Constraint-Informed Learning for Warm-Starting Trajectory Optimization

What is it about?

Future space missions need advanced autonomous systems to explore challenging environments, with motion planning and trajectory optimization being key components. Current optimization methods are too slow for the limited computing resources on flight-grade computers. Trajectory Optimization with Merit Function Warm Starts uses machine learning to speed up this process by training a neural network to provide initial guesses to the optimizer while incorporating problem-specific constraints directly into the learning process. Tests on Lunar and Mars scenarios show that Trajectory Optimization with Merit Function Warm Starts significantly improves computation speed and solution quality compared to traditional learning-based methods.

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