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[Submitted on 14 Oct 2026] Title:Clarity-Driven Adaptive Mission Planning for a Solar-Powered Autonomous Surface Vessel View a PDF of the paper titled Clarity-Driven Adaptive Mission Planning for a Solar-Powered Autonomous Surface Vessel, by Kavin M. Govindarajan and 7 other authors View PDF HTML (experimental) Abstract:This paper presents a comprehensive methodology, backed by simulation and experimental results, for an adaptive mission planning strategy that is mathematically grounded in the maximization of the information-driven quantity of clarity. In particular, the methodology introduced in this work addresses the use of sustainably powered autonomous surface vessels (ASVs) to perform scientific missions that prioritize locations of maximum interest. Because these locations of maximum interest are not known a priori but are rather identified based on in-situ measurements, online adaptation is essential for mission planning. Furthermore, the reliance on a limited-power renewable resource requires an adaptation strategy that maximizes information gathering subject to mobility restrictions imposed by the renewable resource. Our approach addresses these challenges through a clarity-driven ergodic search, where clarity represents a mathematical quantity that has been rigorously linked to differential (information) entropy. In our approach, a target clarity map is adapted online to reflect the prioritization of certain areas based on in-situ measurements, and an ergodic path planner continually re-plans based on the target clarity map and ASV speed limitations (driven by the renewable propulsion source). Focusing on a solar-powered ASV, we present simulations and experiments conducted at Jordan Lake, North Carolina, demonstrating the effectiveness of the proposed approach. Subjects: Robotics (cs.RO); Systems and Control (eess.SY) Cite as: arXiv:2610.20871 [cs.RO] (or arXiv:2610.20871v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2610.20871 arXiv-issued DOI via DataCite (pending registration) Submission history From: Kavin Govindarajan [view email] [v1] Wed, 14 Oct 2026 20:00:27 UTC (1,869 KB) Full-text links: Access Paper: View a PDF of the paper titled Clarity-Driven Adaptive Mission Planning for a Solar-Powered Autonomous Surface Vessel, by Kavin M. Govindarajan and 7 other authors View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.RO new | recent | 2026-10 Change to browse by: cs cs.SY eess eess.SY References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: 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?)