Monocular vision guided deep reinforcement learning UAV systems with representation learning perception
In recent years, numerous studies have applied deep reinforcement learning (DRL) algorithms to vision-guided unmanned aerial systems. However, DRL is not good at training deep networks in an end-to-end manner due to data inefficiency and lack of direct supervision signals. This paper provides a visu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2183828 |
| _version_ | 1797683994254376960 |
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| author | Zhihan Xue Tad Gonsalves |
| author_facet | Zhihan Xue Tad Gonsalves |
| author_sort | Zhihan Xue |
| collection | DOAJ |
| description | In recent years, numerous studies have applied deep reinforcement learning (DRL) algorithms to vision-guided unmanned aerial systems. However, DRL is not good at training deep networks in an end-to-end manner due to data inefficiency and lack of direct supervision signals. This paper provides a visual information dimension reduction scheme with representation learning as the visual perception module, which reduces the dimensions of high-dimensional visual information and retains its features related to UAV navigation. Combining such state representation learning with the DRL model can effectively reduce the complexity of the neural network required by DRL. Based on this scheme, we design three motion control models with a monocular camera as the main sensor and train them to control UAVs for obstacle avoidance tasks in a simulated environment. Experiments show that all these models achieve high obstacle avoidance ability after a certain period of training. In addition, one of them also enables the monocular vision guidance system to avoid obstacles in the blind spot of side vision. |
| first_indexed | 2024-03-12T00:23:56Z |
| format | Article |
| id | doaj.art-4592d6f7fa4c4ae18a55f36d4351472b |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:23:56Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-4592d6f7fa4c4ae18a55f36d4351472b2023-09-15T10:48:01ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.21838282183828Monocular vision guided deep reinforcement learning UAV systems with representation learning perceptionZhihan Xue0Tad Gonsalves1Sophia UniversitySophia UniversityIn recent years, numerous studies have applied deep reinforcement learning (DRL) algorithms to vision-guided unmanned aerial systems. However, DRL is not good at training deep networks in an end-to-end manner due to data inefficiency and lack of direct supervision signals. This paper provides a visual information dimension reduction scheme with representation learning as the visual perception module, which reduces the dimensions of high-dimensional visual information and retains its features related to UAV navigation. Combining such state representation learning with the DRL model can effectively reduce the complexity of the neural network required by DRL. Based on this scheme, we design three motion control models with a monocular camera as the main sensor and train them to control UAVs for obstacle avoidance tasks in a simulated environment. Experiments show that all these models achieve high obstacle avoidance ability after a certain period of training. In addition, one of them also enables the monocular vision guidance system to avoid obstacles in the blind spot of side vision.http://dx.doi.org/10.1080/09540091.2023.2183828deep reinforcement learningvision guided robotic systemmonocular visionuavvae |
| spellingShingle | Zhihan Xue Tad Gonsalves Monocular vision guided deep reinforcement learning UAV systems with representation learning perception Connection Science deep reinforcement learning vision guided robotic system monocular vision uav vae |
| title | Monocular vision guided deep reinforcement learning UAV systems with representation learning perception |
| title_full | Monocular vision guided deep reinforcement learning UAV systems with representation learning perception |
| title_fullStr | Monocular vision guided deep reinforcement learning UAV systems with representation learning perception |
| title_full_unstemmed | Monocular vision guided deep reinforcement learning UAV systems with representation learning perception |
| title_short | Monocular vision guided deep reinforcement learning UAV systems with representation learning perception |
| title_sort | monocular vision guided deep reinforcement learning uav systems with representation learning perception |
| topic | deep reinforcement learning vision guided robotic system monocular vision uav vae |
| url | http://dx.doi.org/10.1080/09540091.2023.2183828 |
| work_keys_str_mv | AT zhihanxue monocularvisionguideddeepreinforcementlearninguavsystemswithrepresentationlearningperception AT tadgonsalves monocularvisionguideddeepreinforcementlearninguavsystemswithrepresentationlearningperception |