Simulation, optimization and control of trajectories of ASVs performing HACBs monitoring missions in lentic waters

Abstract

Harmful Algae and Cyanobacteria Blooms(HACBs) are dangerous dynamic processes for the users/inhabitants of the hydric resources. Their development and contingency plans can be anticipated by using Autonomous Surface Vehicles (ASVs) equipped with a self-driven system capable of deciding how to displace the ASV and its multi-parametric probe to take measurements in the 3D locations of the water body where the HACB is likely to occur. This paper presents a new self-driven system for that purpose, consistent on 1) an offline trajectory planner for the ASV that exploits the information provided by a commercial HACBs simulator to optimize, in turn, the ASV horizontal and probe vertical displacements; and 2) a guidance and control system specially designed for making the ASV follow the planned trajectories. The paper also presents a comprehensive set of simulations to evaluate our proposal’s performance and adjust its parameters.Resumen de autor: This paper presents an advanced autonomous navigation and control system for Autonomous Surface Vehicles (ASVs) designed to monitor Harmful Algae and Cyanobacteria Blooms (HACBs) in lentic water bodies, such as lakes and reservoirs. Given the ecological and health risks posed by HACBs, efficient 3D monitoring of water parameters is crucial for early detection and contingency planning. Methodology: The proposed system consists of two main components: (1) an offline trajectory planner that utilizes data from a commercial HACBs simulator to optimize both the horizontal path of the ASV and the vertical displacement of its multi-parametric probe, and (2) a guidance and control system specifically engineered to ensure the vehicle accurately follows the optimized 3D trajectories. The study employs a comprehensive simulation environment to tune parameters and validate the system's effectiveness. Results: The simulation results demonstrate the system's capability to effectively plan and execute missions that maximize information gathering in dynamic environments. The control system proved robust in guiding the ASV along planned paths, allowing for precise data collection at various depths and locations where blooms are most likely to occur. Conclusions: The integration of predictive simulation with optimized trajectory planning significantly enhances the efficiency of environmental monitoring missions. This research provides a scalable framework for using autonomous robotics in the preservation of hydric resources, offering a proactive tool for water quality management and the mitigation of toxic algae proliferation.