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

For example, a grid-based search is a classical approach for spacecraft preliminary trajectory design. However, this technique is more suitable for impulsive trajectory since the search space is much smaller. Due to the curse of dimensionality, low-thrust trajectory design often needs a more intelligent global search algorithm. Evolutionary algorithms, including Differential Evolution (DE) [4], Genetic algorithm (GA) [5], Particle swarm optimization (PSO) [6], etc., have been widely used in global optimization problems in spacecraft trajectory design [7, 8, 9, 10]. These algorithms iteratively generate new solutions by introducing randomness to previously obtained solutions and downselecting the solutions based on specific quality metrics. In addition, researchers also combine stochastic search algorithms with local gradient-based optimizers to attempt to find the globally optimal solution. The multistart method samples the search space with a fixed distribution and feeds the samples into a local optimizer as starting points for local search [10]. Inspired by energy minimization principles in computational chemistry, Monotonic Basin Hopping (MBH) [11, 12] adds random perturbations during the local search to uncover multiple local optima solutions that are close to each other. MBH rapidly became popular in the sphere of spacecraft trajectory design [1, 13, 14] and has been established as the state-of-the-art algorithm in terms of efficiency and solution quality through various benchmarks [15, 9, 10].