Clarity-Driven Adaptive Mission Planning for a Solar-Powered Autonomous Surface Vessel

Kavin M. Govindarajan; Kaleb Ben Naveed; David Li; Devansh R. Agrawal; Mary Maceda; Matthew Bryant; Dimitra Panagou; Chris Vermillion

cs.ROOct 14, 2026

Clarity-Driven Adaptive Mission Planning for a Solar-Powered Autonomous Surface Vessel

Authors: Kavin M. Govindarajan, Kaleb Ben Naveed, David Li, Devansh R. Agrawal, Mary Maceda, Matthew Bryant, Dimitra Panagou, Chris Vermillion

Organizations: Kavin M. Govindarajan and Kaleb Ben Naveed are with the Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA · David Li is with the Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA · Devansh R. Agrawal is a graduate of the Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USA · Mary Maceda is with the Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695, USA

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.

Figures & tables

Fig. 1 : SeaTrac ASV considered in this work [ 27 ] .

Variable Name	Symbol	Value	Units
Hotel Load	$k_{\mathrm{h}}$	10	$W$
Motor Constant	$k_{\mathrm{m}}$	83	$kg\cdot m^{-1}$
Minimum State of Charge	$b_{\mathrm{min}}$	0	$Wh$
Maximum State of Charge	$b_{\mathrm{max}}$	6500	$Wh$
Minimum Speed	$u_{\mathrm{min}}$	0	$m\cdot s^{-1}$
Maximum Speed	$u_{\mathrm{max}}$	2.315	$m\cdot s^{-1}$

TABLE I : SeaTrac SP-48 Energetic Parameters

Fig. 3 : Block diagram of the proposed integrated control framework. Blue blocks correspond to clarity-driven path planning.

Fig. 4 : Example of tightened SOC constraints computed over a 24-hour period.

Strategy	Target distribution	Path planner
Transect	Uniform	Fixed lawnmower path
Ergodic, nonadaptive	Uniform	Ergodic controller
Ergodic, adaptive	STGPKF estimate	Ergodic controller
BB-IPP, nonadaptive	Uniform	Branch-and-bound IPP
BB-IPP, adaptive	STGPKF estimate	Branch-and-bound IPP
Ergodic, ground truth	Ground-truth field	Ergodic controller

TABLE II : Strategies used in the simulation study.

Fig. 5 : Mission domain at Jordan Lake, North Carolina.

Fig. 6 : Diagnostic simulation environments. The half-domain case separates favorable and unfavorable wind conditions, while the moving-pocket case contains multiple time-varying regions of near-rated wind.

Fig. 7 : Sensitivity to the target-selectivity coefficient

\lambda_{\mathrm{cd}}

. Moderate selectivity increases sampling near the rated wind speed, while large values can degrade exploration and reconstruction.

Fig. 8 : Half-domain results. Restricting the transect to the favorable half and providing the ergodic controller with the ground-truth target both reduce clarity deficit, while online adaptation substantially increases the fraction of near-rated measurements.

Fig. 9 : Moving-pocket results. Adaptation increases near-rated sampling for both planners, while the ground-truth-target ergodic controller better distributes sensing effort across multiple pockets.

Fig. 10 : Monte Carlo sensitivity results across rated wind speed, spatial correlation scale

L_{s}

, and temporal correlation scale

L_{t}

. Color identifies

W

, line style identifies

L_{s}

, and marker shape identifies

L_{t}

Fig. 11 : ASV trajectory for the 90-minute ergodic mission without speed control. The trajectory concentrates in the northeast portion of the domain, where measured wind speeds approach the rated value.

Fig. 12 : Measured-minus-rated wind-speed distributions for the 90-minute missions without speed control. The mean deviations are $0.081\,\mathrm{m/s}$ for ergodic control and $0.533\,\mathrm{m/s}$ for the transect.

Fig. 13 : ASV trajectory for the 90-minute ergodic mission with speed control. The trajectory covers a larger portion of the domain while the wind-field estimate develops.

Fig. 14 : Measured-minus-rated wind-speed distributions for the 90-minute variable-speed missions. The mean deviations are $-0.894\,\mathrm{m/s}$ for ergodic control and $1.176\,\mathrm{m/s}$ for the transect.

Fig. 15 : Ergodic mission: SOC and speed over a 90-minute variable-speed run. The controller adapts speed to track the nominal SOC trajectory.

Fig. 16 : Transect mission SOC and speed over time for a 90-minute variable-speed mission. The controller saturates at maximum speed because the available solar resource exceeded forecasts, preventing tracking of the nominal SOC trajectory.

Fig. 17 : ASV trajectory for a 3-hour variable-speed ergodic mission. The path concentrates in the northeast region where wind speeds approach the ASV’s rated value.

Fig. 18 : Measured-minus-rated wind-speed distributions for the 3-hour variable-speed missions. The mean deviations are $0.048\,\mathrm{m/s}$ for ergodic control and $0.846\,\mathrm{m/s}$ for the transect.

Mission type	Strategy	Mean (m/s)	SD (m/s)
90 min, fixed speed	Ergodic	0.081	2.146
90 min, fixed speed	Transect	0.533	2.052
90 min, variable speed	Ergodic	-0.894	1.595
90 min, variable speed	Transect	1.176	2.183
3 hour, variable speed	Ergodic	0.048	2.008
3 hour, variable speed	Transect	0.846	2.642

TABLE III : Summary of measured-minus-rated wind-speed statistics for experimental missions.

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Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA · Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA

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Sustainably powered mobile robots enable long-duration observational missions by harvesting energy from the environment. However, effective autonomy requires the robots to make decisions as to how to allocate this energy to maximize information quality over time. In this paper, we study a persistent (infinite-horizon) speed trajectory optimization problem that is posed as a periodic optimal control problem in which a robot repeatedly traverses a fixed closed path, coupling a super-linear propulsion cost with dynamic information gain and decay. Using the information-theoretic quantity of clarity as an information metric, we first derive the necessary conditions for optimal control. We then derive a number of specialized follow-on properties, which characterize (i) the nature of the optimal control when points on the path are of varying importance and when they are of equal importance, along with (ii) a high-speed information limit that is approached when energy reserves approach infinity. We illustrate the information-optimal speed trajectories on a simple example in order to showcase key trends that are followed under the optimized speed profile. This is followed by a simulation study based on the persistent characterization of salinity in the Chesapeake Bay, illustrating the practical merit of applying the results from this work.

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