Periodic Reporting for period 1 - COVER (COOPERATIVE AND INTELLIGENT UNMANNED AERIAL VEHICLES FOR EMERGENCY RESPONSE APPLICATIONS)
Période du rapport: 2023-01-01 au 2024-12-31
Equipped with sensors and cameras to provide real-time information, drones or unmanned aircraft can provide efficient and effective support during emergency situations. In this context, the EU-funded COVER project develops a cooperative, connected and intelligent unmanned aerial vehicle (UAV) system for emergency response applications (ERAs). It will create a context-aware and service-oriented UAV-to-everything network, as well as robust cooperative UAV sensing and computing schemes and intelligent cooperative UAV control strategies. By leveraging cutting-edge technologies, the project seeks to enhance the potential of participating researchers, promote research innovation, and contribute to European leadership in UAVs. The technologies and applications will have broad applications in various ERAs, as well as in public safety and agriculture.
This COVER project has the following scientific research and innovation (R&I) objectives (RO):
RO-1: Research and develop context-aware and service-oriented UAV-to-everything (U2X) networks with advanced and scalable communication and networking technologies for delivering mission-critical control messages and sensing data for both direct UAV cooperation and first responder communication under dynamic and challenging emergency environments.
RO-2: Research and design scalable and robust cooperative UAV sensing and computing (CSC) schemes to jointly provide a comprehensive perception of the complicated environment and situational awareness of the emergency scenarios for both UAV and emergency response applications. Particular attention will be on developing secure, privacy-preserved, and incentive-based data-sharing schemes for cooperative perception and situational awareness of UAV flying environments.
RO-3: Develop and test CSC-based intelligent cooperative UAV control schemes for representative ERAs, in order to ensure efficient UAV cooperation, safe UAV flights, and maximize emergence response with accurate situational awareness under tight UAV constraints and strict service requirements of the ERA systems.
This COVER project has the following scientific research and innovation (R&I) objectives (RO):
RO-1: Research and develop context-aware and service-oriented UAV-to-everything (U2X) networks with advanced and scalable communication and networking technologies for delivering mission-critical control messages and sensing data for both direct UAV cooperation and first responder communication under dynamic and challenging emergency environments.
RO-2: Research and design scalable and robust cooperative UAV sensing and computing (CSC) schemes to jointly provide a comprehensive perception of the complicated environment and situational awareness of the emergency scenarios for both UAV and emergency response applications. Particular attention will be on developing secure, privacy-preserved, and incentive-based data-sharing schemes for cooperative perception and situational awareness of UAV flying environments.
RO-3: Develop and test CSC-based intelligent cooperative UAV control schemes for representative ERAs, in order to ensure efficient UAV cooperation, safe UAV flights, and maximize emergence response with accurate situational awareness under tight UAV constraints and strict service requirements of the ERA systems.
The project has made significant progress in UAV-based communication, networking, sensing, and control. It has successfully developed novel communication technologies that enhance UAV-to-anything (U2X) networks, ensuring reliable delivery of both mission-critical and non-critical data. Advanced techniques, including short packet transmission for ultra-reliable low-latency communication (URLLC) and multi-radio access technologies such as 5G V2X, DSRC, and LoRa, have been employed to optimize system efficiency.
Intelligent resource scheduling and networking strategies have been implemented to improve UAV swarm coordination and spectrum access. Deep reinforcement learning-based approaches have been developed for optimizing UAV trajectories in dynamic environments, accounting for obstacles and interference. These strategies significantly enhance UAV efficiency in real-time emergency operations.
A cooperative sensing and computing framework has been designed to integrate UAV communication with environmental perception. Advanced sensor fusion techniques and machine learning models have been deployed to enhance data accuracy and security. Privacy-preserving and incentive-based data-sharing mechanisms, including blockchain-based security models and federated learning, have been proposed to ensure secure and fair information exchange.
In UAV control and emergency response applications, robust formation control strategies have been introduced, allowing UAV teams to adapt to changing conditions while maintaining stable and efficient coordination. Adaptive control algorithms have been developed for leader selection, formation restructuring, and safe navigation in dynamic environments. A distributed collision avoidance system has also been designed, enabling UAVs to cooperatively adjust trajectories while avoiding obstacles and maintaining communication reliability.
Intelligent resource scheduling and networking strategies have been implemented to improve UAV swarm coordination and spectrum access. Deep reinforcement learning-based approaches have been developed for optimizing UAV trajectories in dynamic environments, accounting for obstacles and interference. These strategies significantly enhance UAV efficiency in real-time emergency operations.
A cooperative sensing and computing framework has been designed to integrate UAV communication with environmental perception. Advanced sensor fusion techniques and machine learning models have been deployed to enhance data accuracy and security. Privacy-preserving and incentive-based data-sharing mechanisms, including blockchain-based security models and federated learning, have been proposed to ensure secure and fair information exchange.
In UAV control and emergency response applications, robust formation control strategies have been introduced, allowing UAV teams to adapt to changing conditions while maintaining stable and efficient coordination. Adaptive control algorithms have been developed for leader selection, formation restructuring, and safe navigation in dynamic environments. A distributed collision avoidance system has also been designed, enabling UAVs to cooperatively adjust trajectories while avoiding obstacles and maintaining communication reliability.
The partners have actively collaborated and conducted research activities according to the scheduled plan. The partners have published around 45 high-quality research publications in the areas of communications, networking, control, UAVs, computing, intelligent transport, and the Internet of Things.