(КПІ ім. Ігоря Сікорського, 2025) Akhaladze, А.; Lisovychenko, O.
This paper addresses the problem of partially successful or failed mission execution by multiple drones operated centrally by human pilots over an unreliable control channel. We propose an artificial-intelligence-based approach to the decentralized control of a heterogeneous swarm of unmanned aerial vehicles (UAVs) under intermittent communication. Swarm heterogeneity–stemming from UAVs with diverse sensing, mobility, and endurance capabilities–complicates coordination, while communication outages demand a high degree of on-board autonomy. The method relies on reinforcement-learning techniques that enable individual UAVs to make decisions locally and to adapt to changes in the environment and in swarm composition. The approach improves the resilience, efficiency, and fault tolerance of the system, allowing the swarm to accomplish complex tasks such as reconnaissance, environmental monitoring, and search-and-rescue operations without dependence on a centralized control node. Emphasis is placed on the design of algorithms that ensure effective interaction and cooperative task execution even in the presence of partial or complete loss of inter-UAV communication or the failure of individual agents.