AI Insight
NASA has announced the 2025 Early Career Faculty Awards supporting ten research projects across two main areas: advanced diagnostics for testing spacecraft atmospheric entry systems and autonomous spacecraft planning using machine learning. The diagnostic projects focus on developing ultrafast laser and spectroscopy techniques to better characterize high-temperature flows in ground test facilities that simulate atmospheric entry conditions. The autonomous spacecraft projects aim to create machine learning methods for real-time onboard guidance, navigation, and control with safety guarantees.
Why it matters
These awards advance critical capabilities for future space missions by improving ground testing accuracy for spacecraft heat shield designs and enabling spacecraft to make safe autonomous decisions during flight. The combination of better testing diagnostics and autonomous control systems could enhance mission safety while reducing operational costs and enabling more complex missions to planetary atmospheres.
Understand the Science
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Preparations for Next Moonwalk Simulations Underway (and Underwater)
Advanced Diagnostics for High-Enthalpy Test Facilities Simulating Spacecraft Atmospheric Entry
- Damiano Baccarella
University of Tennessee, Knoxville
Application of Resonance Enhanced Multi-Photon Ionization Diagnostics to the Characterization of Arcjet Flows - Ciprian Dumitrache
Colorado State University
Ultrafast Laser Diagnostics for Nonequilibrium Flowfields Characterization in Atmospheric Entry Studies - Dan Fries
University of Kentucky, Lexington
Multiplexed Polarization Spectroscopy for Single-Shot Multi-Species Diagnostics in High-Enthalpy Flows - Yi Mazumdar
Georgia Institute of Technology
Simultaneous Temperature, Species, and Velocity Measurements using Ultrafast Laser Diagnostics for Ground Testing of Spacecraft Atmospheric Entry Systems
Planning for Autonomous Spacecraft Using Machine Learning Methods to Enable Onboard Guidance, Navigation, and Control
- Glen Chou
Georgia Institute of Technology
Robust Real-Time Hierarchical Neural Planning and Control with System-Level Guarantees - Roshan Eapen
Pennsylvania State University
Hamilton-Jacobi aided Planning and Reasoning for Intelligent Spacecraft Maneuvers (HJ-PRISM) - Bin Hu
University of Houston
Safety-Enabled and Efficient Onboard Planning for Autonomous Spacecraft via Physics-Informed Reinforcement Learning