Asteroid Mining: ACT&Friends' Results for the GTOC 12 Problem
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910904787927040 |
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| author | Izzo, Dario Märtens, Marcus Beauregard, Laurent Bannach, Max Acciarini, Giacomo Blazquez, Emmanuel Hadjiivanov, Alexander Grover, Jai Heißel, Gernot Shimane, Yuri Yam, Chit Hong |
| author_facet | Izzo, Dario Märtens, Marcus Beauregard, Laurent Bannach, Max Acciarini, Giacomo Blazquez, Emmanuel Hadjiivanov, Alexander Grover, Jai Heißel, Gernot Shimane, Yuri Yam, Chit Hong |
| contents | In 2023, the 12th edition of Global Trajectory Competition was organised around the problem referred to as "Sustainable Asteroid Mining". This paper reports the developments that led to the solution proposed by ESA's Advanced Concepts Team. Beyond the fact that the proposed approach failed to rank higher than fourth in the final competition leader-board, several innovative fundamental methodologies were developed which have a broader application. In particular, new methods based on machine learning as well as on manipulating the fundamental laws of astrodynamics were developed and able to fill with remarkable accuracy the gap between full low-thrust trajectories and their representation as impulsive Lambert transfers. A novel technique was devised to formulate the challenge of optimal subset selection from a repository of pre-existing optimal mining trajectories as an integer linear programming problem. Finally, the fundamental problem of searching for single optimal mining trajectories (mining and collecting all resources), albeit ignoring the possibility of having intra-ship collaboration and thus sub-optimal in the case of the GTOC12 problem, was efficiently solved by means of a novel search based on a look-ahead score and thus making sure to select asteroids that had chances to be re-visited later on. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_20839 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Asteroid Mining: ACT&Friends' Results for the GTOC 12 Problem Izzo, Dario Märtens, Marcus Beauregard, Laurent Bannach, Max Acciarini, Giacomo Blazquez, Emmanuel Hadjiivanov, Alexander Grover, Jai Heißel, Gernot Shimane, Yuri Yam, Chit Hong Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning In 2023, the 12th edition of Global Trajectory Competition was organised around the problem referred to as "Sustainable Asteroid Mining". This paper reports the developments that led to the solution proposed by ESA's Advanced Concepts Team. Beyond the fact that the proposed approach failed to rank higher than fourth in the final competition leader-board, several innovative fundamental methodologies were developed which have a broader application. In particular, new methods based on machine learning as well as on manipulating the fundamental laws of astrodynamics were developed and able to fill with remarkable accuracy the gap between full low-thrust trajectories and their representation as impulsive Lambert transfers. A novel technique was devised to formulate the challenge of optimal subset selection from a repository of pre-existing optimal mining trajectories as an integer linear programming problem. Finally, the fundamental problem of searching for single optimal mining trajectories (mining and collecting all resources), albeit ignoring the possibility of having intra-ship collaboration and thus sub-optimal in the case of the GTOC12 problem, was efficiently solved by means of a novel search based on a look-ahead score and thus making sure to select asteroids that had chances to be re-visited later on. |
| title | Asteroid Mining: ACT&Friends' Results for the GTOC 12 Problem |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2410.20839 |