A Path-Planning Method for UAV Swarm under Multiple Environmental Threats

To weaken or avoid the impact of dynamic threats such as wind and extreme weather on the real-time path of a UAV swarm, a path-planning method based on improved long short-term memory (LSTM) network prediction parameters was constructed. First, models were constructed for wind, static threats, and d...

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Bibliographic Details
Published in:Drones (Basel) 2024-05, Vol.8 (5), p.171
Main Authors: Fan, Xiangyu, Li, Hao, Chen, You, Dong, Danna
Format: Article
Language:English
Subjects:
Atmospheric effects
Collaboration
Control systems
Design
Drone aircraft
Drones
dynamic threat
Efficiency
Environmental aspects
extreme weather
Genetic algorithms
long short-term memory
Methods
Optimization algorithms
Parameters
path optimization method
Path planning
Planning
predicting atmospheric parameter
Real time
Swarm intelligence
Time series
unmanned aerial vehicle swarm
Unmanned aerial vehicles
Weather
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Summary:To weaken or avoid the impact of dynamic threats such as wind and extreme weather on the real-time path of a UAV swarm, a path-planning method based on improved long short-term memory (LSTM) network prediction parameters was constructed. First, models were constructed for wind, static threats, and dynamic threats during the flight of the drone. Then, it was found that atmospheric parameters are typical time series data with spatial correlation. The LSTM network was optimized and used to process time series parameters to construct a network for predicting atmospheric parameters. The state of the drone was adjusted in real time based on the prediction results to mitigate the impact of wind or avoid the threat of extreme weather. Finally, a path optimization method based on an improved LSTM network was constructed. Through simulation, it can be seen that compared to the path that does not consider atmospheric effects, the optimized path has significantly improved flightability and safety.
ISSN:2504-446X
2504-446X
DOI:10.3390/drones8050171