Solar-Sail Trajectory Design for Multiple Near Earth Asteroid Exploration Based on Deep Neural Networks

TL;DR

This paper introduces a deep neural network-based method to quickly estimate solar sail transfer times between orbits, combined with Monte Carlo Tree Search for optimal asteroid exploration sequences, validated through two examples.

Contribution

It presents a novel approach integrating deep neural networks and Monte Carlo Tree Search for efficient multi-asteroid trajectory planning with solar sails.

Findings

Deep neural networks effectively estimate transfer times.

Monte Carlo Tree Search optimizes asteroid exploration sequences.

Method validated with two practical examples.

Abstract

In the preliminary trajectory design of the multi-target rendezvous problem, a model that can quickly estimate the cost of the orbital transfer is essential. The estimation of the transfer time using solar sail between two arbitrary orbits is difficult and usually requires to solve an optimal control problem. Inspired by the successful applications of the deep neural network in nonlinear regression, this work explores the possibility and effectiveness of mapping the transfer time for solar sail from the orbital characteristics using the deep neural network. Furthermore, the Monte Carlo Tree Search method is investigated and used to search the optimal sequence considering a multi-asteroid exploration problem. The obtained sequences from preliminary design will be solved and verified by sequentially solving the optimal control problem. Two examples of different application backgrounds validate the effectiveness of the proposed approach.