Ice2Thrust

Solar-Electric Water Propulsion System

European EIC Pathfinder Project Research expert - RL-based Autonomous Navigation

Project Overview

ICE2THRUST concept

ICE2THRUST represents a groundbreaking approach to space propulsion, developing the world's first In-Situ Resource Utilisation (ISRU) end-to-end process chain that transforms ice into thrust. This innovative system utilizes water as a propellant, decomposed by an electrolyser into hydrogen and oxygen for subsequent use in a thruster.

As Lead Research Scientist for RL-based autonomous navigation systems, I'm responsible for developing the intelligent control algorithms that enable autonomous proximity operations, docking, and propellant refilling procedures.

Technical Approach

🚀 Solar-Electric Water Electrolysis Propulsion

Development of high-efficiency propulsion systems using water as propellant, achieving the highest specific impulse of all storable chemical propulsion systems.

🤖 Autonomous Proximity Operations

RL-based algorithms for autonomous docking, proximity operations, and propellant refilling procedures with machine learning-enhanced decision making.

⛏️ In-Space Resource Utilization

Advanced systems for water extraction from icy regolith and integration with propulsion systems for self-sustainable space operations.

My Research Contributions

Reinforcement Learning for Autonomous Navigation

  • Simulation Environment Development: Created a modular flight dynamics simulator for 3DoF and 6DoF spacecraft dynamics, enabling the testing of RL algorithms in realistic space scenarios
  • Autonomous Docking Algorithms: Developed RL-based policies for precise spacecraft docking operations under uncertain conditions
  • Lab Testing and Validation: Conducted extensive testing and validation of algorithms in Zero-G laboratory using a floating platform

Machine Learning Integration

  • Integration of deep RL control policies into hardware-in-the-loop (HIL) setups for realistic testing
  • Development of sim-to-real transfer techniques for RL algorithms in realistic laboratory settings
  • Implementation of scaling strategies to adapt laboratory-tested algorithms to real-world space conditions

Project Resources

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