{
  "abstract": "Gravity-assist trajectory design requires searching numerous leg combinations, but applying nonlinear programming (NLP) for low-thrust trajectory optimization to all combinations is computationally prohibitive. Therefore, conventional frameworks first perform broad search using lightweight trajectory models such as Lambert-based calculation and simplified deep-space maneuver models, while pruning infeasible candidates. However, to maintain efficiency, conventional approaches handle only low-dimensional formulations with limited constraints such as up to a couple of impulse maneuvers per leg. Consequently, they cannot accommodate the constrained multivariable optimization required for low-thrust design, running the risk of prematurely pruning viable trajectories. To address this, this paper introduces a convex pruning approach capable of solving constrained multivariable problems directly within broad search while retaining the computational efficiency. We then augment the broad search stage with a robust local low-thrust optimization algorithm based on thrust regularization, which together enable global exploration and optimization in an automated fashion. We apply the proposed framework in a BepiColombo-inspired scenario involving nine gravity assists, successfully demonstrating its broad search capability to discover a far greater number of solutions (36,335) compared to a conventional approach (4,664) while identifying a wider feasible launch window by one year.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.09515",
  "author_total": 3,
  "authors": [
    {
      "name": "Ryo Iijima"
    },
    {
      "name": "Kenshiro Oguri"
    },
    {
      "name": "Toshinori Kuwahara"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fwgsb8wksx8b3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.09515",
  "fulltext_url": null,
  "id": "fwgsb8wksx8b3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.09515",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2202|Spacecraft Dynamics and Control"
    ]
  ],
  "openalex": "W7212176192",
  "pdf_url": "https://arxiv.org/pdf/2609.09515",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fwgsb8wksx8b3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:15Z",
      "native_id": "2609.09515",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.09515"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Global Optimization Framework for Automated Low-Thrust Gravity-Assist Trajectory Design",
  "title_text": "Global Optimization Framework for Automated Low-Thrust Gravity-Assist Trajectory Design",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:15Z",
  "url": "https://research.draconitic.com/paper/fwgsb8wksx8b3",
  "venue": "arXiv",
  "versions": []
}