ISME activity on the use of autonomous surface and underwater vehicles for acoustic surveys at sea

The paper presents an overview of the recent and ongoing research activities of the Italian Interuniversity Center on Integrated Systems for the Marine Environment (ISME) in the field of geotechnical seismic surveying. Such activities, performed in the framework of the H2020 European project WiMUST, include the development of technologies and algorithms for Autonomous Surface Crafts and Autonomous Underwater Vehicles to perform geotechnical seismic surveying by means of a team of robots towing streamers equipped with acoustic sensors

ISME trends: Autonomous surface and underwater vehicles for geoseismic survey

The paper presents the recent and ongoing activities of the Italian Center named ISME on the use of Autonomous Surface Crafts (ASCs) and Autonomous Underwater Vehicles (AUVs) for geoseismic survey. In particular, the paper will focus on the technologies and the algorithms developed in the framework of the H2020 European Project WiMUST

Implementation of a Passive Acoustic Barrier for Surveillance

Dias, A. R., Santos, N. P., & Lobo, V. (2023). Implementation of a Passive Acoustic Barrier for Surveillance. In OCEANS 2023 - Limerick (pp. 1-6). IEEE Computer Society. https://doi.org/10.1109/OCEANSLimerick52467.2023.10244682With the end of the cold war, the interest in underwater warfare decreased dramatically. However, recent developments have brought underwater warfare back to center stage. Anti-submarine warfare is always one of the major concerns of a navy since it is difficult to detect an enemy submarine in the vast ocean. Conjugating the recent developments in unmanned vehicles and active and passive acoustic surveillance, we can perform better data fusion and increase our knowledge about the events occurring in our waters. The processed data originating from acoustic surveillance can potentially be an essential source of naval intelligence. An acoustic barrier can perform this detection with success. Still, these systems require highly qualified personnel to operate, present a costly infrastructure, and are hard to implement and maintain. Data fusion from multiple sources, and even from low-cost sensors with noisy measures, are a promising solution, especially if resource optimization is a priority. The implementation described in this paper is intended to be proof of concept of a low-cost implementation in shallow waters that can be easily expanded and evolved to different scenarios. The preliminary results obtained confirm that this is a viable solution.authorsversionpublishe

The GLINT10 field trial results

Autonomous underwater vehicles (AUVs) have gained more interest in recent years for military as well as civilian applications. One potential application of AUVs is for the purpose of undersea surveillance. As research into undersea surveillance using AUVs progresses, issues arise as to how an AUV acquires, acts on, and shares information about the undersea battle space. These issues naturally touch on aspects of vehicle autonomy and underwater communications, and need to be resolved through a spiral development process that includes at sea experimentation. This paper presents a recent AUV implementation for active anti-submarine warfare tested at sea in the summer of 2010. On-board signal processing capabilities and an adaptive behavior are discussed in both a simulation and experimental context. The implications for underwater surveillance using AUVs are discussed

The Widely scalable Mobile Underwater Sonar Technology (WiMUST) H2020 project: first year status

The Widely scalable Mobile Underwater Sonar Technology (WiMUST) project aims at developing a system of cooperative Autonomous Underwater Vehicles (AUVs) for geotechnical surveying and geophysical exploration. The paper reports about the first year activities and it gives an overview of the main objectives and methods. Results relative to distributed sensor array, cooperative control, mission planning, communications and preliminary experiments are summarized

Efficient artifacts filter by density-based clustering in long term 3D whale passive acoustic monitoring with five hydrophones fixed under an Autonomous Surface Vehicle

International audiencePassive underwater acoustics allows for the monitoring of the echolocation clicks of cetaceans. Static hydrophone arrays monitor from a fixed location, however, they cannot track animals over long distances. More flexibility can be achieved by mounting hydrophones on a mobile structure. In this paper, we present the design of a small non-uniform array of five hydrophones mounted directly under the Autonomous Surface Vehicle (ASV) Sphyrna (also called an Autonomous Laboratory Vehicle) built by SeaProven in France. This configuration is made challenging by the 40cm aperture of the hydrophone array, extending only two meters below the surface and above the thermocline, thus presenting various artifacts. The array, fixed under the keel of the drone, is numerically stabilized in yaw and roll using the drone's Motion Processing Unit (MPU). To increase the accuracy of the 3D tracking computed from a four hour recording of a Sperm Whale diving several kilometers away, we propose an efficient joint filtering of the clicks in the Time Delay of Arrival (TDoA) space. We show how the DBSCAN algorithm efficiently removes any outlier detection among the thousands of transients, and yields to coherent high definition 3D tracks

Underwater Localization in a Confined Space Using Acoustic Positioning and Machine Learning

Localization is a critical step in any navigation system. Through localization, the vehicle can estimate its position in the surrounding environment and plan how to reach its goal without any collision. This thesis focuses on underwater source localization, using sound signals for position estimation. We propose a novel underwater localization method based on machine learning techniques in which source position is directly estimated from collected acoustic data. The position of the sound source is estimated by training Random Forest (RF), Support Vector Machine (SVM), Feedforward Neural Network (FNN), and Convolutional Neural Network (CNN). To train these data-driven methods, data are collected inside a confined test tank with dimensions of 6m x 4.5m x 1.7m. The transmission unit, which includes Xilinx LX45 FPGA and transducer, generates acoustic signal. The receiver unit collects and prepares propagated sound signals and transmit them to a computer. It consists of 4 hydrophones, Red Pitay analog front-end board, and NI 9234 data acquisition board. We used MATLAB 2018 to extract pitch, Mel-Frequency Cepstrum Coefficients (MFCC), and spectrogram from the sound signals. These features are used by MATLAB Toolboxes to train RF, SVM, FNN, and CNN. Experimental results show that CNN archives 4% of Mean Absolute Percentage Error (MAPE) in the test tank. The finding of this research can pave the way for Autonomous Underwater Vehicle (AUV) and Remotely Operated Vehicle (ROV) navigation in underwater open spaces

Overview and first year progress of the Widely scalable Mobile Underwater Sonar Technology H2020 project

open20siPubblicazione su rivista di contributo a Convegno -10th IFAC Conference on Control Applications in Marine Systems (CAMS2016)The Widely scalable Mobile Underwater Sonar Technology (WiMUST) project is an H2020 Research and Innovation Action funded by the European Commission. The action's main goal is to develop robotic technologies exploiting Autonomous Underwater Vehicles (AUVs) for geotechnical surveying and geophysical exploration. The paper briefly describes the project and its state of the art after the first year of activities.openIndiveri, Giovanni; Antonelli, Gianluca; Arrichiello, Filippo; Caffaz, Andrea; Caiti, Andrea; Casalino, Giuseppe; Volpi, Nicola Catenacci; de Jong, Ivan Bielic; De Palma, Daniela; Duarte, Henrique; Gomes, Joao Pedro; Grimsdale, Jonathan; Jesus, Sergio; Kebkal, Konstantin; Kelholt, Elbert; Pascoal, Antonio; Polani, Daniel; Pollini, Lorenzo; Simetti, Enrico; Turetta, AlessioIndiveri, Giovanni; Antonelli, Gianluca; Arrichiello, Filippo; Caffaz, Andrea; Caiti, Andrea; Casalino, Giuseppe; Volpi, Nicola Catenacci; de Jong, Ivan Bielic; De Palma, Daniela; Duarte, Henrique; Gomes, Joao Pedro; Grimsdale, Jonathan; Jesus, Sergio; Kebkal, Konstantin; Kelholt, Elbert; Pascoal, Antonio; Polani, Daniel; Pollini, Lorenzo; Simetti, Enrico; Turetta, Alessi