Exécution de notre outil de synthèse sur un nouvel article

Résumé : This paper proposes an artificial intelligence (AI) driven mission capability aware unmanned aerial vehicle (UAV) swarm coordination framework for autonomous post disaster search and rescue. An integrated sensing and communication enabled master high altitude platform station senses the disaster environment, detects victims and hazards, and constructs adaptive rescue regions based on spatially varying mission demands. A mission capability requirement (MCR) quantifies regional capability demand, while a normalized mission capability score (MCS) characterizes the real time capability of individual UAVs. A capability-aware assignment mechanism matches UAV capabilities to regional requirements while minimizing deployment distance. An MCS based reallocation mechanism dynamically transfers UAVs from capability surplus to capability deficient regions while preserving donor sufficiency. A hierarchical deep reinforcement learning framework with proximal policy optimization performs high-level region selection, while MCR MCS matching enables feasible UAV level reallocation. Finally, joint trajectory optimization considers travel time, distance, propulsion energy, operational capability, obstacle avoidance, and inter UAV collision avoidance. Theoretical analysis and simulations demonstrate improved mission capability satisfaction, resource efficiency, and disaster response performance