STM Journals Peer-reviewed research from Consortium e-Learning Network

Research & Reviews : Journal of Space Science & Technology Review Article

AI-Empowered Space Traffic Management: Challenges and Strategies

  1. Jasmine Department of Computer Science Engineering, Mody University of Science and Technology, Lakshmangarh, Narodara Rural
  2. Bhuvi Jain Department of Computer Science Engineering, Mody University of Science and Technology, Lakshmangarh, Narodara Rural
  3. Manasvi Jain Department of Computer Science Engineering, Mody University of Science and Technology, Lakshmangarh, Narodara Rural
  4. Sanjeev Patwa Department of Computer Science Engineering, Mody University of Science and Technology, Lakshmangarh, Narodara Rural

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

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