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
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
kh
10
W
Motor Constant
km
83
kg⋅m−1
Minimum State of Charge
bmin
0
Wh
Maximum State of Charge
bmax
6500
Wh
Minimum Speed
umin
0
m⋅s−1
Maximum Speed
umax
2.315
m⋅s−1
TABLE I : SeaTrac SP-48 Energetic Parameters
Fig. 2 : 24 Hour Solar Irradiance Model
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 λcd . 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 Ls , and temporal correlation scale Lt . Color identifies W , line style identifies Ls , and marker shape identifies Lt .
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 0.081m/s for ergodic control and 0.533m/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 −0.894m/s for ergodic control and 1.176m/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 0.048m/s for ergodic control and 0.846m/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.
Informative path planning chooses robot routes and sensing actions that gather useful information while respecting travel, energy, time, and safety constraints.
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.
Kavin M. Govindarajan, Dimitra Panagou, Chris Vermillion
Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA · Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Autonomous surface vehicles (ASVs) offer an efficient solution for environmental cleanup as well as search and rescue operations in open waters. Targets in these settings drift continuously, so efficient search must balance exploration of unobserved regions with tracking of known targets. However, most target tracking and pursuit scenarios consider simple guidance behaviours and short-term predictions for decision-making. In this letter, we address the problem of search and capture of multiple drifting targets, such as litter, in marine environments, using an adaptive informative planning approach. A key aspect of our strategy is a spatiotemporal informative planning method based on model predictive path integral (MPPI) control, a sampling-based model predictive control method. Our planner balances search and tracking objectives with a cost design that couples spatiotemporal target drift predictions and exploration objectives. The planner directly generates kinematic-level commands by optimising continuous trajectories over long horizons. In the interception stage, we use pure pursuit guidance for physical capture of the targets. Simulations show that our planner captures up to 18% more targets than the strongest baseline, proving its efficiency for environmental cleanup. Finally, we validate our approach in field trials with an ASV.
Sanjeev Ramkumar Sudha, Marija Popović, Erlend M. Coates
Norwegian University of Science and Technology (NTNU), Norway · TU Delft, The Netherlands
An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of greatest value, and paying for every meter of travel and the cost of every measurement it takes. Classical information-seeking and reward-seeking criteria address only one of these objectives at a time. Here, we propose Expected Free Energy (EFE), the principled action-selection objective from active inference, as a unifying criterion for budgeted robotic informative path planning. Maintaining a Gaussian-process belief over the information field, our agent plans continuous trajectories that minimize expected free energy under hard path-length constraints. The results from multiple realizations show that EFE-based planning yields accurate posterior maps and locates the highest-value regions simultaneously, outperforming information-theoretic baselines under the same settings. In robotic exploration, these unified, easy-to-tune principled information-gathering strategies facilitate autonomous deployment while enforcing efficiency and resource constraints.
Ajith Anil Meera, Pablo Lanillos, Wouter Kouw
TU Eindhoven, The Netherlands · Cajal Centre for Neuroscience, Spanish National Research Council, Madrid, Spain