Selected Papers from IAAI 2020

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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 7856

Special Issue Editors


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Guest Editor
Head of the Optimization and Machine Learning Group ENAC Research Lab, Ecole Nationale de l’Aviation Civile (ENAC), Université Fédérale de Toulouse, 7 Avenue Edouard Belin, FR-31055 Toulouse CEDEX, France
Interests: stochastic optimization (simulated annealing, artificial evolution, Tabu search); radar tracking filtering; signal processing; probabilities; aerospace sectorization; traffic assignment
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Guest Editor
Dean of International Academy of Aviation Industry (IAAI), King Mongkut's Institute of Technology Ladkrabang (KMITL), No.1 Soi Chalong Krung 1, Ladkrabang, Bangkok 10520, Thailand
Interests: new materials; plasma technology; real-time tracking system; engineering materials arrested system; gas turbine engine; electric vehicles; unmanned aerial vehicles

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Guest Editor
Assistant Dean of International Academy of Aviation Industry (IAAI), King Mongkut's Institute of Technology Ladkrabang (KMITL), No.1 Soi Chalong Krung 1, Ladkrabang, Bangkok 10520, Thailand
Interests: aerospace intelligent information; tracking system; aerospace image processing; remote sensing; space debris mitigation; active debris removal; orbit and attitude control; satellite constellation

Special Issue Information

Dear Colleagues,

This Special Issue is cooperating with the first Innovation Aviation & Aerospace Industry—International Conference 2020 (https://iaai.asia/), which was organized by the International Academy of Aviation Industry (IAAI) and King Mongkut's Institute of Technology Ladkrabang (KMITL). It was held as a five-day event in Chumphon Province, Thailand, 13‒17 January, 2020.

The IAAI International Conference aims to become the leading annual conference in fields related to the aviation and aerospace industries. This conference focuses on innovations in aviation and aerospace by fostering knowledge and new ideas for research work within the community, speakers, plenary speeches, young researchers, and practitioners. The conference also seeks to provide forums, oral presentations, technical workshops, and scientific sessions. We invite researchers, aviation industry representatives, and others with an interest in the impacts of the aviation industry to join us for what is an exciting event.

The conference hosts several internationally renowned speakers and invites submissions for oral presentations. All presenters at the conference are invited to submit extended versions of their conference work to this Special Issue for publication.

Keywords:

Aviation Technology

  • Air navigation
  • Satellite and telecommunication systems
  • Air transportation
  • Inter-modal transport
  • Reliability and maintenance
  • Airport design and construction
  • Airport operation
  • Aircraft operation
  • Fleet planning
  • Meteorology
  • Surveillance
  • Aero sports
  • Big data analysis
  • Cabin technology

Aerospace Engineering

  • Astrodynamics
  • Space debris mitigation
  • Active debris removal
  • Orbit and attitude control
  • Mission analysis
  • Satellite constellation
  • Collision avoidance maneuver
  • Remote sensing
  • Aerodynamics
  • Flight dynamics and control
  • Computational fluid dynamics
  • Lightweight structure
  • New materials
  • Aircraft and spacecraft design
  • Avionics
  • Structural dynamics
  • Fracture mechanics
  • Advanced propulsion
  • Multi-disciplinary design optimization
  • Unmanned aerial vehicles
  • Aircraft smart systems

Dr. Soemsak Yooyen
Dr. Patcharin Kamsing
Prof. Dr. Daniel Delahaye
Guest Editors

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Published Papers (1 paper)

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Research

22 pages, 4723 KiB  
Article
Vision-Based Spacecraft Pose Estimation via a Deep Convolutional Neural Network for Noncooperative Docking Operations
by Thaweerath Phisannupawong, Patcharin Kamsing, Peerapong Torteeka, Sittiporn Channumsin, Utane Sawangwit, Warunyu Hematulin, Tanatthep Jarawan, Thanaporn Somjit, Soemsak Yooyen, Daniel Delahaye and Pisit Boonsrimuang
Aerospace 2020, 7(9), 126; https://doi.org/10.3390/aerospace7090126 - 30 Aug 2020
Cited by 49 | Viewed by 6958
Abstract
The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit [...] Read more.
The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future. Full article
(This article belongs to the Special Issue Selected Papers from IAAI 2020)
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