Artificial Neural Networks for Multiple NEA Rendezvous Missions with Continuous Thrust

Abstract

The interest for near-Earth asteroids for scientific studies and, in particular, for potentially hazardous asteroids requires the space community to perform multiple-asteroid missions with close-up observations. To this end, multiple near-Earth asteroid rendezvous missions can help reduce the cost of the mission. Given the enormous number of asteroids, this work proposes a method based on artificial neural networks (ANNs) to quickly estimate the transfer time and cost between asteroids using low-thrust propulsion. The neural network output is used in a sequence-search algorithm based on a tree-search method to identify feasible sequences of asteroids to rendezvous. The rendezvous sequences are optimized by solving an optimal control problem for each leg to verify the feasibility of the transfer. The effectiveness of the presented methodology is assessed through sequences of asteroids of interest optimized using two low-thrust propulsion systems, namely solar electric propulsion and solar sailing. The results show that ANNs are able to estimate the duration and cost of low-thrust transfers with high accuracy in a modest computational time.