Improved double DQN with deep reinforcement learning for UAV indoor autonomous obstacle avoidance

Abstract Aiming at the problems of insufficient autonomous obstacle avoidance performance of UAVs in complex indoor environments, an improved Double DQN algorithm based on deep reinforcement learning is proposed. The algorithm enhances the perception and learning capabilities by optimizing the netwo...

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Autors principals:	Ruiqi Yu, Qingdang Li, Jiewei Ji, Tingting Wu, Jian Mao, Shun Liu, Zhen Sun
Format:	Artigo
Idioma:	Inglês
Publicat:	Nature Portfolio 2025-08-01
Col·lecció:	Scientific Reports
Matèries:	UAV Indoor autonomous obstacle avoidance Deep reinforcement learning Double DQN algorithm Dynamic exploration strategy
Accés en línia:	https://doi.org/10.1038/s41598-025-02356-6
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