Uncertainty-guided informative path planning for ecological monitoring using autonomous surface vehicles under Dubins motion constraints.

Autonomous surface vehicles (ASVs) enable efficient in-situ data collection for large-scale ecological monitoring; however, effective environmental mapping requires planning strategies that account for not only informative measurements, but also vehicle motion constraints and limited mission resources. Existing approaches often rely on stationary environmental models or loosely coupled planning frameworks that do not fully exploit model uncertainty when generating feasible trajectories. To address these limitations, we propose a closed-loop informative path planning (IPP) framework that tightly integrates environmental modeling and trajectory generation for autonomous sampling. The proposed approach combines a nonstationary uncertainty representation using Gaussian Process Regression with Attentive Kernels (AK-GPR), uncertainty- guided adaptive sampling, and a Dubins-constrained RRT* planner to generate dynamically feasible and information-rich trajectories. The proposed framework is evaluated through staged experiments, including simulation and field validation, across representative ecological monitoring scenarios such as algal plume tracking, bathymetric mapping, and seagrass probability estimation. The results demonstrate that the proposed planner shows improved performance relative to the baseline planners in different initialization grid densities, with particularly strong performance in scenarios with limited prior information. In all environments, the proposed IPP framework achieved an average reduction of approximately 24% in mean absolute error (MAE) and 32% in predictive uncertainty compared to baseline planners, with improvements reaching up to 33% in MAE and 42%, respectively, in sparse initialization settings. These results demonstrate the benefits of a tightly coupled framework that balances uncertainty reduction and spatial coverage, enabling more efficient environmental exploration under realistic vehicle constraints.