| Download | - View final version: Decentralized UAV swarm control: a multi-layered architecture for integrated flight mode management and dynamic target interception (PDF, 6.8 MiB)
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| DOI | Resolve DOI: https://doi.org/10.3390/drones8080350 |
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| Author | Search for: Xia, BingzeORCID identifier: https://orcid.org/0009-0007-8121-8026; Search for: Mantegh, Iraj1ORCID identifier: https://orcid.org/0000-0002-9826-1692; Search for: Xie, WenfangORCID identifier: https://orcid.org/0000-0003-2449-6306 |
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| Affiliation | - National Research Council of Canada. Aerospace
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| Funder | Search for: National Research Council of Canada; Search for: Natural Sciences and Engineering Research Council of Canada |
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| Format | Text, Article |
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| Subject | uncrewed aerial vehicles (UAVs); multi-layered control architecture; distributed swarm network; dual flight modes; unpredictable and complex environments; multi-dynamic target interception |
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| Abstract | Uncrewed Aerial Vehicles (UAVs) are increasingly deployed across various domains due to their versatility in navigating three-dimensional spaces. The utilization of UAV swarms further enhances the efficiency of mission execution through collaborative operation and shared intelligence. This paper introduces a novel decentralized swarm control strategy for multi-UAV systems engaged in intercepting multiple dynamic targets. The proposed control framework leverages the advantages of both learning-based intelligent algorithms and rule-based control methods, facilitating complex task control in unknown environments while enabling adaptive and resilient coordination among UAV swarms. Moreover, dual flight modes are introduced to enhance mission robustness and fault tolerance, allowing UAVs to autonomously return to base in case of emergencies or upon task completion. Comprehensive simulation scenarios are designed to validate the effectiveness and scalability of the proposed control system under various conditions. Additionally, a feasibility analysis is conducted to guarantee real-world UAV implementation. The results demonstrate significant improvements in tracking performance, scheduling efficiency, and overall success rates compared to traditional methods. This research contributes to the advancement of autonomous UAV swarm coordination and specific applications in complex environments. |
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| Publication date | 2024-07-29 |
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| Publisher | MDPI |
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| Licence | |
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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 | f22a0e0e-e77d-4bc9-8dfd-aa388480cef4 |
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| Record created | 2025-04-09 |
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| Record modified | 2025-04-09 |
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