Receding Fixed-Horizon Optimization for Near-Time-Optimal Trajectory Planning and Control

[2503.11072] Receding Fixed-Horizon Optimization for Near-Time-Optimal Trajectory Planning and Control Skip to main content Search Submit Donate Log in Computer Science > Robotics arXiv:2503.11072 (cs) [Submitted on 14 Mar 2025 ( v1 ), last revised 28 Aug 2026 (this version, v4)] Title: Receding Fixed-Horizon Optimization for Near-Time-Optimal Trajectory Planning and Control Authors: Haotian Tan , Yuan-Hua Ni View PDF HTML (experimental) Abstract: Time-optimal trajectory planning and control is central for autonomous vehicles, yet its application and real-time deployment confronts two fundamental challenges: the non-convexity of optimal control problems and the unpredictable computation time inherent to nonlinear programming. To address these challenges, we propose a hierarchical convex optimization framework that addresses both issues by decomposing the original problem into short, fixed-horizon planning cycles. Each cycle solves a convex subproblem within a collision-free region identified by a customized search algorithm; the complete trajectory and control is assembled by concatenating state-input sequences across cycles. Under mild assumptions, we establish finite-time convergence of the decomposition procedure and show that the concatenated solution satisfies the necessary conditions for local optimality. Numerical experiments on randomly generated maps with static and dynamic obstacles demonstrate that the proposed algorithm achieves a higher success rate and substantially lower computation time than sequential convex programming, while maintaining comparable control time. These results show that decomposition-based convex optimization provides a practical pathway to reliable, real-time near-time-optimal trajectory planning. Subjects: Robotics (cs.RO) ; Optimization and Control (math.OC) Cite as: arXiv:2503.11072 [cs.RO] (or arXiv:2503.11072v4 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2503.11072 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Haotian Tan [ view email ] [v1] Fri, 14 Mar 2025 04:31:35 UTC (1,267 KB) [v2] Sun, 6 Apr 2025 05:40:19 UTC (1,268 KB) [v3] Mon, 10 Nov 2025 08:48:03 UTC (767 KB) [v4] Fri, 28 Aug 2026 05:11:49 UTC (799 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO < prev | next > new | recent | 2025-03 Change to browse by: cs math math.OC References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation 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)

Related documents

• Receding Fixed-Horizon Optimization for Near-Time-Optimal Trajectory Planning and Control#684216
• Shortcut Trajectory Planning for Efficient Offline Reinforcement Learning#361463
• Low-Thrust Trajectory Optimization with Quantum Computing and Sequential Convex Programming#820863
• HorizonFlow: Variable-Length Planning for Offline Goal-Conditioned RL#1132097
• Closed-Loop Motion Planning for Differentially Flat Systems: A Time-Varying Optimization Framework#1014448