[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
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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)
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