Computer Networks, Journal Year: 2024, Volume and Issue: 252, P. 110656 - 110656
Published: July 17, 2024
Language: Английский
Computer Networks, Journal Year: 2024, Volume and Issue: 252, P. 110656 - 110656
Published: July 17, 2024
Language: Английский
Computer Networks, Journal Year: 2024, Volume and Issue: unknown, P. 110777 - 110777
Published: Sept. 1, 2024
Language: Английский
Citations
2Drones, Journal Year: 2024, Volume and Issue: 8(12), P. 721 - 721
Published: Nov. 29, 2024
Precise trajectory planning is crucial in terms of enabling unmanned aerial vehicles (UAVs) to execute interference avoidance and target capture actions unknown environments when facing intermittent loss. To address the problem UAVs such conditions, this paper proposes a UAV system that includes predictor planner. Specifically, employs bidirectional Temporal Convolutional Network (TCN) Gated Recurrent Unit (GRU) network algorithm with an adaptive attention mechanism (BITCN-BIGRU-AAM) train model incorporates historical motion features intention inferred by Dynamic Bayesian (DBN). The resulting predictions maneuvering are used as terminal inputs for An improved Radial Basis Function (RBF) utilized combination offline–online method real-time obstacle planning. Additionally, considering future practical applications, planner adopt parallel optimization correction structure ensure accuracy computational efficiency. Simulation results indicate proposed can accurately avoid dynamic precisely during tasks involving loss, while also meeting capacity requirements.
Language: Английский
Citations
1Computer Networks, Journal Year: 2024, Volume and Issue: 252, P. 110656 - 110656
Published: July 17, 2024
Language: Английский
Citations
0