Amortized Global Search for Efficient Preliminary Trajectory Design with Deep Generative Models

Understand

Preliminary trajectory design is a global search problem that seeks multiple qualitatively different solutions to a trajectory optimization problem.

• Due to its high dimensionality and non-convexity, and the frequent adjustment of problem parameters, the global search becomes computationally demanding.
• In this paper, we exploit the clustering structure in the solutions and propose an amortized global search (AmorGS) framework.
• We use deep generative models to predict trajectory solutions that share similar structures with previously solved problems, which accelerates the global search for unseen parameter values.