Nowadays, AUVs are state-of-the-art technologies for seabed inspection of an area of interest to generate optical/ acoustic mosaics, gather representative bathymetric data, or search for objects of interest. As a matter of fact, typically, inspection surveys are designed by skilled operators using a lawnmower or zig-zag patterns. Nevertheless, the outcomes are not predictable since the performance of exteroceptive sensors depends on environmental conditions, seafloor, and targets composition and shape. Thus, in the last years, sensor-driven planning strategies that actively consider the sensor acquisitions as feedback, avoiding multiple attempts, were proposed. This paper presents a sensor-driven planning solution based on a Randomized Model Predictive Control approach. The developed system simulates multiple AUV possible trajectories and selects the one that better steers the AUV toward non-explored regions. Moreover, a novel method based on the Kernel Density Estimation technique for fast evaluating the AUV trajectories is proposed. Realistic simulations are presented to validate the hereby proposed strategy.

Randomized MPC for view planning in AUV seabed inspections / Zacchini L.; Franchi M.; Bucci A.; Secciani N.; Ridolfi A.. - ELETTRONICO. - 2021-September:(2021), pp. 1-6. (Intervento presentato al convegno OCEANS 2021: San Diego – Porto tenutosi a San Diego, USA nel 20-23 settembre 2021) [10.23919/OCEANS44145.2021.9705931].

Randomized MPC for view planning in AUV seabed inspections

Zacchini L.
;
Bucci A.;Secciani N.;Ridolfi A.
2021

Abstract

Nowadays, AUVs are state-of-the-art technologies for seabed inspection of an area of interest to generate optical/ acoustic mosaics, gather representative bathymetric data, or search for objects of interest. As a matter of fact, typically, inspection surveys are designed by skilled operators using a lawnmower or zig-zag patterns. Nevertheless, the outcomes are not predictable since the performance of exteroceptive sensors depends on environmental conditions, seafloor, and targets composition and shape. Thus, in the last years, sensor-driven planning strategies that actively consider the sensor acquisitions as feedback, avoiding multiple attempts, were proposed. This paper presents a sensor-driven planning solution based on a Randomized Model Predictive Control approach. The developed system simulates multiple AUV possible trajectories and selects the one that better steers the AUV toward non-explored regions. Moreover, a novel method based on the Kernel Density Estimation technique for fast evaluating the AUV trajectories is proposed. Realistic simulations are presented to validate the hereby proposed strategy.
2021
Oceans Conference Record (IEEE) - OCEANS 2021: San Diego – Porto
OCEANS 2021: San Diego – Porto
San Diego, USA
20-23 settembre 2021
Goal 9: Industry, Innovation, and Infrastructure
Zacchini L.; Franchi M.; Bucci A.; Secciani N.; Ridolfi A.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1280626
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