ACO-Path: ACO-Based Informative Path Planning with Gaussian Processes for Water Monitoring with a Fleet of ASVs

Autonomous surface vehicles can support water-quality monitoring, but they require planners that place measurements where they most improve the environmental estimate under mission constraints. This paper proposes ACO-Path, an informative path planner that couples Ant Colony Optimization -Ant System- with online Gaussian Process mapping. During the mission, the Gaussian Process updates a mean or contamination map and a variance or uncertainty map, from which dynamic action zones are derived and used to guide an explicit explore then exploit policy. The method is evaluated in a simulated water resource monitoring scenario inspired by Lake Ypacaraí, considering three exploration distances and two heuristic weights. In a comparison against five baseline planners, ACO-Path achieves the lowest hotspot error, Errorpeak=0.19896±0.39400, while remaining competitive in global reconstruction, MSEmap=0.00144±0.00348, R2=0.96066±0.09861. In addition, a turning analysis based on the absolute heading change between consecutive segments |Δα| shows that ACO-Path produces smoother trajectories, with fewer sharp turns |Δα|≥45° than counterpart baselines under the same mission constraints.