{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/trajectory-optimisation-of-a-swarm-orbiting","title":"Trajectory Optimisation of a Swarm Orbiting 67P/Churyumov-Gerasimenko Maximising Gravitational Signal","arxiv_id":"2306.01602","date":"2023-06-02","proceeding":null,"authors":["Rasmus Maråk","Emmanuel Blazquez","Pablo Gómez"],"abstract":"Proper modelling of the gravitational fields of irregularly shaped asteroids and comets is an essential yet challenging part of any spacecraft visit and flyby to these bodies. Accurate density representations provide crucial information for proximity missions which rely heavily on it to design safe and efficient trajectories. This work explores using a spacecraft swarm to maximise the measured gravitational signal in a hypothetical mission around the comet 67P/Churyumov-Gerasimenko. Spacecraft trajectories are simultaneously propagated with an evolutionary optimisation approach to maximise overall signal return. The propagation is based on an open-source polyhedral gravity model using a detailed mesh of 67P and considers the comet's sidereal rotation. We compare performance on a mission scenario using one and four spacecraft. The results show that the swarm achieved almost twice the single spacecraft coverage over a fixed mission duration. However, optimising for a single spacecraft results in a more effective trajectory. Overall, this work serves as a testbed for efficiently designing a set of trajectories in this complex gravitational environment balancing measured signals and risks in a swarm scenario. The codebase and results are publicly available at https://github.com/rasmusmarak/TOSS","url_abs":"https://arxiv.org/abs/2306.01602v1","url_pdf":"https://arxiv.org/pdf/2306.01602v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"trajectory-optimisation-of-a-swarm-orbiting","repo_url":"https://github.com/rasmusmarak/toss","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}