SET PhD researcher Giulia Viavattene has presented her research at the 15th International Conference on Hybrid Artificial Intelligence Systems (HAIS’20), held virtually due to the pandemic.

Giulia's work, titled "Artificial Neural Network Design for Tours of Multiple Asteroids" and co-authored by Matteo Ceriotti, developed a method based on Artificial Neural Networks (ANNs) to quickly estimate the cost and duration of low-thrust transfers between asteroids. Designing multiple near-Earth asteroid (NEA) rendezvous missions is a complex global optimization problem, which involves the solution of a large combinatorial part to select the sequences of asteroids to visit. Given that more than 22,000 NEAs are known to date, trillions of permutations between asteroids need to be considered. Results show that ANN can estimate the cost or duration of optimal low-thrust transfers with high accuracy, resulting into a mean relative error of less than 4%.

The proceedings of the conference are published in a special issue of Lecture Notes in Computer Science book series (LNCS, volume 12344), HAIS 2020: Hybrid Artificial Intelligent Systems by Springer.

First published: 11 November 2020

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Conference paper: Artificial Neural Network Design for Tours of Multiple Asteroids

15th International Conference on Hybrid Artificial Intelligence Systems (HAIS’20)

Giulia Viavattene's video presentation (YouTube)