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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מידע ביבליוגרפי

Principais autores:	Hu Yuan, Shengkai Yan, Zhuzhi Liu, Suli Wang, Qiang Wang, Gaocheng Chen
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	MDPI AG 2026-06-01
סדרה:	Drones
נושאים:	coverage path planning (CPP) deep reinforcement learning (DRL) search missions unmanned aerial vehicle (UAV)
גישה מקוונת:	https://www.mdpi.com/2504-446X/10/6/442
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