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.
Figures & tables
Fig. 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.
Fig. 2: Bathymetric map of Maschsee Lake with overlaid RGB imagery from the USV ’s onboard camera.
Fig. 3: Detection of relevant areas with aquatic plants. From left to right, this figure depicts the APA index for the 6 th 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.
Fig. 4: Vegetation map derived from SONAR bathymetry using elevation difference, based on surveys conducted on the 19 th and 20 th of August 2024.
Fig. 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.
Fig. 6: Submerged boat launch rail [ a ] Camera image above water [ b ] Multibeam SONAR 3D point cloud data [ c ] SONAR Backscatter data, where the red line indicates the trajectory of the USV during data acquisition.
Fig. 7: Underwater image captured with a GoPro camera showing SAV in Maschsee Lake, used for qualitative validation of SONAR-based weed detection.
Marine litter is a growing threat to the underwater ecosystem, driving demand for autonomous survey methods that can locate debris efficiently over large areas. Existing survey methods typically follow predefined paths or operate with a single sensing modality, typically a camera (with image quality suffering in poor-visibility conditions) or sonar (usually noisy and low-resolution). We present an active mapping framework in which a forward-looking sonar and a camera both feed into a shared Bayesian occupancy map, and an optimization problem is solved at each step to decide on the next best view. Candidate viewpoints are scored by a two-term utility that balances exploration of uncertain regions via voxel entropy against exploitation of likely objects. Each sensor is characterized by range- and bearing-dependent detection and false-alarm probability tables determined from data. We evaluate the approach in a realistic underwater simulator, demonstrating that active mapping finds objects faster than a lawnmower coverage pattern, and that the dual-sensor approach works better than using either of the individual sensors.
David Rete, Patrick Boros, Lucian Busoniu
Department of Automation Technical University of Cluj-Napoca Romania
Accurate seabed mapping is essential for habitat monitoring and infrastructure inspection. In turbid, shallow coastal waters, such as shellfish aquaculture farms, the effectiveness of traditional optical methods is limited. Autonomous surface vehicles (ASVs) equipped with forward-looking sonar (FLS) offer a promising alternative. However, existing sonar-based systems face challenges in achieving fine resolution mapping over long trajectories due to low-resolution positioning measurements and accumulated drift over long trajectories. In this paper, we present a drift-resilient seabed mapping framework that integrates local FLS frame alignment using the Fourier-Mellin transform (FMT) with global trajectory optimization based on an extended Kalman filter (EKF) that fuses global positioning system (GPS), inertial measurement unit (IMU), and compass data. A variance-based image blending strategy is used to further reduce visual artifacts in overlapping regions. Field trials on a structured oyster farm site show that our framework helps reduce drift in RMSE by 9.5% relative to the FMT-only baseline. This framework also enables sub-meter reconstruction accuracy and preservation of high-resolution textures needed for oyster inventory estimation within the mapped areas.
Yisheng Zhang, Michael Xu, Alan Williams +3
University of Maryland, College Park, MD 20742, USA · University of Maryland Center for Environmental Science, Cambridge, MD 21613, USA · Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
High-resolution seafloor mapping necessitates stable and precise positioning for underwater robots. This paper introduces a novel mathematical model for SeaVis remotely operated towed vehicles (ROTVs) and develops a gain-scheduled linear-quadratic regulator (LQR) for robust depth and attitude control. We validate the approach in a high-fidelity simulation, benchmarking the LQR against a conventional PID controller over a challenging seabed profile. The presented results demonstrate the LQR's superior performance, with significantly enhanced robustness to disturbances, greater control efficiency, and substantially reduced flap actuation. The gain scheduling also confirms the controller's effectiveness across the full operational velocity range. The complete simulation environment and controller are open-sourced.
Abdelhakim Amer, Aske Alstrup, Frederik Rasmussen +3
Artificial Intelligence in Robotics Laboratory (AiR Lab), Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus C, Denmark · EIVA a/s, 8660 Skanderborg, Denmark · Automatic Control Group (RAT), Department of Electrical Engineering and Information Technology, Paderborn University, Paderborn, Germany