A Hybrid Robotic Network for Maritime Situational Awareness: Results From the INFORE22 Sea Trial

In this paper we describe an integrated system for maritime situational awareness (MSA) applications, built on a cooperative autonomous robotic network, and developed in the maritime use case of the INFORE EU H2020 project. INFORE project developed an interactive, real-time, extreme-scale analytics and forecasting system for handling and analysing massive streams of data. The MSA INFORE system exploits the synergy between global view (AIS data, ESA Sentinel satellite data), which provide contextual information over a wide area, and local view produced by a sensorised hybrid robotic network. Such a network was validated during the INFORE22 sea trial, held off the coast of Portovenere, Italy, in the Gulf of La Spezia, in February-March 2022. It was composed of a thermal camera and of two autonomous underwater vehicles (AUVs) equipped with passive sonars. The robots fused their measurements, and cooperatively adapted their navigation for localising a suspicious vessel, communicating her position to the MSA INFORE platform in real-time. For controlling the robots, we developed a perception layer, based on an occupancy grid map framework, which enabled the fusion of passive sonar bearing-only measurements for target localisation. The perception layer was also the basis for making cooperative decisions to adapt the network spatial configuration for improving mission performance. In this paper we report and discuss results from the INFORE22 trial. These results are one the first demonstrations of how data fusion and cooperative autonomy can increase the performance of a complex robotic network in a passive sonar surveillance mission in a real-world scenario. The INFORE MSA systems paves the way towards the development of complex, integrated MSA systems, which exploit robotic networks, characterised by advanced cooperative autonomy capabilities, for improving the local view picture, and for promptly reacting to the evolving scenario.