ACO-Path : ACO-based informative path planning with Gaussian processes for water monitoring with a fleet of ASVs

Abstract

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, ACOPath 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 shows that ACO-Path produces smoother trajectories, with fewer sharp turns 45 than counterpart baselines under the same mission constraints.