Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss
Precise trajectory planning is crucial in terms of enabling unmanned aerial vehicles (UAVs) to execute interference avoidance and target capture actions in unknown environments and when facing intermittent target loss. To address the trajectory planning problem of UAVs in such conditions, this paper...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/12/721 |
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| author | Zhengpeng Yang Suyu Yan Chao Ming Xiaoming Wang |
| author_facet | Zhengpeng Yang Suyu Yan Chao Ming Xiaoming Wang |
| author_sort | Zhengpeng Yang |
| collection | DOAJ |
| description | Precise trajectory planning is crucial in terms of enabling unmanned aerial vehicles (UAVs) to execute interference avoidance and target capture actions in unknown environments and when facing intermittent target loss. To address the trajectory planning problem of UAVs in such conditions, this paper proposes a UAV trajectory planning system that includes a predictor and a planner. Specifically, the system employs a bidirectional Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) network algorithm with an adaptive attention mechanism (BITCN-BIGRU-AAM) to train a model that incorporates the historical motion trajectory features of the target and motion intention the inferred by a Dynamic Bayesian Network (DBN). The resulting predictions of the maneuvering target are used as terminal inputs for the planner. An improved Radial Basis Function (RBF) network is utilized in combination with an offline–online trajectory planning method for real-time obstacle avoidance trajectory planning. Additionally, considering future practical applications, the predictor and planner adopt a parallel optimization and correction algorithm structure to ensure planning accuracy and computational efficiency. Simulation results indicate that the proposed method can accurately avoid dynamic interference and precisely capture the target during tasks involving dynamic interference in unknown environments and when facing intermittent target loss, while also meeting system computational capacity requirements. |
| format | Article |
| id | doaj-art-cfa3ceba7d3240ee810f0dbd097924df |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-cfa3ceba7d3240ee810f0dbd097924df2025-08-20T02:50:59ZengMDPI AGDrones2504-446X2024-11-0181272110.3390/drones8120721Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target LossZhengpeng Yang0Suyu Yan1Chao Ming2Xiaoming Wang3College of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaCollege of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaCollege of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaCollege of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaPrecise trajectory planning is crucial in terms of enabling unmanned aerial vehicles (UAVs) to execute interference avoidance and target capture actions in unknown environments and when facing intermittent target loss. To address the trajectory planning problem of UAVs in such conditions, this paper proposes a UAV trajectory planning system that includes a predictor and a planner. Specifically, the system employs a bidirectional Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) network algorithm with an adaptive attention mechanism (BITCN-BIGRU-AAM) to train a model that incorporates the historical motion trajectory features of the target and motion intention the inferred by a Dynamic Bayesian Network (DBN). The resulting predictions of the maneuvering target are used as terminal inputs for the planner. An improved Radial Basis Function (RBF) network is utilized in combination with an offline–online trajectory planning method for real-time obstacle avoidance trajectory planning. Additionally, considering future practical applications, the predictor and planner adopt a parallel optimization and correction algorithm structure to ensure planning accuracy and computational efficiency. Simulation results indicate that the proposed method can accurately avoid dynamic interference and precisely capture the target during tasks involving dynamic interference in unknown environments and when facing intermittent target loss, while also meeting system computational capacity requirements.https://www.mdpi.com/2504-446X/8/12/721UAVtrajectory predictiondynamic trajectory planningunknown environmentintermittent target loss |
| spellingShingle | Zhengpeng Yang Suyu Yan Chao Ming Xiaoming Wang Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss Drones UAV trajectory prediction dynamic trajectory planning unknown environment intermittent target loss |
| title | Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss |
| title_full | Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss |
| title_fullStr | Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss |
| title_full_unstemmed | Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss |
| title_short | Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss |
| title_sort | intelligent dynamic trajectory planning of uavs addressing unknown environments and intermittent target loss |
| topic | UAV trajectory prediction dynamic trajectory planning unknown environment intermittent target loss |
| url | https://www.mdpi.com/2504-446X/8/12/721 |
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