Fully distributed and resilient source seeking for robot swarms

[2410.15921] Fully distributed and resilient source seeking for robot swarms Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2410.15921 (cs) [Submitted on 21 Oct 2024 ( v1 ), last revised 16 Aug 2026 (this version, v3)] Title: Fully distributed and resilient source seeking for robot swarms Authors: Jesus Bautista , Antonio Acuaviva , Jose Hinojosa , Weijia Yao , Juan Jimenez , Hector Garcia de Marina View a PDF of the paper titled Fully distributed and resilient source seeking for robot swarms, by Jesus Bautista and 5 other authors View PDF Abstract: Existing source-seeking algorithms for robot swarms typically require either direct gradient measurements or rigid geometric formations, limiting their flexibility and resilience to robot failures. We propose a fully distributed solution that overcomes these limitations by computing an ascending direction through local field measurements and distributed estimation of centroid-relative coordinates. The resulting architecture consists of three exponentially convergent algorithms operating in a slow-fast closed-loop system, enabling simultaneous estimation and motion control without central coordination. Our framework accommodates arbitrary swarm geometries and analyzes how the spatial distribution of robots affects gradient observability, robustness, and resilience to failures. We characterize optimal swarm shapes that guarantee alignment with the true gradient and show how shape morphing can maneuver the collective motion. The approach is developed for kinematic points in $\mathbb{R}^m$ and extended to 2D unicycles with constant speed. Simulations with large-scale swarms validate the methodology. Comments: 16 pages, T-TAC. Jesus Bautista and Antonio Acuaviva contributed equally to this work Subjects: Robotics (cs.RO) ; Systems and Control (eess.SY) Cite as: arXiv:2410.15921 [cs.RO] (or arXiv:2410.15921v3 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2410.15921 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jesús Bautista [ view email ] [v1] Mon, 21 Oct 2024 11:52:37 UTC (13,174 KB) [v2] Tue, 24 Jun 2025 11:05:34 UTC (12,135 KB) [v3] Sun, 16 Aug 2026 10:13:00 UTC (3,924 KB) Full-text links: Access Paper: View a PDF of the paper titled Fully distributed and resilient source seeking for robot swarms, by Jesus Bautista and 5 other authors View PDF TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2024-10 Change to browse by: cs cs.SY eess eess.SY References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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