking of the generated trajectory. Deep reinforcement learning is particularly interesting for enabling spacecraft’s autonomous guidance, since this problem can be formulated as a Partially Observable Markov Decision Process and because it leverages domain randomization well to cope with model uncertainty, thanks to the neural networks’ generalizing capabilities. The main drawback of this method is that it is difficult to verify its optimality mathematically and the constraints can be added only as part of the reward function, so it is not guaranteed that the solution satisfies them. To this end a convex Model Predictive Control formulation is employed to track the DRL-based trajectory, while simultaneously enforcing compliance with the constraints. Two neural network architectures are proposed and compared: a recurrent one and the more recent transformer. The trained reinforcement learning agent is then tested in an end-to-end AI-based pipeline with image generation in the loop, and the results are presented. The computational effort of the entire guidance and control strategy is also verified on a Raspberry Pi board. This work represents a viable solution to apply artificial intelligence methods for spacecraft’s autonomous motion, still retaining a higher level of explainability and safety than that given by more classical guidance and control approaches. Full article
(This article belongs to the Special Issue Advanced AI and Robotic Technologies for Spacecraft Modelling, Optimization, and Decision-Making)
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40 pages, 7578 KB  
Article
Guidance and Control Architecture for Rendezvous and Approach to a Non-Cooperative Tumbling Target
by Agostino Madonna, Giuseppe Napolano, Alessia Nocerino, Roberto Opromolla, Giancarmine Fasano and Michele Grassi
Aerospace 2025, 12(8), 708; https://doi.org/10.3390/aerospace12080708 - 10 Aug 2025
Cited by 1 | Viewed by 1622
Abstract
This paper proposes a novel Guidance and Control architecture for close-range rendezvous and final approach of a chaser spacecraft towards a non-cooperative and tumbling space target. In both phases, reference trajectory generation relies on a Sequential Convex Programming algorithm which iteratively solves a [...] Read more.
This paper proposes a novel Guidance and Control architecture for close-range rendezvous and final approach of a chaser spacecraft towards a non-cooperative and tumbling space target. In both phases, reference trajectory generation relies on a Sequential Convex Programming algorithm which iteratively solves a non-linear optimization problem accounting for propellant consumption, relative dynamics, collision avoidance and navigation sensor pointing constraints. At close range, trajectory tracking is entrusted to a translational H-infinity controller, coupled with a quaternion-feed-back regulator for target pointing. In the final approach phase, an attitude-pointing strategy is adopted, requiring a six degree-of-freedom H-infinity controller to follow a reference roto-translational trajectory generated to ensure target-chaser motion synchronization. Performance is evaluated in a high-fidelity simulation environment that includes environmental perturbations, navigation errors, and actuator (i.e., cold gas thrusters and reaction wheels) modelling. In particular, the latter aspects are also addressed by integrating the proposed solution within a complete Guidance, Navigation and Control pipeline including a state-of-the-art LIDAR-based relative navigation filter and a dispatching function for the distribution of commanded control actions to the actuation system. A statistical analysis on 1000 simulations shows the robustness of the proposed approach, achieving centimeter-level position accuracy and sub-degree attitude accuracy near the docking/berthing point. Full article
(This article belongs to the Special Issue Advanced AI and Robotic Technologies for Spacecraft Modelling, Optimization, and Decision-Making)
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22 pages, 19937 KB  
Article
Development and Evaluation of a Two-Dimensional Extension/Contraction-Driven Rover for Sideslip Suppression During Slope Traversal
by Kenta Sagara, Daisuke Fujiwara and Kojiro Iizuka
Aerospace 2025, 12(8), 699; https://doi.org/10.3390/aerospace12080699 - 6 Aug 2025
Viewed by 792
Abstract
Wheeled rovers are widely used in lunar and planetary exploration missions owing to their mechanical simplicity and energy efficiency. However, they face serious mobility challenges on sloped soft terrain, especially in terms of sideslip and loss of attitude angle when traversing across slopes. [...] Read more.
Wheeled rovers are widely used in lunar and planetary exploration missions owing to their mechanical simplicity and energy efficiency. However, they face serious mobility challenges on sloped soft terrain, especially in terms of sideslip and loss of attitude angle when traversing across slopes. Previous research proposed using wheelbase extension/contraction and intentionally sinking wheels into the ground, thereby increasing shear resistance and reducing sideslip. Building upon this concept, this study proposes a novel recovery method that integrates beam extension/contraction and Archimedean screw-shaped wheels to enable lateral movement without rotating the rover body. The beam mechanism allows for independent wheel movement, maintaining stability by anchoring stationary wheels during recovery. Meanwhile, the helical structure of the screw wheels helps reduce lateral earth pressure by scraping soil away from the sides, improving lateral drivability. Driving experiments on a sloped sandbox test bed confirmed that the proposed 2DPPL (two-dimensional push-pull locomotion) method significantly reduces sideslip and prevents a drop in attitude angle during slope traversal. Full article
(This article belongs to the Special Issue Advanced AI and Robotic Technologies for Spacecraft Modelling, Optimization, and Decision-Making)
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