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Aerospace
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Advances on Autonomous Navigation for Spacecraft Proximity Operations and Formation...
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Advances on Autonomous Navigation for Spacecraft Proximity Operations and Formation Flying: Algorithms Development and Verification Methods

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

Deadline for manuscript submissions: 15 December 2024 | Viewed by 5526

Special Issue Editors

Dr. Stefano Silvestrini

E-Mail Website
Guest Editor
Aerospace Science and Technology Department, Politecnico di Milano, Milano, Italy
Interests: spacecraft guidance navigation and control; deep learning; optical navigation
Prof. Dr. Michèle Lavagna

E-Mail Website
Guest Editor
Department of Aerospace Science and Technologies, Politecnico di Milano, 20156 Milan, Italy
Interests: active debris removal; space system engineering; Artificial Intelligence for autonomy; In Situ Resource Utilization; model-based system engineering

Special Issue Information

Dear Colleagues,

Spacecraft autonomous navigation has become the enabling technology for future space missions, entailing delicate operations around target bodies, being either cooperative spacecraft or uncooperative elements. Robust and adaptive autonomous systems are crucial to achieving mission objectives that require minimal interaction with the ground. The recent advent of artificial intelligence to solve specific shortcomings of classical algorithms has pushed the applicability of autonomous navigation to different scenarios and domains, from image processing to system identification. A major challenge that slows down the implementation of such algorithms in real space missions is the validation and verification process, which is objectively challenging given the hard reproducibility of the domain in which the system flies.

This Special Issue aims to provide an overview of recent advances in autonomous navigation for spacecraft proximity operations and formation flying as well as to establish the state of the art on V&V activities and facilities to increase the TRL of the algorithms. Authors are invited to submit full research articles and review manuscripts addressing (but not limited to) the following topics:

  • Cooperative relative navigation sensors and architectures
  • Uncooperative relative navigation sensors and architectures
  • Line-of-sight navigation
  • Range-only navigation
  • Relative navigation filters
  • Pose estimation techniques
  • Artificial Neural Networks for image processing
  • Artificial Neural Networks for temporal data processing
  • Algorithms for system identification of unknown bodies
  • Rendezvous and Proximity testing facilities
  • Dataset generation, image rendering tools and functional engineering infrastructures for PIL/HIL verification activities

Dr. Stefano Silvestrini
Prof. Dr. Michèle Lavagna
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

  • spacecraft
  • proximity
  • rendezvous
  • testing
  • processor-in-the-loop
  • hardware-in-the-loop
  • autonomous navigation

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Published Papers (2 papers)

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Research

15 pages, 3813 KiB  
Article
Redundant Space Manipulator Autonomous Guidance for In-Orbit Servicing via Deep Reinforcement Learning
by Matteo D’Ambrosio, Lorenzo Capra, Andrea Brandonisio, Stefano Silvestrini and Michèle Lavagna
Aerospace 2024, 11(5), 341; https://doi.org/10.3390/aerospace11050341 - 25 Apr 2024
Viewed by 1688
Abstract
The application of space robotic manipulators and heightened autonomy for In-Orbit Servicing (IOS) represents a paramount pursuit for leading space agencies, given the substantial threat posed by space debris to operational satellites and forthcoming space endeavors. This work presents a guidance algorithm based [...] Read more.
The application of space robotic manipulators and heightened autonomy for In-Orbit Servicing (IOS) represents a paramount pursuit for leading space agencies, given the substantial threat posed by space debris to operational satellites and forthcoming space endeavors. This work presents a guidance algorithm based on Deep Reinforcement Learning (DRL) to solve for space manipulator path planning during the motion-synchronization phase with the mission target. The goal is the trajectory generation and control of a spacecraft equipped with a 7-Degrees of Freedom (7-DoF) robotic manipulator, such that its end effector remains stationary with respect to the target point of capture. The Proximal Policy Optimization (PPO) DRL algorithm is used to optimize the manipulator’s guidance law, and the autonomous agent generates the desired joint rates of the robotic arm, which are then integrated and passed to a model-based feedback linearization controller. The agent is first trained to optimize its guidance policy and then tested extensively to validate the results against a simulated environment representing the motion synchronization scenario of an IOS mission. Full article
(This article belongs to the Special Issue Advances on Autonomous Navigation for Spacecraft Proximity Operations and Formation Flying: Algorithms Development and Verification Methods)
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12 pages, 9136 KiB  
Article
Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting
by Van Minh Nguyen, Emma Sandidge, Trupti Mahendrakar and Ryan T. White
Aerospace 2024, 11(3), 183; https://doi.org/10.3390/aerospace11030183 - 25 Feb 2024
Cited by 1 | Viewed by 3027
Abstract
The accelerating deployment of spacecraft in orbit has generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-cooperative, possibly unknown, resident space objects. Safety concerns with [...] Read more.
The accelerating deployment of spacecraft in orbit has generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-cooperative, possibly unknown, resident space objects. Safety concerns with manned missions and lag times with ground-based control necessitate complete autonomy. This requires robust characterization of the target’s geometry. In this article, we present an approach for mapping geometries of satellites on orbit based on 3D Gaussian splatting that can run on computing resources available on current spaceflight hardware. We demonstrate model training and 3D rendering performance on a hardware-in-the-loop satellite mock-up under several realistic lighting and motion conditions. Our model is shown to be capable of training on-board and rendering higher quality novel views of an unknown satellite nearly 2 orders of magnitude faster than previous NeRF-based algorithms. Such on-board capabilities are critical to enable downstream machine intelligence tasks necessary for autonomous guidance, navigation, and control tasks. Full article
(This article belongs to the Special Issue Advances on Autonomous Navigation for Spacecraft Proximity Operations and Formation Flying: Algorithms Development and Verification Methods)
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