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SafeNav

Financiado

Cerrado

Safe Navigation

SafeNav’s ambition is to develop and test a highly innovative digital collision prevention solution that will significantly reduce the probability of collisions, impact damage, grounding, and contribute to safer navigation by a) f... ver más

31/08/2025

Offshore Monitorin...

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto

O.M. OFFSHORE MONITORING LIMITED No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-09-01

HORIZON-CL5-2022-D6-01-07: More resilient aircraft and increas... ExpectedOutcome:Project results are expected to contribute to two or more of the following expected...

I+D +1

Cerrada hace 3 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

Participantes

Offshore Monitoring

Lider

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Duración del proyecto: 35 meses Fecha Inicio: 2022-09-01
Fecha Fin: 2025-08-31

Líder del proyecto

O.M. OFFSHORE MONITORING LIMITED No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 6M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto SafeNav’s ambition is to develop and test a highly innovative digital collision prevention solution that will significantly reduce the probability of collisions, impact damage, grounding, and contribute to safer navigation by a) faster reliable real-time detection of a variety of obstacles (other vessels, fixed installations, submerged/semi-submerged objects, and marine mammals) in the marine environment, using data from state-of-the-art sensors and other relevant sources, and b) effective visual representation of the multi-source data to the navigators for quick COLREG-based decision-making support. To this end, SafeNav unites 10 key partners from the maritime industry and academia, including renowned SMEs, R&D institutes and universities to address the ‘Navigational Accidents’ aspect of the work programme . We will design collision avoidance algorithms built on multi-sensory data input from propriety (LADARTM sensor suite) and off-the-shelf sensors already installed on vessels, extensive statistics of navigational accidents, and other sources (AIS and route exchange services) to create a holistic decision support system (DSS). Processed information from the automatic DSS will feed into SafeNav collision-avoidance algorithms and generate real-time COLREGs-compliant suggestions for the navigator when an obstacle is detected. This reduces pressure on navigators onboard, providing them with efficient decision-making aid and access to visual navigation data on a single graphical user-interface. Sensors will also be used for container tracking, and mathematical models will predict container drift trajectory, transmitting collected data to a SafeNav Navigational Hazard Database available to nearby vessels/stakeholders, facilitating the recovery of lost containers. Moreover, we propose to prevent vessel collisions with cetaceans with optimal-tuned pingers to alert them of an approaching vessels.

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