Curriculum learning for adaptive multi-UAV swarm target search in obstacle environment
摘要
Previous studies have paid limited attention to the critical role of unmanned aerial vehicle(UAV)altitude variation in determining three-dimensional(3D)search efficiency.To address this gap,this paper introduces a deep reinforcement learning framework integrated with adaptive matrix optimization in multi-UAV target search.The proposed framework explicitply models altitude variations'impacts on observation range and detection accuracy.To balance flight safety,coverage,and detection precision,deep Q-network(DQN)-altitude matrix opti-mization(AMO)discretizes 3D trajectories into horizontal paths and vertical altitude decisions,effectively reducing the problem dimensionality.Furthermore,a curriculum learning approach is employed to decompose the search process into phased sub-tasks,each with customized decision rules and reward mecha-nisms.This hierarchical strategy accelerates agent learning and enhances performance in complex scenarios.Comprehensive experimental evaluations in simulated environments demon-strate that the proposed DQN-AMO outperforms benchmark methods in both robustness and generalization.
年，卷(期)：
2026, 37(4)
机标分类号：
S
S15
TP317.3
在线出版日期：
2026年9月10日
