Work

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

Year: 2026

Type: article

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, 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.

Source: Electronics

DOI: 10.3390/electronics15030676

Authors: Micaela Jara Ten Kathen, Natalia Benitez, Mario Arzamendia, Daniel Gutiérrez Reina

Institutions: Universidad Católica Nuestra Señora de la Asunción, Universidad Politécnica Taiwán Paraguay, Universidad Nacional de Asunción, Universidad de Sevilla

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $2,896