{
  "abstract": "Semidefinite programming (SDP) provides a principled framework for convex relaxations of nonconvex geometric constraints in motion planning, yet existing solvers are too computationally expensive for real-time control, particularly on resource-constrained embedded systems. To address this gap, we introduce TinySDP, the first semidefinite programming solver designed for embedded systems, enabling real-time model-predictive control (MPC) on microcontrollers for problems with nonconvex obstacle constraints. Our approach integrates positive-semidefinite cone projections into a cached-Riccati-based ADMM solver, leveraging computational structure for embedded tractability. We pair this solver with an a posteriori rank-1 certificate that converts relaxed solutions into explicit geometric guarantees at each timestep. On challenging benchmarks, e.g., cul-de-sac and dynamic obstacle avoidance scenarios that induce failures in local methods, TinySDP achieves collision-free navigation with up to 73% shorter paths than state-of-the-art baselines. We validate our approach on a Crazyflie quadrotor, demonstrating that semidefinite constraints can be enforced at real-time rates for agile embedded robotics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.13748",
  "author_total": 7,
  "authors": [
    {
      "name": "Ishaan Mahajan"
    },
    {
      "name": "Jon Arrizabalaga"
    },
    {
      "name": "Andrea Grillo"
    },
    {
      "name": "Fausto Vega"
    },
    {
      "name": "James Anderson"
    },
    {
      "name": "Zachary Manchester"
    },
    {
      "name": "Brian Plancher"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8rys28h1gjzhq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.13748",
  "fulltext_url": null,
  "id": "8rys28h1gjzhq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.13748",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.RO"
    ],
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "math.OC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2605.13748",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8rys28h1gjzhq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:45Z",
      "native_id": "2605.13748",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.13748"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics",
  "title_text": "TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:45Z",
  "url": "https://research.draconitic.com/paper/8rys28h1gjzhq",
  "venue": "arXiv",
  "versions": []
}