TY  - GEN
N2  - Navigation of Autonomous Underwater Vehicles (AUVs) remains a challenge due to the impossibility to use radio frequency signals and Global Positioning System (GPS). Navigation systems usually integrate different proprioceptive sensors to estimate the asset and the speed of the vehicle. In particular, the Doppler Velocity Log (DVL) is fundamental during the navigation to have an accurate estimate of the vehicle’s speed. This work addresses the enhancement of the navigation performance of an AUV through the development of a Deep Water Navigation Filter (DWNF). The DWNF is able to work in those scenarios where traditional navigation sensors show their limits: e.g., deep waters where DVL bottom lock cannot be achieved, or areas where the use of traditionally used static and dedicated beacons is incompatible with the mission requirements. The proposed approach exploits the concept of using a network of vehicles cooperatively supporting each other for their navigation. Several types of measurements coming from the different nodes (i.e. acoustic positioning system such as ship-mounted SSBL acoustic positioning system, USBL, range measurements from the different nodes) are fused in an Extended Kalman Filter framework with the odometry data. DWNF pushes forward the idea of using a network of robotic assets as a provider of navigation services allowing more flexible and robust operations of the deployed system.

The approach has been tested at sea during several experiments. We report here results from DWNF running successfully in real-time on the NATO STO-Centre for Maritime Research and Experimentation (CMRE) vehicles during the Dynamic Mongoose’17 experimentation off the South coast of Iceland (June-July 2017). 
AB  - Navigation of Autonomous Underwater Vehicles (AUVs) remains a challenge due to the impossibility to use radio frequency signals and Global Positioning System (GPS). Navigation systems usually integrate different proprioceptive sensors to estimate the asset and the speed of the vehicle. In particular, the Doppler Velocity Log (DVL) is fundamental during the navigation to have an accurate estimate of the vehicle’s speed. This work addresses the enhancement of the navigation performance of an AUV through the development of a Deep Water Navigation Filter (DWNF). The DWNF is able to work in those scenarios where traditional navigation sensors show their limits: e.g., deep waters where DVL bottom lock cannot be achieved, or areas where the use of traditionally used static and dedicated beacons is incompatible with the mission requirements. The proposed approach exploits the concept of using a network of vehicles cooperatively supporting each other for their navigation. Several types of measurements coming from the different nodes (i.e. acoustic positioning system such as ship-mounted SSBL acoustic positioning system, USBL, range measurements from the different nodes) are fused in an Extended Kalman Filter framework with the odometry data. DWNF pushes forward the idea of using a network of robotic assets as a provider of navigation services allowing more flexible and robust operations of the deployed system.

The approach has been tested at sea during several experiments. We report here results from DWNF running successfully in real-time on the NATO STO-Centre for Maritime Research and Experimentation (CMRE) vehicles during the Dynamic Mongoose’17 experimentation off the South coast of Iceland (June-July 2017). 
AD  - NATO Centre for Maritime Research and Experimentation La Spezia, ITALY
AD  - NATO Centre for Maritime Research and Experimentation La Spezia, ITALY
AD  - NATO Centre for Maritime Research and Experimentation La Spezia, ITALY
AD  - NATO Centre for Maritime Research and Experimentation La Spezia, ITALY
AD  - NATO Centre for Maritime Research and Experimentation La Spezia, ITALY
AD  - NATO Centre for Maritime Research and Experimentation La Spezia, ITALY
AD  - NATO Centre for Maritime Research and Experimentation La Spezia, ITALY
AD  - Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, ITALY
AD  - Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, ITALY
AD  - Dipartimento di Ingegneria dell'Informazione, Università di Pisa, Pisa, ITALY
AD  - Marine Autonomous & Robotic Systems National Oceanographic Centre (NOC) Southampton, UK
T1  - An acoustic-based approach for real-time deep-water navigation of an AUV
DA  - 2018-10-02
AU  - Tesei, A
AU  - Micheli, M
AU  - Vermeij, A
AU  - Ferri, G
AU  - Mazzi, M
AU  - Grenon, G
AU  - Morlando, L
AU  - Costanzi, R
AU  - Fenucci, D
AU  - Caiti, A
AU  - Munafò, A
L1  - https://library.imarest.org/record/7718/files/ISCSS%202018%20Paper%20033%20Tesei%20FINAL.pdf
JF  - Conference Proceedings of iSCSS
VL  - iSCSS 2018
PY  - 2018-10-02
ID  - 7718
L4  - https://library.imarest.org/record/7718/files/ISCSS%202018%20Paper%20033%20Tesei%20FINAL.pdf
KW  - Autonomous Underwater Vehicles
KW  - Deep Water navigation
KW  - Extended Kalman Filter
KW  - Data Fusion
TI  - An acoustic-based approach for real-time deep-water navigation of an AUV
Y1  - 2018-10-02
L2  - https://library.imarest.org/record/7718/files/ISCSS%202018%20Paper%20033%20Tesei%20FINAL.pdf
LK  - https://www.imarest.org/iscss
LK  - https://library.imarest.org/record/7718/files/ISCSS%202018%20Paper%20033%20Tesei%20FINAL.pdf
UR  - https://www.imarest.org/iscss
UR  - https://library.imarest.org/record/7718/files/ISCSS%202018%20Paper%20033%20Tesei%20FINAL.pdf
ER  -