Towards Robotic Lake Maintenance: Integrating SONAR and Satellite Data to Assist Human Operators
This work addresses the labor-intensive maintenance of artificial lakes for operators, but it is incremental as it combines existing technologies in a new application.
The paper tackles the problem of managing submerged aquatic vegetation in artificial lakes by proposing a two-step approach using satellite data for initial detection and an unmanned surface vehicle with SONAR for precise mapping, demonstrating feasibility for targeted weed harvesting.
Artificial Water Bodies (AWBs) are human-made systems that require continuous monitoring due to their artificial biological processes. These systems demand regular maintenance to manage their ecosystems effectively. As a result of these artificial conditions, underwater vegetation can grow rapidly and must be harvested to preserve the ecological balance. This paper proposes a two-step approach to support targeted weed harvesting for the maintenance of artificial lakes. The first step is the initial detection of Submerged Aquatic Vegetation (SAV), also referred to in this paper as areas of interest, is performed using satellite-derived indices, specifically the Aquatic Plants and Algae (APA) index, which highlights submerged vegetation in water bodies. Subsequently, an Unmanned Surface Vehicle (USV) equipped with multibeam SOund NAvigation and Ranging (SONAR) performs high-resolution bathymetric mapping to locate and quantify aquatic vegetation precisely. This two-stage approach offers an effective human-robot collaboration, where satellite data guides the USV missions and boat skippers leverage detailed SONAR maps for targeted harvesting. This setup narrows the search space and reduces manual workload from human operators, making the harvesting process less labour-intensive for operators. Preliminary results demonstrate the feasibility of integrating satellite imagery and underwater acoustic sensing to improve vegetation management in artificial lakes.