Artificial Neural Network For Preliminary Multiple NEA Rendezvous Mission Using Low Thrust

摘要

Since the 1960s the study of near-Earth asteroids (NEAs) has become extremely interesting for science, Earth protection, and future exploitation of their resources. The knowledge of these objects can be considerably improved by multiple NEA rendezvous missions with close-up observations of several asteroids. Given the enormous number of NEAs, which have been discovered until now, it becomes paramount to develop a method for quick identification of the transfer time and cost. This work develops a methodology based on Artificial Neural Networks (ANN) to identify a preliminary multiple NEA rendezvous trajectory using low-thrust propulsion. It takes advantage of the ANN capability to map the transfer time and cost starting from parameters that can describe the initial and final orbits and boundary conditions of the transfer. The ANN architecture and parameters are tuned to provide an optimal performance. The outcome of the network is used as input in a combinatorial problem to search for the asteroid sequence to visit, where a tree-search method is employed. Once the multiple rendezvous sequence is identified, the feasibility of the transfer with the given propulsion system is studied. Thus, an optimal control problem is solved for each leg by means of an optimisation solver based on pseudospectral method. The performance of the presented method is assessed by conducting analyses of sequences of asteroids of interest using different low-thrust options, such as solar electric propulsion and solar sailing.