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Aerospace
Special Issues
Artificial Intelligence and Space Robotics: Perception, Autonomy, and Intelligence
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Artificial Intelligence and Space Robotics: Perception, Autonomy, and Intelligence

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5307

Special Issue Editor

Prof. Dr. George Z. H. Zhu

E-Mail Website
Guest Editor
Department of Mechanical Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
Interests: dynamics and control of tethered spacecraft system and space robotics; electrodynamic tether propulsion and space debris removal; multi- functional materials; additive manufacturing in space; solid mechanics and finite element method
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Autonomous space robotics is a key enabling technology to assist and even replace human astronauts in performing manipulation, assembly, and service functions in future space exploration. The past several decades have witnessed a great deal of research in dynamic modeling, fabrication, control design, human–robot interaction and robot trustworthiness. The fast-growing artificial intelligence (AI) has enabled autonomous robots to sense/analyze/interfere with the environment, make decisions and execute tasks independently, bringing them beyond their previous use as simply tools to perform repetitive and dangerous work for humans. This Special Issue welcomes the submission of high-quality original contributions to AI-based modeling approaches for space robotics, AI in control strategies, machine learning algorithms in robotics, AI for human–robot interaction, the trustworthiness of autonomous robotics, dynamic analysis and/or experimental results in the context of space robotics methodologies, business perspectives and initiatives.

Prof. Dr. George Z. H. Zhu
Guest Editor

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

  • autonomous robotics
  • intelligent control
  • artificial intelligence
  • machine learning
  • human–robot interaction
  • trustworthiness
  • environment interaction
  • decision making
  • space exploration
  • on-orbit service

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Published Papers (2 papers)

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Research

15 pages, 1219 KiB  
Article
Deep-Learning-Based Satellite Relative Pose Estimation Using Monocular Optical Images and 3D Structural Information
by Sijia Qiao, Haopeng Zhang, Gang Meng, Meng An, Fengying Xie and Zhiguo Jiang
Aerospace 2022, 9(12), 768; https://doi.org/10.3390/aerospace9120768 - 28 Nov 2022
Cited by 4 | Viewed by 2297
Abstract
Relative pose estimation of a satellite is an essential task for aerospace missions, such as on-orbit servicing and close proximity formation flying. However, the changeable situation makes precise relative pose estimation difficult. This paper introduces a deep-learning-based satellite relative pose estimation method for [...] Read more.
Relative pose estimation of a satellite is an essential task for aerospace missions, such as on-orbit servicing and close proximity formation flying. However, the changeable situation makes precise relative pose estimation difficult. This paper introduces a deep-learning-based satellite relative pose estimation method for monocular optical images. The method is geared towards uncooperative target satellites with known 3D models. This paper proposes a novel convolutional neural network combined with 3D prior knowledge expressed by the 3D model in the form of the point cloud. The method utilizes point cloud convolution to extract features from the point cloud. To make the result more precise, a loss function that is more suitable for satellite pose estimation tasks is designed. For training and testing the proposed method, large amounts of data are required. This paper constructs a satellite pose estimation dataset BUAA-SID-POSE 1.0 by simulation. The proposed method is applied to the dataset and shows desirable performance on the pose estimation task. The proposed technique can be used to accomplish monocular vision-based relative pose estimation tasks in space-borne applications. Full article
(This article belongs to the Special Issue Artificial Intelligence and Space Robotics: Perception, Autonomy, and Intelligence)
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18 pages, 7436 KiB  
Article
An Observer-Based Stiffness Estimation for Space Target Capture by Space Robot with Controllable Damping Mechanism
by Rui Chang, Qingxuan Jia, Ming Chu and Xiaodong Zhang
Aerospace 2022, 9(11), 726; https://doi.org/10.3390/aerospace9110726 - 18 Nov 2022
Viewed by 2021
Abstract
The space target capturing task using the spacecraft-manipulator system (SMS) has special significance in on-orbit servicing due to its theoretical challenges and practical value. The contact force between the end effector (gripper) and the target exerted by the tumbling motion of the space [...] Read more.
The space target capturing task using the spacecraft-manipulator system (SMS) has special significance in on-orbit servicing due to its theoretical challenges and practical value. The contact force between the end effector (gripper) and the target exerted by the tumbling motion of the space target destabilizes the spacecraft base. A full-dimensional controllable damping mechanism (FDCDM) with a cross-axis structure was designed to buffer the transient impact force on the end joint. The introduction of a damping mechanism gives the space robot a variable stiffness and damping system, and a stiffness estimation algorithm is proposed to calibrate the system stiffness, as stiffness cannot be measured directly. The full-dimensional controllable damping mechanism (FDCDM) with a cross-axis structure is equivalent to a four-DOF tandem joint, and the whole-body dynamic model of the SMS endowed with a full-dimensional controllable damping mechanism (FDCDM) was established using the Kane equation. Then, an unknown input observer (UIO)-based identification theory is proposed to precisely estimate the internal flexibility torque and the corresponding joint stiffness. A model-based neural learning algorithm is proposed to update the variable parameter matrix of the observer. The simulation experiment results demonstrate that the flexibility torque and joint stiffness could be accurately estimated within the expected error, illustrating the feasibility and effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Artificial Intelligence and Space Robotics: Perception, Autonomy, and Intelligence)
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