Autonomous Space Debris Detection and Risk Assessment System

Author(s)	Ms. Shreeya Pandey, Ms. Manasvi Jain, Mr. Saiyam ., Dr. Yatu Rani
Country	India
Abstract	The increased frequency of satellite launches combined with the increased rate of accumulation of space debris outstrips the capabilities of the monitoring systems currently in place. In this work, we propose a multi-agent AI model based on YOLOv8, Kalman Filtering, SGP4 orbit propagation algorithm, and a combination of the random forest and XGBoost models that enables detection of space debris and recommends collision avoidance maneuvers in real-time. Our system operates with sub-second latency autonomously with an accuracy and F1 score of 97.23%, and 97.19% respectively.
Keywords	Space Debris, Collision Avoidance, Multi-Agent System, YOLOv8, Random Forest, XGBoost, Orbital Mechanics, Risk Assessment, Kalman filtering
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-13

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International Journal For Multidisciplinary Research