Optimizing Spatial State Representation in Reinforcement Learning for Coverage Path Planning in UAV Search Missions

To enhance path planning efficiency in unmanned aerial vehicle (UAV) search missions in complex environments, this paper proposes a coverage path planning (CPP) algorithm for a UAV that integrates the deep Q-network (DQN) with the A* algorithm (DQN-A*). In the proposed DQN-A* algorithm, a dual-drive...

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Autors principals:	Hu Yuan, Shengkai Yan, Zhuzhi Liu, Suli Wang, Qiang Wang, Gaocheng Chen
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2026-06-01
Col·lecció:	Drones
Matèries:	coverage path planning (CPP) deep reinforcement learning (DRL) search missions unmanned aerial vehicle (UAV)
Accés en línia:	https://www.mdpi.com/2504-446X/10/6/442
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