| DOI | Resolve DOI: https://doi.org/10.1109/ICUAS57906.2023.10156454 |
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| Author | Search for: Xia, Bingze; Search for: Mantegh, Iraj1; Search for: Xie, Wen-Fang |
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| Affiliation | - National Research Council of Canada. Aerospace
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| Funder | Search for: National Research Council Canada |
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| Format | Text, Article |
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| Conference | 2023 International Conference on Unmanned Aircraft Systems (ICUAS), June 6-9, 2023, Warsaw, Poland |
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| Subject | uncrewed aircraft systems; deep reinforcement learning; autonomous navigation |
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| Abstract | As Uncrewed Aircraft Systems (UAS) become more ubiquitous in urban airspace around the world, the need for reliable navigation and de-confliction technologies becomes paramount. In this paper, the authors improve the popular Deep Reinforcement Learning (RL) methods of Twin Delayed DDPG (TD3) and Proximal Policy Optimization (PPO) and propose two new integrated algorithms for de-confliction with single and multiple intruder UASs in different cases of fixed and variable altitudes. Based on the Actor-Critic method, new RL systems and reward functions are designed that enhance the training efficiency of the navigating UAS agent for the considered environment models. The simulation results show the capability of the trained agent to successfully navigate in a complex environment amid fixed and velocity obstacles. This research contributes to the development of autonomous navigation for UAS in urban airspace. |
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| Publication date | 2023-06-06 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | ae15557c-91c1-4b76-ad40-2af656fd129f |
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| Record created | 2024-07-29 |
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| Record modified | 2024-07-29 |
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