Informed Hybrid Zonotope-based Motion Planning Algorithm

[2507.09309] Informed Hybrid Zonotope-based Motion Planning Algorithm Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2507.09309 (cs) [Submitted on 12 Jul 2025 ( v1 ), last revised 10 Apr 2026 (this version, v5)] Title: Informed Hybrid Zonotope-based Motion Planning Algorithm Authors: Peng Xie , Johannes Betz , Amr Alanwar View a PDF of the paper titled Informed Hybrid Zonotope-based Motion Planning Algorithm, by Peng Xie and 2 other authors View PDF HTML (experimental) Abstract: Optimal path planning in nonconvex free spaces poses substantial computational challenges. A common approach formulates such problems as mixed-integer linear programs (MILPs); however, solving general MILPs is computationally intractable and severely limits scalability. To address these limitations, we propose HZ-MP, an informed Hybrid Zonotope-based Motion Planner, which decomposes the obstacle-free space and performs low-dimensional face sampling guided by an ellipsotope heuristic, thereby concentrating exploration on promising transition regions. This structured exploration mitigates the excessive wasted sampling that degrades existing informed planners in narrow-passage or enclosed-goal scenarios. We prove that HZ-MP is probabilistically complete and asymptotically optimal, and demonstrate empirically that it converges to high-quality trajectories within a small number of iterations. Subjects: Robotics (cs.RO) Cite as: arXiv:2507.09309 [cs.RO] (or arXiv:2507.09309v5 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2507.09309 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Peng Xie [ view email ] [v1] Sat, 12 Jul 2025 14:54:46 UTC (367 KB) [v2] Sat, 19 Jul 2025 17:13:29 UTC (367 KB) [v3] Sun, 5 Apr 2026 16:29:21 UTC (719 KB) [v4] Thu, 9 Apr 2026 08:16:54 UTC (719 KB) [v5] Fri, 10 Apr 2026 06:02:08 UTC (375 KB) Full-text links: Access Paper: View a PDF of the paper titled Informed Hybrid Zonotope-based Motion Planning Algorithm, by Peng Xie and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2025-07 Change to browse by: cs 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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