Self-organizing neural network-based unmanned aerial vehicle mission planning method
A neural network and mission planning technology, applied in biological neural network models, neural architecture, two-dimensional position/channel control, etc., can solve problems such as damage, UAV failure, and model building without considering actual battlefield needs.
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[0062] The present invention will be further explained below in conjunction with the accompanying drawings.
[0063] For the convenience of description, the main variables in the algorithm are simply defined:
[0064] The task is set as the drone's attack on targets distributed in different positions. The drone's attack on a target represents a sub-task. The number of task points is m, the number of drones is n, and the number of task points is n. The set is {T 1 ,T 2 ,…,T m}, the set of drones is {U 1 , U 2 ,…,U n}, the weapon load carried by each UAV is {C u1 ,C u2 ,…,C un}, the number of weapons that need to be dropped at each mission point is {P T1 ,P T2 ,…,P Tm}, t is the current iteration number of the algorithm, t max Indicates the maximum number of iterations.
[0065] The present invention proposes a UAV mission planning method based on a self-organizing neural network. The overall flow chart of the algorithm is as follows: Figure 4 As shown in the figu...
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