Funding entity Airbus Defence and Space S.A.U
To address the complexities of the SEAD mission, the SIAGEN Project is developing a scalable, graph-based task-allocation system that considers agent-task relationships and spatial constraints. Within this initiative, a graph-aware allocation framework is developed to handle heterogeneous platforms and dynamic objectives, a feasibility layer is implemented to ensure strict constraint adherence, and training and evaluation is conducted in a simulation environment to validate performance. Ultimately, a robust, high-performance task-allocation engine for large-scale UAV operations is created to enhance decision-making in uncertain environments, ensuring optimized and formally feasible plans for real-world deployment.
Layman's summary: To address SEAD missions, a graph-based UAV task-allocation system is being developed. A framework for dynamic objectives is created, a feasibility layer is implemented, and simulation testing is conducted to deliver optimized, compliant plans for real-world deployment.
Techniques employed: Reinforcement Learning, Trajectory Optimization, Splines
Airbus_AI_5
