Academic Journal
Multi-Head Attention DQN and Dynamic Priority for Path Planning of Unmanned Aerial Vehicles Oriented to Penetration.
| Τίτλος: | Multi-Head Attention DQN and Dynamic Priority for Path Planning of Unmanned Aerial Vehicles Oriented to Penetration. |
|---|---|
| Συγγραφείς: | Cheng, Liuyu, Shang, Wei |
| Πηγή: | Electronics (2079-9292); Jan2026, Vol. 15 Issue 1, p167, 35p |
| Θεματικοί όροι: | Robotic path planning, Reinforcement learning, Decision making, Drone aircraft |
| Περίληψη: | Unmanned aerial vehicle (UAV) penetration missions in hostile environments face significant challenges due to dense threat coverage, dynamic defense systems, and the need for real-time decision-making under uncertainty. Traditional path planning methods suffer from computational intractability in high-dimensional spaces, while existing deep reinforcement learning approaches lack efficient feature extraction and sample utilization mechanisms for threat-dense scenarios. To address these limitations, this paper presents an enhanced Deep Q-Network (DQN) framework integrating multi-head attention mechanisms with dynamic priority experience replay for autonomous UAV path planning. The proposed architecture employs four specialized attention heads operating in parallel to extract proximity, danger, alignment, and threat density features, enabling selective focus on critical environmental aspects. A dynamic priority mechanism adaptively adjusts sampling strategies during training, prioritizing informative experiences in early exploration while maintaining balanced learning in later stages. Experimental results demonstrate that the proposed method achieves 94.3% mission success rate in complex penetration scenarios, representing 7.1–17.5% improvement over state-of-the-art baselines with 2.2× faster convergence. The approach shows superior robustness in high-threat environments and meets real-time operational requirements with 18.3 ms inference latency, demonstrating its practical viability for autonomous UAV penetration missions. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-Head Attention DQN and Dynamic Priority for Path Planning of Unmanned Aerial Vehicles Oriented to Penetration. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cheng%2C+Liuyu%22">Cheng, Liuyu</searchLink><br /><searchLink fieldCode="AR" term="%22Shang%2C+Wei%22">Shang, Wei</searchLink> – Name: TitleSource Label: Source Group: Src Data: Electronics (2079-9292); Jan2026, Vol. 15 Issue 1, p167, 35p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Unmanned aerial vehicle (UAV) penetration missions in hostile environments face significant challenges due to dense threat coverage, dynamic defense systems, and the need for real-time decision-making under uncertainty. Traditional path planning methods suffer from computational intractability in high-dimensional spaces, while existing deep reinforcement learning approaches lack efficient feature extraction and sample utilization mechanisms for threat-dense scenarios. To address these limitations, this paper presents an enhanced Deep Q-Network (DQN) framework integrating multi-head attention mechanisms with dynamic priority experience replay for autonomous UAV path planning. The proposed architecture employs four specialized attention heads operating in parallel to extract proximity, danger, alignment, and threat density features, enabling selective focus on critical environmental aspects. A dynamic priority mechanism adaptively adjusts sampling strategies during training, prioritizing informative experiences in early exploration while maintaining balanced learning in later stages. Experimental results demonstrate that the proposed method achieves 94.3% mission success rate in complex penetration scenarios, representing 7.1–17.5% improvement over state-of-the-art baselines with 2.2× faster convergence. The approach shows superior robustness in high-threat environments and meets real-time operational requirements with 18.3 ms inference latency, demonstrating its practical viability for autonomous UAV penetration missions. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/electronics15010167 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 35 StartPage: 167 Subjects: – SubjectFull: Robotic path planning Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Decision making Type: general – SubjectFull: Drone aircraft Type: general Titles: – TitleFull: Multi-Head Attention DQN and Dynamic Priority for Path Planning of Unmanned Aerial Vehicles Oriented to Penetration. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cheng, Liuyu – PersonEntity: Name: NameFull: Shang, Wei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20799292 Numbering: – Type: volume Value: 15 – Type: issue Value: 1 Titles: – TitleFull: Electronics (2079-9292) Type: main |
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