Towards Robotic Lake Maintenance: Integrating SONAR and Satellite Data to Assist Human Operators

TL;DR

This paper presents a two-step method combining satellite imagery and SONAR mapping to assist human operators in targeted aquatic vegetation harvesting in artificial lakes, reducing manual effort and improving ecological management.

Contribution

It introduces an integrated approach using satellite indices and SONAR data for precise underwater vegetation detection and mapping, enhancing human-robot collaboration in lake maintenance.

Findings

Satellite imagery effectively identifies areas of interest for vegetation.

SONAR mapping provides detailed bathymetric data for targeted harvesting.

Preliminary results confirm the feasibility of the integrated approach.

Abstract

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.