Projeto e construção de um veículo de superfície não tripulado, para o monitoramento dos parâmetros físico-químicos de corpos hídricos

Abstract

The growing concern for sustainable water resource management stems from increased pollution and climate change, which affect water quality, human health, and aquatic ecosystems. Data from the ONG SOS Mata Atlântica shows that, in 2023, 15% of the 129 rivers monitored in the watersheds of the Mata Atlântica biome still do not present suitable conditions for use, highlighting the fragility of aquatic environments. This scenario is further exacerbated by the impacts of mining, which generates sediments contaminated with toxic substances such as arsenic, lead, and cyanide, increasing pollution risks. Additionally, the collapse of tailings dams, as seen in Mariana (MG), causes river contamination, siltation, fish mortality, and the destruction of riparian vegetation, directly affecting the communities that rely on these resources. To address these challenges, technologies such as Unmanned Surface Vehicles (USVs) offer efficient solutions for real-time data collection, especially in hard-to-reach areas. This work presents the construction of a USV prototype developed to monitor the physicochemical parameters of water bodies, using a practical and cost-effective approach. The prototype was designed to operate in various aquatic environments, from reservoirs to rivers and lakes, even under adverse conditions. During testing, the USV collected data from 1,371 points over approximately one hour, with 976 points obtained dynamically at a sampling rate of 1 point every 1.5 seconds. This dynamic sampling capability represents a significant advancement over traditional manual collection methods, highlighting the efficiency and versatility of the prototype. The USV provides an accessible and replicable tool that facilitates the identification of water quality changes, enabling quick responses to contamination and the implementation of corrective actions. The future integration of advanced technologies such as high-speed data transmission and Artificial Intelligence further enhances its potential in environmental monitoring.