AI for Autonomous Spacecraft Navigation

Enable spacecraft to perceive, plan, and control trajectories autonomously

Autonomous navigation AI fuses sensors (vision, lidar, radar, star trackers), state estimation, guidance, and control to execute maneuvers with minimal ground contact.

Certification, explainability, and fail-safes are critical to ensure robust operation and adherence to mission objectives.

IMU, star trackers, cameras, lidar/radar, GNSS (where applicable), eph

State estimation (EKF/UKF/particle), SLAM/VO, guidance and control policies, hazard detection/avoidance, and fault detection are key components of the autonomous navigation system.

An onboard autonomy stack, coupled with ground validation tools and simulation environments, allows for comprehensive testing and verification of the spacecraft's navigational capabilities.

live demo · related simulation● LIVE

Relative navigation for clusters/swarms

Onboard navigation systems, operating with limited ground support, are crucial for managing complex formations of spacecraft.

Autonomous evasive maneuvers within predefined guardrails enable coordinated operation and resilience against potential threats.

Frequently asked questions

What is the process of integrating sensors, dynamics, and maps to achieve autonomous navigation?

The integration involves synchronizing sensor data with vehicle dynamics models and incorporating high-fidelity maps for accurate localization and path planning. Careful management of data pipelines ensures traceability throughout the training and validation processes.

How are governance, safety, and explainability addressed in autonomous spacecraft navigation systems?

Governance frameworks establish clear roles and responsibilities for oversight and compliance. Safety protocols incorporate redundancy and fail-safe mechanisms, while explainability techniques provide insights into the AI's decision-making processes.

What role does telemetry replay, decision traces, and rationale play in verifying autonomous navigation?

Telemetry replay allows for detailed analysis of spacecraft behavior during maneuvers. Decision traces capture the sequence of actions taken by the AI, while rationales provide explanations for those decisions, enhancing trust and facilitating troubleshooting.

What considerations are involved in obtaining certifications, managing export controls, and adhering to safety standards?

Certification paths demonstrate compliance with relevant regulations and industry standards. Export controls govern the transfer of sensitive technologies, while rigorous adherence to safety standards ensures operational integrity and minimizes risks.

▶ Try it live

Everything above runs in your browser — open Inverse Kinematics (FABRIK) and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Inverse Kinematics (FABRIK) simulation