Guidance, Navigation, and Control of Spacecraft and Space Robots

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1678

Special Issue Editors


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Guest Editor
Department of Industrial Engineering, University of Naples ”Federico II“, Piazzale V. Tecchio 80, 80125 Napoli, Italy
Interests: lidar sensor data fusion; spacecraft relative navigation; spacecraft GNC; on orbit servicing; active debris removal

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Guest Editor
Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy
Interests: spacecraft guidance; navigation and control; spacecraft relative navigation; pose determination; electro-optical sensors; LIDAR; star tracker; unmanned aerial vehicles; autonomous navigation; sense and avoid; visual detection and tracking
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Special Issue Information

Dear Colleagues,

The future of space exploration depends on the development of fully autonomous and intelligent systems capable of operating safely and efficiently in complex and unpredictable environments. Missions involving on-orbit servicing, satellite formation flying, and planetary exploration require spacecraft and robotic systems that can make decisions, adapt to uncertainties, and execute tasks with minimal human intervention. Achieving high levels of autonomy is essential for enabling long-duration missions, improving operational efficiency, and reducing reliance on ground control.

To ensure the safety and reliability of autonomous space operations, innovative Guidance, Navigation, and Control (GNC) solutions are required. These must be able to handle uncertainties, optimize trajectories, and manage multi-body interactions, ensuring robust and adaptive performance in dynamic space environments.

The aim of this Special Issue is to assemble both theoretical and experimental research advancements in the dynamics, guidance, navigation, and control of space vehicles and space robots, covering topics from fundamental modeling to real-world applications.

Topics of interest include, but are not limited to, the following:

  • Orbital and attitude dynamics;
  • Autonomous Guidance, Navigation, and Control (GNC);
  • Robust and adaptive control for space systems;
  • Space robotic manipulation and assembly;
  • Autonomous space robots for planetary exploration and satellite servicing;
  • Artificial intelligence and machine learning for spacecraft GNC.

We look forward to receiving your contributions.

Dr. Alessia Nocerino
Dr. Roberto Opromolla
Guest Editors

Manuscript Submission Information

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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. Machines 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

  • guidance navigation and control
  • autonomous spacecraft operations
  • robust control
  • fault-taulerant control
  • flight dynamics
  • attitude dynamics
  • artificial intelligence for space applications
  • space robotics

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Published Papers (1 paper)

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Research

22 pages, 7765 KB  
Article
Dynamic Multi-Robot Task Allocation for Human-in-the-Loop Space Exploration: Knowledge Graph-Guided CBBA with LLM-Assisted Fault Analysis
by Hao Wang, Shuqi Xue, Hongbo Zhang, Lifen Tan, Chunhui Wang and Yan Fu
Machines 2026, 14(3), 265; https://doi.org/10.3390/machines14030265 - 26 Feb 2026
Viewed by 1001
Abstract
In dynamic and extreme space environments, current multi-robot systems inevitably encounter failures during autonomous task execution. Addressing these failures requires human-in-the-loop collaboration with astronauts, who first conduct fault analysis and then perform dynamic multi-robot task allocation (MRTA), a process critical for achieving mission [...] Read more.
In dynamic and extreme space environments, current multi-robot systems inevitably encounter failures during autonomous task execution. Addressing these failures requires human-in-the-loop collaboration with astronauts, who first conduct fault analysis and then perform dynamic multi-robot task allocation (MRTA), a process critical for achieving mission objectives. This paper proposes a Knowledge Graph-guided Consensus-Based Bundle Algorithm (KG-CBBA) that integrates astronaut fault analysis generated by large language models (LLMs) into the fault recovery process for space exploration. Firstly, a knowledge graph (KG) is constructed to encode objective constraints and semantic triples between tasks and robots, enabling a unified representation of task feasibility and utility. Secondly, a semantic-enhanced utility allocation mechanism is designed to ensure consistent, feasible, and efficient task sequences under static allocation. When dynamic tasks arrive, KG-CBBA resolves conflicts and inserts new tasks while preserving the stability of existing task sequences. Numerical simulations validate the feasibility of KG-CBBA and demonstrate its superior performance compared with consensus-based bundle algorithm (CBBA), particle swarm optimization (PSO), and greedy baselines. In addition, a user study involving 96 participants shows that KG-CBBA, when integrated with LLMs, enhances collaborative fault recovery. Overall, KG-CBBA provides an effective solution for dynamic MRTA in space exploration and supports human-in-the-loop collaboration. Full article
(This article belongs to the Special Issue Guidance, Navigation, and Control of Spacecraft and Space Robots)
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