Look Up and Skynopy partner on automated satellite collision avoidance service

2026-06-17 · Source: SpaceNews · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, quick

Summary

Space surveillance venture Look Up and ground station network provider Skynopy announced a partnership on June 17 to automate a low Earth orbit (LEO) collision avoidance service. Skynopy is contracted to integrate its network with Look Up's ATLAS² service, co-funded by Europe, enabling satellites to respond in near real time to collision threats detected by Look Up's terrestrial SORASYS radars. The initial phase, wrapping up mid-year, involves defining network interfaces, simulating contacts, and providing a test environment. ATLAS² aims to reduce reaction time and operational complexity, automating the detection-to-command chain from hours to minutes while retaining human oversight. Look Up, founded in 2022, operates its SORASYS-1 radar in Southern France, tracking objects down to 10 centimeters and raised \$58 million last year. Skynopy, founded in 2023, virtualizes access to 17 operational ground sites for around 10 satellite operators. The EU contributes 70% of the 3.4 million euro (\$4 million) cost for ATLAS², which envisions seven radars for sovereign LEO-tracking amidst over 15,000 active satellites.

Key takeaway

For AI Architects designing space traffic management systems, this partnership highlights a critical shift towards automated collision avoidance. You should prioritize integrating ground station networks with terrestrial radar systems to enable rapid, near real-time satellite maneuver execution. Ensure your designs incorporate human-in-the-loop decision points, especially for high-value assets, to balance automation efficiency with necessary oversight. This approach can significantly reduce reaction times from hours to minutes.

Key insights

Look Up and Skynopy partner to automate LEO collision avoidance, reducing reaction times while maintaining human oversight.

Principles

• Automation reduces operational complexity.
• Human oversight remains crucial for high-value assets.
• Phased deployment improves coverage and accuracy.

Method

Integrate ground stations with terrestrial radars to enable near real-time satellite response, simulating contacts and modeling radio behavior for automated command execution.

In practice

• Implement automated detection-to-command chains.
• Design systems for human-in-the-loop decision authority.
• Utilize distributed radar networks for LEO tracking.

Topics

• Space Situational Awareness
• LEO Collision Avoidance
• Ground Station Networks
• SORASYS Radars
• ATLAS² System
• Automated Space Safety

Best for: AI Engineer, AI Architect, Entrepreneur

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Editorial summary, takeaway, and curation by AIssential. Original article published by SpaceNews.