TrialsNet

The use case focuses on showcasing how autonomous and smart systems can perform typical ground handling operations at the APRON, such as passenger handling, in-flight catering, aircraft fuelling, potable water & aircraft toilet servicing, baggage and cargo handling, and Foreign Object Damage (FOD) prevention. This will be achieved using remotely controlled or unmanned vehicles like collaborative robots. The Digital Twins of the APRON will be accessed by VR headsets, enabling a real-time depiction of the physical world inside the virtual one. Digital Twins significantly impact optimising the operations of the staff supervising APRON, ensuring safer and incident-free operations. Operators can intervene remotely and take control of vehicles in critical situations.

Data will be collected from vehicles in the airport APRON and robots using a variety of sensors, including LIDAR and GPS, as well as images and videos from security cameras. Advanced AI techniques will be employed to analyse the data, identify patterns, and make accurate predictions, allowing for continuous monitoring and analysis of airport operations. Based on these analyses, alerts and suggestions will be generated to improve operations and enhance overall airport efficiency.

The integration of a distributed monitoring system will enable the continuous monitoring of unmanned vehicles, collaborative robots, and relevant resources. This system can collect data across the Edge and far-edge resources, and traffic profiling will be conducted to detect network anomalies and predict/prevent failures and security breaches. Automated mitigation procedures will be applied to address any issues that arise.