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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-02356-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849388086112288768 |
|---|---|
| author | Ruiqi Yu Qingdang Li Jiewei Ji Tingting Wu Jian Mao Shun Liu Zhen Sun |
| author_facet | Ruiqi Yu Qingdang Li Jiewei Ji Tingting Wu Jian Mao Shun Liu Zhen Sun |
| author_sort | Ruiqi Yu |
| collection | DOAJ |
| description | 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 network model and employs a dynamic exploration strategy that encourages exploration in the early stage and reduces it later to accelerate convergence and improve efficiency. Simulation experiments in two scenarios of varying complexity, using an indoor simulation environment built with AirSim and UE4(Unreal Engine 4), show that in the simpler scenario, the average cumulative reward increased by 22.88%, the maximum reward increased by 101.56%, the average safe flight distance increased by 23.17%, and the maximum safe flight distance by 105.62%. In the more complex scenario, the average cumulative reward increased by 2.66%, the maximum reward increased by 88.77%, the average safe flight distance increased by 2.05%, and the maximum safe flight distance by 84.68%. |
| format | Article |
| id | doaj-art-66b2ee2b4f5c4aee86ec16e117e0fa32 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-66b2ee2b4f5c4aee86ec16e117e0fa322025-08-20T03:42:25ZengNature PortfolioScientific Reports2045-23222025-08-0115111710.1038/s41598-025-02356-6Improved double DQN with deep reinforcement learning for UAV indoor autonomous obstacle avoidanceRuiqi Yu0Qingdang Li1Jiewei Ji2Tingting Wu3Jian Mao4Shun Liu5Zhen Sun6College of Data Science, Qingdao University of Science and TechnologyCollege of Sino-German Science and Technology, Qingdao University of Science and TechnologyCollege of Data Science, Qingdao University of Science and TechnologyCollege of Data Science, Qingdao University of Science and TechnologyQingdao Central Hospital, University of Health and Rehabilitation SciencesCollege of Information Science and Technology, Qingdao University of Science and TechnologyCollege of Data Science, Qingdao University of Science and TechnologyAbstract 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 network model and employs a dynamic exploration strategy that encourages exploration in the early stage and reduces it later to accelerate convergence and improve efficiency. Simulation experiments in two scenarios of varying complexity, using an indoor simulation environment built with AirSim and UE4(Unreal Engine 4), show that in the simpler scenario, the average cumulative reward increased by 22.88%, the maximum reward increased by 101.56%, the average safe flight distance increased by 23.17%, and the maximum safe flight distance by 105.62%. In the more complex scenario, the average cumulative reward increased by 2.66%, the maximum reward increased by 88.77%, the average safe flight distance increased by 2.05%, and the maximum safe flight distance by 84.68%.https://doi.org/10.1038/s41598-025-02356-6UAVIndoor autonomous obstacle avoidanceDeep reinforcement learningDouble DQN algorithmDynamic exploration strategy |
| spellingShingle | Ruiqi Yu Qingdang Li Jiewei Ji Tingting Wu Jian Mao Shun Liu Zhen Sun Improved double DQN with deep reinforcement learning for UAV indoor autonomous obstacle avoidance Scientific Reports UAV Indoor autonomous obstacle avoidance Deep reinforcement learning Double DQN algorithm Dynamic exploration strategy |
| title | Improved double DQN with deep reinforcement learning for UAV indoor autonomous obstacle avoidance |
| title_full | Improved double DQN with deep reinforcement learning for UAV indoor autonomous obstacle avoidance |
| title_fullStr | Improved double DQN with deep reinforcement learning for UAV indoor autonomous obstacle avoidance |
| title_full_unstemmed | Improved double DQN with deep reinforcement learning for UAV indoor autonomous obstacle avoidance |
| title_short | Improved double DQN with deep reinforcement learning for UAV indoor autonomous obstacle avoidance |
| title_sort | improved double dqn with deep reinforcement learning for uav indoor autonomous obstacle avoidance |
| topic | UAV Indoor autonomous obstacle avoidance Deep reinforcement learning Double DQN algorithm Dynamic exploration strategy |
| url | https://doi.org/10.1038/s41598-025-02356-6 |
| work_keys_str_mv | AT ruiqiyu improveddoubledqnwithdeepreinforcementlearningforuavindoorautonomousobstacleavoidance AT qingdangli improveddoubledqnwithdeepreinforcementlearningforuavindoorautonomousobstacleavoidance AT jieweiji improveddoubledqnwithdeepreinforcementlearningforuavindoorautonomousobstacleavoidance AT tingtingwu improveddoubledqnwithdeepreinforcementlearningforuavindoorautonomousobstacleavoidance AT jianmao improveddoubledqnwithdeepreinforcementlearningforuavindoorautonomousobstacleavoidance AT shunliu improveddoubledqnwithdeepreinforcementlearningforuavindoorautonomousobstacleavoidance AT zhensun improveddoubledqnwithdeepreinforcementlearningforuavindoorautonomousobstacleavoidance |