AI-Empowered Space Traffic Management: Challenges and Strategies

Year : 2025 | Volume : 14 | Issue : 01 | Page : 20 28
    By

    Jasmine,

  • Bhuvi Jain,

  • Manasvi Jain,

  • Sanjeev Patwa,

  1. Student, Department of Computer Science Engineering, Mody University of Science and Technology, Lakshmangarh, Narodara Rural, Rajasthan, India
  2. Student, Department of Computer Science Engineering, Mody University of Science and Technology, Lakshmangarh, Narodara Rural, Rajasthan, India
  3. Student, Department of Computer Science Engineering, Mody University of Science and Technology, Lakshmangarh, Narodara Rural, Rajasthan, India
  4. Associate Professor, Department of Computer Science Engineering, Mody University of Science and Technology, Lakshmangarh, Narodara Rural, Rajasthan, India

Abstract

Space traffic management (STM) has emerged as a critical field of study due to the rapid expansion of space activities, including satellites, debris, and future crewed missions. This research paper delves into the multifaceted issues and challenges associated with STM and explores innovative strategies, specifically focusing on integrating artificial intelligence (AI). It examines the pressing problems of space debris proliferation, collision avoidance, spectrum congestion, and the need for international cooperation. The paper identifies STM’s primary issues, such as the exponential increase in the number of satellites and space debris, limited resources, and the absence of comprehensive regulatory frameworks. It also discusses the critical challenges posed by the rise of mega-constellations, autonomous spacecraft, and the potential for hostile actions in space. To address these issues and challenges, the research paper discusses a set of strategies that can improve STM, including the development of standardized STM protocols, enhanced tracking and monitoring capabilities, improved data sharing and communication, and the promotion of international cooperation. Moreover, the paper emphasizes the integration of AI as a pivotal strategy for improving STM, highlighting its potential to automate collision avoidance, predict and mitigate space debris collisions, and optimize satellite orbits. AI-based solutions, such as machine learning algorithms and autonomous decision-making systems, play a crucial role in enhancing situational awareness, enabling real-time data analysis, and improving space traffic prediction. The integration of AI not only provides a more efficient STM infrastructure but also aids in mitigating the growing challenges in space.

Keywords: Artificial intelligence, space debris proliferation, machine learning algorithms, spacecraft, space traffic management

[This article belongs to Research & Reviews : Journal of Space Science & Technology ]

How to cite this article:
Jasmine, Bhuvi Jain, Manasvi Jain, Sanjeev Patwa. AI-Empowered Space Traffic Management: Challenges and Strategies. Research & Reviews : Journal of Space Science & Technology. 2025; 14(01):20-28.
How to cite this URL:
Jasmine, Bhuvi Jain, Manasvi Jain, Sanjeev Patwa. AI-Empowered Space Traffic Management: Challenges and Strategies. Research & Reviews : Journal of Space Science & Technology. 2025; 14(01):20-28. Available from: https://journals.stmjournals.com/rrjosst/article=2025/view=201984


References

  1. Manfletti C, Guimarães M, Soares AI for space traffic management. J Space Safety Eng. 2023; 10 (4): 495–504. doi: 10.1016/j.jsse.2023.08.007.
  2. Larsen PB. Space traffic management standards. J Air Law Commerce. 2018; 83 (2): 359–387.
  3. Russo A, Lax G. Using artificial intelligence for space challenges: a survey. Appl Sc. 2022; 12 (10): doi: 10.3390/app12105106.
  4. Cukurtepe H, Akgun Towards space traffic management system. Acta Astronaut. 2009; 65 (5–6): 870–878.
  5. Ailor Space traffic management: implementations and implications. Acta Astronaut. 2006; 58 (5): 279–286. doi: 10.1016/j.actaastro.2005.12.002.
  6. McClintock B, Ligor DC, McCormick D, Herron M, Jukneviciute K, Van Bibber T, Feistel K, Rao A, Rao A, Grosso T, Fenner M, Lee H, Ar Rafee A, Urbina The Time for International Space Traffic Management Is Now. RB-A1949-1. Santa Monica, CA, USA: RAND Corporation; 2023.Available at https://www.rand.org/pubs/research_briefs/RBA1949-1.html
  7. Peterson GE, Jenkin AB, Sorge ME, McVey Implications of proposed satellite constellations on space traffic management and long-term debris growth in near-earth environment. IAC-16, A6,7,8, x32389. In: 67th International Astronautical Conference, Guadalajara, Mexico, September 26-30, 2016.
  8. Sanchez L, Vasile M. Intelligent decision support for collision avoidance manoeuvre planning under uncertainty. Adv Space Res. 2023; 72 (7): 2627–2648. doi: 1016/j.asr.2022.09.023.
  9. Siddique I. Small satellites: revolutionizing space exploration and earth observation. Eur J Adv Eng Technol. 2024; 11 (3): 118–1
  10. Bakambekova A, Kouzayha N, Al-Naffouri T. On the interplay of artificial intelligence and space-air-ground integrated networks: a survey. arXiv preprint arXiv:2402.00881. January 20, 2024.
Regular Issue Subscription Review Article
Volume 14
Issue 01
Received 31/01/2025
Accepted 10/02/2025
Published 25/02/2025
Publication Time 25 Days
338

Login


My IP

PlumX Metrics