A Real-Time Autonomous Path Planning Framework for Space Satellites Using Improved Interfered Fluid Dynamic System (IFDS)

Abstract

In the vast expanse of space, a critical challenge threatens the sustainability of satellite operations and future exploration: space debris. The accumulation of inactive satellites and small debris has elevated the risk of cascading collisions, known as the Kessler Syndrome, which could render critical orbital paths unusable. This scenario would significantly impact our ability to deploy and maintain satellites essential for global communication, weather monitoring, navigation, and scientific research. Addressing the urgent need for advanced space traffic management solutions, this research proposes an autonomous satellite navigation system designed to optimize collision avoidance maneuvers and minimize fuel consumption, contributing to more sustainable space operations. Our system integrates the Interfered Fluid Dynamic System (IFDS) with Machine Learning (ML) models, leveraging real-time predictive capabilities to enhance satellite safety and reduce human intervention. Using the Nutcracker Optimization Algorithm (NOA), optimal parameters are generated to train the predictive model, enabling efficient dataset generation. XGBoost, trained on this dataset, is then employed within the IFDS framework to predict optimal collision-avoidance parameters in real time. This two-step approach enables satellites to autonomously adjust trajectories, maintaining safe distances from debris with minimal fuel consumption. XGBoost achieved an 92% success rate in predicting the optimal reaction parameter of the IFDS Algorithm such that the collision is avoided with a minimum of 2000 m, proving its effectiveness in dynamic orbital environments. Our work also compares NOA with Particle Swarm Optimization (PSO) for tuning IFDS parameters. Our results show NOA’s superior convergence rate and computational efficiency, reducing processing time by approximately 47% compared to PSO. This efficiency accelerates dataset generation and model training. Simulations were conducted using the orekit library to assess the system’s operational effectiveness. The IFDS algorithm, guided by XGBoost-predicted parameters, effectively executes preemptive collision avoidance maneuvers, achieving minimum fuel consumption while ensuring safe separation from debris up to one hour in advance of a potential collision. In conclusion, this research introduces a framework for autonomous satellite collision avoidance that enhances the safety and efficiency of space operations. By reducing reliance on ground intervention, conserving fuel, and enabling safe, independent navigation, this system supports more effective and scalable space traffic management, paving the way for future advancements in satellite operations.