Automated guidance algorithms for a space station-based crew escape vehicle

Area of Science:

• Spacecraft engineering
• Orbital mechanics
• Astrodynamics

Background:

• Space station crew evacuation requires robust emergency escape systems.
• Uncontrolled station rotation poses significant collision risks during evacuation.
• Existing escape sequences need refinement for dynamic, tumbling environments.

Purpose of the Study:

• To develop and validate an emergency escape sequence for space stations.
• To address collision avoidance challenges during uncontrolled station rotation.
• To ensure safe crew evacuation through effective separation and avoidance maneuvers.

Main Methods:

• Development of algorithms for two critical separation events (SEP1 and SEP2).
• Implementation of collision avoidance strategies for uncontrolled station dynamics (up to 2 deg/sec).
• Utilizing neural network theory for model-free function approximation in SEP2 burn calculations.

Main Results:

• Successful simulation of a complete emergency escape sequence.
• Demonstrated effective collision avoidance during uncontrolled station rotation.
• Neural network approach provided a robust solution for calculating SEP2 avoidance burns.

Conclusions:

• The proposed escape vehicle and sequence provide a viable solution for emergency crew evacuation.
• Collision avoidance in tumbling scenarios is achievable with advanced guidance algorithms.
• Neural network-based methods are effective for complex orbital maneuver calculations.