In the project we integrate advanced sensors, such as multi-wavelength fluorometers and underwater laser cameras, to state-of-the-art robots and develop machine-learning algorithm for automatic identification of marine features and marine organisms. Monitoring and management of marine environments and infrastructures is a challenging and costly activity due to the considerable heterogeneity of the oceans, which display large changes in water conditions (e.g. current, visibility), water quality (e.g., nutrient loads, carbon content) and marine life, at all scales from few meters to thousand of kilometers. There is a need to achieve cost effective solutions in infrastructure inspections as well as water sampling, analyses and observations performed directly in the ocean (in-situ) and at temporal and spatial scales relevant for the processes investigated. This requires solving specific challenges since: a) in-situ sensors and analytical probes are still limited; b) collection of long-time series is problematic; c) infrequent sampling cannot capture transients and episodic events.
The project is divided in three workpackages.
- D1.1 Purchase, assembly and testing an underwater robotic unit (M1.1).
- D1.2 Integration of the camera sensor (M1.2).
- D1.3 Integration of the fluorescence sensor (M1.3).
- D1.4 Design of an integrated mechanical sampling device (M1.4).
WP1 Integration of sensors
- D2.1 Data collection
- D2.2 Pattern identification algorithm.
- D2.3 Cognition and action control (M2.3).
WP2 Cognition and target sampling algorithm
- E3.1 Course integration (M3.1).
- E3.2 BSc and MSc projects (M3.2).
- I3.3 Demonstration to stakeholders (M3.3).