The MATRAC-ACP (Adaptive Real-time monitoring with sampling automation - Harbor and coastal waters) project aims at defining innovative water monitoring protocols based on the use of Unmanned Marine Vehicles and adaptive sampling strategies. Preliminary activities on the analysis of existing regu- lations for monitoring of port waters and operation of unmanned vehicles at sea, as well as of state-of-the-art sampling methods and sensor packages are presented in this paper. Moreover, new robotic-based procedures for monitoring of port water are being drafted, together with the design of adaptive guidance strategies for water sampling, innovative tools for sediment sampling, and adaptation of prototype robotic vehicles.

Robotics and adaptive sampling techniques for harbor waters monitoring: the MATRAC-ACP project

Massimo Caccia;Roberta Ferretti;Angelo Odetti;Gabriele Bruzzone;Michela Spagnuolo;Michela Mortara;Serena Berretta;Daniela Cabiddu;Simone Pittaluga;
2019

Abstract

The MATRAC-ACP (Adaptive Real-time monitoring with sampling automation - Harbor and coastal waters) project aims at defining innovative water monitoring protocols based on the use of Unmanned Marine Vehicles and adaptive sampling strategies. Preliminary activities on the analysis of existing regu- lations for monitoring of port waters and operation of unmanned vehicles at sea, as well as of state-of-the-art sampling methods and sensor packages are presented in this paper. Moreover, new robotic-based procedures for monitoring of port water are being drafted, together with the design of adaptive guidance strategies for water sampling, innovative tools for sediment sampling, and adaptation of prototype robotic vehicles.
2019
Istituto di Matematica Applicata e Tecnologie Informatiche - IMATI -
Istituto di iNgegneria del Mare - INM (ex INSEAN)
978-1-7281-1450-7
Unmanned underwater vehicles
Environmental monitoring
Marine technology
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/393993
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