RAWFIE (Road-, Air-, and Water- based Future Internet Experimentation) is a project funded by the European Commission (Horizon H2020 programme) under the Future Internet Research Experimentation (FIRE+) initiative that aims at providing research facilities for Internet of Things (IoT) devices. The project introduces a unique platform across the space and technology by integrating numerous test beds of unmanned vehicles for research experimentation in vehicular, aerial and maritime environments. The platform will support experimenters with smart tools to conduct and monitor experiments in the domains of IoT, networking, sensing and satellite navigation. The project that is bringing together thirteen partner organizations from eight EU countries will organize two open calls to attract researchers from academia and industry, test bed operators and unmanned vehicles manufacturers. http://www.rawfie.eu/

Phase 1:

• USV – Scanning the designated area and transmit all the visual and thermal data
• CC custom software – Recognizes and classified the possible “dangerous object”

Phase 2:

• USV – Having defined the “dangerous object” a semi-autonomous mission of tracking is enabled to USV through CC

“dangerous object” = any foreign object on the water’s surface with dimensions between 0.5m to 1m

Resources

• Testbed : HMOD
• UxV : NIRIIS USV
• Sensors : Day/Night Thermal Cameras
  
  HD Camera
• Data : Live streaming of the detected “dangerous object”
  
  Snapshots of the “dangerous object”
  
  Position of “dangerous object”

Objectives

• the familiarization with the features and the ablities of the framework

• the collection data using USV

• the post-analysis of the data, using RAWFIE platform’s tools

• the exploitation of the final integrity of data, using visualization technology

• the capitalization, branding and market research of the outcomes

State-of-the-art

• to test connectivity and compatibility with third party equipment
• to test and adopt the stack of RAWFIE’s services in a commercial product
• to manage crisis situations in the most time-effective and cost-effective way
• to adopt the developed algorithms
• object detection and classification
• object tracking