AI Empowered Space Traffic Management: Challenges and Strategies


Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 14 | Issue : 01 | Page : –
    By

    Jasmine,

  • Bhuvi Jain,

  • Manasvi Jain,

  • Dr. 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

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_175693’);});Edit Abstract & Keyword

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 (rrjosst)]

How to cite this article:
Jasmine, Bhuvi Jain, Manasvi Jain, Dr. Sanjeev Patwa. AI Empowered Space Traffic Management: Challenges and Strategies. Research & Reviews : Journal of Space Science & Technology. 2025; 14(01):-.
How to cite this URL:
Jasmine, Bhuvi Jain, Manasvi Jain, Dr. Sanjeev Patwa. AI Empowered Space Traffic Management: Challenges and Strategies. Research & Reviews : Journal of Space Science & Technology. 2025; 14(01):-. Available from: https://journals.stmjournals.com/rrjosst/article=2025/view=0


document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_175693’);});Edit

References

  1. Chiara Manfletti, Marta Guimarães, Claudia Soares, AI for space traffic management, Journal of Space Safety Engineering,2023, https://doi.org/10.1016/j.jsse.2023.08.007 (https://www.sciencedirect.com/science/article/pii/S24688967230008 97)
  2. Russo, Antonia, and Gianluca Lax. 2022. “Using Artificial Intelligence for Space Challenges: A Survey” Applied Sciences 12, no. 10: 5106. https://doi.org/10.3390/app12105106
  3. Haydar Cukurtepe, Ilker Akgun, Towards space traffic management system, Acta Astronautica Volume 65, Issues 5–6,2009,
  4. William H. Ailor, Space traffic management: Implementations and implications, Acta Astronautica, Volume 58, Issue 5,2006, Pages 279-286, https://doi.org/10.1016/j.actaastro.2005.12.002. (https://www.sciencedirect.com/science/article/pii/S0094576506000087
  5. Paul B. Larsen, Space Traffic Management Standards, 83 J. AIR L. & COM. 359 (2018) https://scholar.smu.edu/jalc/vol83/iss2/5
  6. McClintock, Bruce, Douglas C. Ligor, Dan McCormick, Marissa Herron, Kotryna Jukneviciute, Thomas Van Bibber, Katie Feistel, Akhil Rao, Adi Rao, Taylor Grosso, Michael Fenner, Hanjun Lee, Abdullah Ar Rafee, and Tomás Urbina, The Time for International Space Traffic Management Is Now, RAND Corporation, RB-A1949-1, 2023. As of November 29, 2023: https://www.rand.org/pubs/research_briefs/RBA1949-1.html
  7. Glenn E. Peterson, A. B. Jenkin, M. E. Sorge. P. 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, 67th International Astronautical Conference, Guadalajara, Mexico, September 26-30, 2016.
  8. 8 Sanchez, Luis & Vasile, Massimiliano. (2022). Intelligent Decision Support for Collision Avoidance Manoeuvre Planning Under Uncertainty. Advances in Space Research. 10.1016/j.asr.2022.09.023.
  9. Siddique I. Small satellites: revolutionizing space exploration and earth observation. European Journal of Advances in Engineering and Technology. 2024 Mar 31;11(3):118-24.
  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. 2024 Jan 20.
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
233

async function fetchCitationCount(doi) {
let apiUrl = `https://api.crossref.org/works/${doi}`;
try {
let response = await fetch(apiUrl);
let data = await response.json();
let citationCount = data.message[“is-referenced-by-count”];
document.getElementById(“citation-count”).innerText = `Citations: ${citationCount}`;
} catch (error) {
console.error(“Error fetching citation count:”, error);
document.getElementById(“citation-count”).innerText = “Citations: Data unavailable”;
}
}
fetchCitationCount(“10.37591/RRJoSST.v14i01.0”);

Loading citations…