Aerospace Technology and Space Informatics

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

Deadline for manuscript submissions: closed (20 December 2024) | Viewed by 1953

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Guest Editor
Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, 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
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Special Issue Information

Dear Colleagues,

Space exploration with innovative aerospace technology expands mankind’s understanding of the Earth and the universe and promotes the social and technological progress of human civilization. Our knowledge of planet Earth and outer space has given us an understanding of the requirements for planetary habitability and the resources supporting life and its sustainable development. On this basis, aerospace technology and space informatics are becoming strategic research foci for many nations. Space informatics is one of the most exciting and contemporary research topics in deep-space exploration, which requires substantial information sharing, exchange, and integration. Space informatics enables information to be shared that facilitates the development of many subsequent research areas, which ultimately boost the development and deployment of the latest related advanced technologies, such as virtual reality, artificial intelligence, robotics, deep learning, innovative machines, etc., in deep-space explorations. Considering the extremely high complexity, cost, and risk involved in spacecraft, advanced technologies in information modeling, computer simulation, optimisation, and decision support algorithms played a major role in enhancing the efficiencies, reliabilities, and safety of space missions. To contribute to future space missions, spacecraft development, and other space projects, this Special Issue proposes to collect excellent research and articles on the research and development of innovative technology for space projects based on space informatics.

Prof. Dr. Kai Leung Yung
Prof. Dr. Andrew W. H. Ip
Prof. Dr. Zhuming Bi
Guest Editors

Manuscript Submission Information

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Keywords

  • aerospace
  • space explorations
  • deep space
  • space informatics
  • innovative design
  • AI and robotics
  • computer simulations

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Published Papers (1 paper)

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Research

26 pages, 12157 KiB  
Article
A Machine Learning Approach for the Autonomous Identification of Hardness in Extraterrestrial Rocks from Digital Images
by Shuyun Liu, Haifeng Zhao, Zihao Yuan, Liping Xiao, Chengcheng Shen, Xue Wan, Xuhai Tang and Lu Zhang
Aerospace 2025, 12(1), 26; https://doi.org/10.3390/aerospace12010026 - 31 Dec 2024
Viewed by 796
Abstract
Understanding rock hardness on extraterrestrial planets offers valuable insights into planetary geological evolution. Rock hardness correlates with morphological parameters, which can be extracted from navigation images, bypassing the time and cost of rock sampling and return. This research proposes a machine-learning approach to [...] Read more.
Understanding rock hardness on extraterrestrial planets offers valuable insights into planetary geological evolution. Rock hardness correlates with morphological parameters, which can be extracted from navigation images, bypassing the time and cost of rock sampling and return. This research proposes a machine-learning approach to predict extraterrestrial rock hardness using morphological features. A custom dataset of 1496 rock images, including granite, limestone, basalt, and sandstone, was created. Ten features, such as roundness, elongation, convexity, and Lab color values, were extracted for prediction. A foundational model combining Random Forest (RF) and Support Vector Regression (SVR) was trained through cross-validation. The output of this model was used as the input for a meta-model, undergoing linear fitting to predict Mohs hardness, forming the Meta-Random Forest and Support Vector Regression (MRFSVR) model. The model achieved an R2 of 0.8219, an MSE of 0.2514, and a mean absolute error of 0.2431 during validation. Meteorite samples were used to validate the MRFSVR model’s predictions. The model is used to predict the hardness distribution of extraterrestrial rocks using images from the Tianwen-1 Mars Rover Navigation and Terrain Camera (NaTeCam) and a simulated lunar rock dataset from an open-source website. The results demonstrate the method’s potential for enhancing extraterrestrial exploration. Full article
(This article belongs to the Special Issue Aerospace Technology and Space Informatics)
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