Balancing Safety and Optimality in Robot Path Planning: Algorithm and Metric

[2505.23197] Balancing Safety and Optimality in Robot Path Planning: Algorithm and Metric Skip to main content Search Submit Donate Log in Computer Science > Robotics arXiv:2505.23197 (cs) [Submitted on 29 May 2025 ( v1 ), last revised 25 Aug 2026 (this version, v5)] Title: Balancing Safety and Optimality in Robot Path Planning: Algorithm and Metric Authors: Jatin Kumar Arora , Soutrik Bandyopadhyay , Sunil Sulania , Shubhendu Bhasin View PDF HTML (experimental) Abstract: Path planning for autonomous robots faces a fundamental trade-off between path length and obstacle clearance. While existing algorithms typically prioritize a single objective, we introduce the Unified Path Planner (UPP), a graph-search algorithm that dynamically balances safety and optimality via adaptive heuristic weighting. UPP employs a local inverse-distance safety field and auto-tunes its parameters based on real-time search progress, maintaining bounded heuristic behavior while maintaining superior clearance. To enable rigorous evaluation, we introduce the OptiSafe index, a normalized metric that quantifies the trade-off between safety and optimality. Extensive evaluation across 10 environments shows that UPP achieves a 0.94 OptiSafe score in cluttered environments, compared with 0.22-0.85 for existing methods, with only 0.5-1% path-length overhead in simulation and a 100% success rate. Hardware validation on TurtleBot confirms practical advantages despite sim-to-real gaps. Comments: 31 pages Subjects: Robotics (cs.RO) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2505.23197 [cs.RO] (or arXiv:2505.23197v5 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2505.23197 Focus to learn more arXiv-issued DOI via DataCite Journal reference: International Journal of Intelligent Robotics and Applications, 2026 Related DOI : https://doi.org/10.1007/s41315-026-00589-w Focus to learn more DOI(s) linking to related resources Submission history From: Jatin Arora [ view email ] [v1] Thu, 29 May 2025 07:34:56 UTC (1,347 KB) [v2] Fri, 29 Aug 2025 08:59:50 UTC (332 KB) [v3] Sat, 14 Mar 2026 05:03:11 UTC (1,872 KB) [v4] Mon, 24 Aug 2026 02:33:56 UTC (1,085 KB) [v5] Tue, 25 Aug 2026 09:19:48 UTC (1,085 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 | 2025-05 Change to browse by: cs cs.AI 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)

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