Advanced Algorithmic Methods for Spacecraft and Space Systems

Space systems engineering increasingly relies on advanced algorithms to enhance the autonomy, safety, efficiency, and resilience of spacecraft, satellites, and missions. Progress in guidance, navigation, and control; trajectory planning; mission optimization; signal processing; fault diagnosis; and space-data analysis is being driven by advances in high-fidelity simulation, increasingly capable onboard and ground computing, machine learning, and distributed architectures. Space missions operate under stringent constraints, including limited computational and communication resources, uncertain and nonlinear operating environments, incomplete or noisy data, and demanding requirements for real-time execution, explainability, verification, and safety. Recent studies have demonstrated promising advances in data-driven control, spacecraft guidance, trajectory optimization, digital twins, satellite fault diagnosis, and autonomous decision-making. Nevertheless, substantial gaps remain in translating these results from simulations and laboratory environments to operational missions. Further research is needed to develop generalizable algorithmic methods that combine theoretical guarantees, computational efficiency, scalability, robustness, and dependable performance in complex space environments.This Research Topic aims to advance the development, analysis, and application of algorithmic methods for spacecraft and space systems. It will encourage contributions that establish new theoretical foundations, enhance computational efficiency, or demonstrate practical solutions for the design, operation, monitoring, and optimization of space systems. Particular emphasis will be placed on methods that address uncertainty, nonlinear dynamics, limited onboard resources, safety requirements, autonomy, and the integration of heterogeneous mission data. The Research Topic also seeks to promote connections between algorithm development and space system implementation by examining validation, benchmarking, interpretability, robustness, and the transition from simulated environments to real-world deployment. While recent work has focused specifically on AI-driven autonomy and its deployment assurance, this Research Topic adopts a broader algorithmic perspective, spanning classical optimization and control theory alongside AI/ML, with particular emphasis on guidance, trajectory optimization, and digital twins for space missions.This Research Topic welcomes computational, theoretical, and applied contributions to spacecraft and space systems engineering, while excluding work with no clear relevance to space engineering applications. We welcome articles addressing, but not limited to, the following themes:• Guidance, navigation, and control algorithms for spacecraft and satellites.• Autonomous mission planning, decision-making, and multi-agent coordination for space systems.• Trajectory planning and mission optimization.• Artificial intelligence and machine learning for spacecraft and satellite systems.• Real-time, robust, and fault-tolerant algorithms for onboard systems.• Space situational awareness, space traffic management, and orbital mechanics.• Digital twins, simulation, and hardware-in-the-loop validation for space systems.• Computational and data-driven methods for space materials selection and performance prediction. Topic editors: Luis Santos, Francisco Pérez Moreno, Zhenbo Wang Source: https://www.frontiersin.org/research-topics/82378/advanced-algorithmic-methods-for-spacecraft-and-space-systems