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Fixed-Wing UAV Coverage Path Planning Based on Turning Span Selection
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AI
DOI：10.1109/JIOT.2025.3528077.png)
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摘要
En 中文
In agricultural production, fixed-wing UAVs are widely used for image acquisition of pest detection because of their long endurance. However, due to its characteristic of being constrained by the turning radius, it causes the long turning path and much energy consumption. Thus, this article designs an algorithm based on turning span selection (TSS) for fixed-wing UAV, which plans a coverage path with the shortest turning path as possible. First, the target region model of the farmland is established. Then, by analyzing the relationship between turning radius and turning span of fixed-wing UAV, three different turning strategies are proposed. Finally, according to the pointer network model and the actor-critic algorithm in reinforcement learning, the TSS algorithm is designed. Simulation results show that the proposed TSS algorithm can effectively plan a flight path with a shorter turning path, and has obvious performance improvement compared with the existing algorithms
Keyword:
Autonomous aerial vehicles
Turning
Path planning
Heuristic algorithms
Reinforcement learning
Crops
Optimization
Energy consumption
Shape
Internet of Things
Coverage path planning (CPP)
fixed-wing UAV
reinforcement learning
turning span
