Graph Attention-based Decentralized Actor-Critic for Dual-Objective Control of Multi-UAV Swarms
This work addresses the problem of efficient and scalable control for multi-UAV systems, which is incremental as it builds on existing actor-critic and graph attention approaches.
This research tackled optimizing multi-UAV swarms for dual objectives of maximizing service coverage and extending battery lifetime by proposing a Graph Attention-based Decentralized Actor-Critic (GADC) method, achieving superior performance in benchmarking against state-of-the-art methods in both ideal and realistic environments.
This research focuses on optimizing multi-UAV systems with dual objectives: maximizing service coverage as the primary goal while extending battery lifetime as the secondary objective. We propose a Graph Attention-based Decentralized Actor-Critic (GADC) to optimize the dual objectives. The proposed approach leverages a graph attention network to process UAVs' limited local observation and reduce the dimension of the environment states. Subsequently, an actor-double-critic network is developed to manage dual policies for joint objective optimization. The proposed GADC uses a Kullback-Leibler (KL) divergence factor to balance the tradeoff between coverage performance and battery lifetime in the multi-UAV system. We assess the scalability and efficiency of GADC through comprehensive benchmarking against state-of-the-art methods, considering both theory and experimental aspects. Extensive testing in both ideal settings and NVIDIA Sionna's realistic ray tracing environment demonstrates GADC's superior performance.