An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

• Published in: Electronics (Basel) 2021-07, Vol.10 (13), p.1605
• Main Authors: Kathen, Micaela Jara Ten, Flores, Isabel Jurado, Reina, Daniel Gutiérrez
• Format: Article
• Language: English
• Subjects: Autonomous vehicles; Deep learning; Distance learning; Gaussian process; Genetic algorithms; Monitoring; Monitoring systems; Optimization; Particle swarm optimization; Path planning; Planning; Sensors; Surface vehicles; Uncertainty; Water quality; Water resources; Water supply
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10131605

Controlling the water quality of water supplies has always been a critical challenge, and water resource monitoring has become a need in recent years. Manual monitoring is not recommended in the case of large water surfaces for a variety of reasons, including expense and time consumption. In the last few years, researchers have proposed the use of autonomous vehicles for monitoring tasks. Fleets or swarms of vehicles can be deployed to conduct water resource explorations by using path planning techniques to guide the movements of each vehicle. The main idea of this work is the development of a monitoring system for Ypacarai Lake, where a fleet of autonomous surface vehicles will be guided by an improved particle swarm optimization based on the Gaussian process as a surrogate model. The purpose of using the surrogate model is to model water quality parameter behavior and to guide the movements of the vehicles toward areas where samples have not yet been collected; these areas are considered areas with high uncertainty or unexplored areas and areas with high contamination levels of the lake. The results show that the proposed approach, namely the enhanced GP-based PSO, balances appropriately the exploration and exploitation of the surface of Ypacarai Lake. In addition, the proposed approach has been compared with other techniques like the original particle swarm optimization and the particle swarm optimization with Gaussian process uncertainty component in a simulated Ypacarai Lake environment. The obtained results demonstrate the superiority of the proposed enhanced GP-based PSO in terms of mean square error with respect to the other techniques.