Lodestar Space Ltd

Lodestar Space Ltd
Description: Lodestar Space is a London-based startup developing adaptive robotics and in-orbit interaction technologies for space debris mitigation, asset protection, and on-orbit servicing. Their platform combines machine learning with autonomous vision and manipulator systems to dynamically engage with unknown and non-cooperative targets in space.
Vision:
To be the frontline service provider for off-Earth operations, pioneering the next generation of tools for the new space age.
Mission:
To develop advanced robotic technologies that ensure the protection, inspection, and repair of high-value space assets, thereby safeguarding national security and enabling large-scale in-orbit construction.
Contact:
References:
Investors:
Funding:
  • Pre-Seed Funding: Jul 25, 2024, €2.3 million
  • Grant : Jul 25, 2024, £0.8 million
Total Funding: $: US Dollar3.5 million
Key Milestones:
  • 2022: UK, entered accelerator programs (CDL, Seraphim)
  • 2023: Secured early-stage venture capital funding amounting to $2.49 million to advance the development of its robotic systems.
  • 2024 : Entered into a collaboration agreement with Momentus to define requirements for integrating and performing a robotic arm mission on a Vigoride Orbital Service Vehicle.Published neural network model validation data; Momentus partnership initiated
  • 2025 : Goal: First hosted tech demo on third-party satellite or tug
Key People:
Sector:
Products & Services:
  • Dexterous Robotic Arm: A platform-agnostic robotic arm designed to perform autonomous, highly dexterous tasks in microgravity environments. This system is capable of inspecting and repairing orbiting assets, withstanding radiation and extreme temperatures, and maintaining precision in space operations.
  • Lodestar AI Framework: Proprietary neural network system for planning grasping and contact with novel targets using synthetic data and on-orbit training.
  • Partnership Programs: Collaborative testing with in-orbit service providers (e.g., Momentus) for scenario validation. TRL: 4–5 (benchmarked in lab; in-space testing pending)
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