European Advanced Reusable Satellite (EARS) Project

- Founded: 2019
- Country:
- Industry:
Description: The EARS project aims to design a reusable small satellite and develop related critical technologies. The reusable spacecraft is intended to de-orbit in a controlled maneuver to deliver its products and results back to Earth after several months in orbit, thereby enabling innovative research that would benefit from the space environment, especially microgravity conditions.
Vision:
To establish a family of small satellite platforms that can be launched and reused frequently at a competitive price, minimizing environmental impact and resource waste both in space and on Earth.
Mission:
To introduce the disruptive concept of reusability in the small satellite segment for a greener and sustainable Europe.
Contact:
- Website: https://www.earsproject.eu/
- Email: v.raimondi@ifac.cnr.it
- Social Media:
Key Milestones:
- 2023 : Project initiation.
- 2024 : First review meeting held to present the project's progress to the European Commission and external experts.
- 2024 : EARS partners presented five oral presentations during the 75th International Astronautical Congress (IAC) in Milan, Italy.
Products & Services:
- Reusable Small Satellite Platform: A modular architecture designed to guarantee maximum flexibility and the possibility of continuous upgrades over time. The satellite uses an inflatable/deployable heat shield and a precise landing parafoil to achieve a high payload fraction for a reusable system.
- EARS Planetary Rover Platform: Modular, adaptable rover designed for exploration of lunar, Martian, and asteroid surfaces. Robotic surface exploration, resource scouting, infrastructure deployment support Customizable mobility systems, advanced terrain-adaptive capabilities TRL 4–5 (functional prototypes under testing)
- AI-Based Navigation Systems: Autonomous navigation algorithms optimized for unpredictable extraterrestrial terrains. Autonomous planetary exploration, hazard avoidance, real-time route optimization Self-learning algorithms tailored for dynamic, low-data environments.TRL 4 (laboratory and simulated environment validation)