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

• URL: http://arxiv.org/abs/2308.03960v1
• Date: Mon, 7 Aug 2023 23:52:03 GMT
• Title: Amortized Global Search for Efficient Preliminary Trajectory Design with Deep Generative Models
• Authors: Anjian Li, Amlan Sinha, Ryne Beeson
• Abstract summary: Preliminary trajectory design is a global problem that seeks multiple qualitatively different solutions to a trajectory optimization problem. In this paper, we exploit the structure in the solutions to propose an solutions amortized global search (AGS) framework. Our method is evaluated using Derust's 5th function and a low-th circular restricted three-body problem.
• Score: 1.1602089225841632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Our method is evaluated using De Jong's 5th function and a low-thrust circular restricted three-body problem.

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