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

Ahmed H. Elsayed, Christoph Manss, Tarek A. El-Mihoub, Andrej Lejman, Frederic Stahl

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.

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

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.
Paper Structure (8 sections, 1 equation, 7 figures, 2 algorithms)

This paper contains 8 sections, 1 equation, 7 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. Methodology & Mission Setup
  3. Satellite Imagery For Vegetation Detection
  4. Underwater SONAR Mapping
  5. Experimental Results & Findings
  6. Stage 1: Satellite
  7. Stage 2: SONAR-based Mapping results
  8. Discussion

Figures (7)

  • Figure 1: Mission setup. 1-Satellite detection: potential SAV clusters marked in green. 2-On‑site survey:USV and harvester deployed. 3-Vegetation map: SONAR‑derived weed height overlaid on Maschsee Lake map.
  • Figure 2: Bathymetric map of Maschsee Lake with overlaid RGB imagery from the USV's onboard camera.
  • Figure 3: Detection of relevant areas with aquatic plants. From left to right, this figure depicts the APA index for the 6th of August 2024, the cropping to the lake boundaries, the clustering according to vegetation intensity, the clustering according to location, and the resulting areas with relevant submerged vegetation.
  • Figure 4: Vegetation map derived from SONAR bathymetry using elevation difference, based on surveys conducted on the 19th and 20th of August 2024.
  • Figure 5: 2D view of SONAR scan from an inspection area, showing the height difference in weed distribution detected by multibeam SONAR [Top] before harvesting and [Bottom] after harvesting, showing an average difference of 1.3m. Each color cluster represents a single scan from the SONAR.
  • ...and 2 more figures