基于神经网络和遗传算法的多无人机协同覆盖侦察

Chang Liu, Wen-jun Xie, Peng Zhang, Qing Guo, Doujian Ding
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引用次数: 5

摘要

针对多无人机协同覆盖侦察任务规划问题,提出了一种神经网络与遗传算法相结合的规划方法。首先,将多架无人机之间的相对位置关系、每架无人机与目标区域边界的位置关系以及每架无人机的运动性能作为神经网络的输入,输出为每架无人机的粗糙路径;然后,利用遗传算法对神经网络的权值和阈值进行优化,求解多无人机协同区域侦察的最优路径;仿真结果表明,该方法不仅能使无人机自主学习侦察规则,还能规划各无人机的协同侦察路径,实现对目标区域的有效覆盖,具有良好的侦察效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-UAVs Cooperative Coverage Reconnaissance with Neural Network and Genetic Algorithm
Aiming at the problem of multi-UAVs cooperative coverage reconnaissance mission planning, a planning method combining neural network and genetic algorithm is proposed. Firstly, the relative position relationship between multiple UAVs, the position relationship between each UAV and the boundary of the target area and the motion performance of each UAV are taken as inputs of the neural network, and the output is rough path of each UAV. Then, the weights and thresholds of neural network are optimized by using genetic algorithm, and the optimal paths of multi-UAVs cooperative regional reconnaissance is solved. The simulation results show that the method can not only enable UAVs to learn reconnaissance rules autonomously, but also plan the cooperative reconnaissance paths of each UAV, achieve effective coverage of the target area, and have good reconnaissance efficiency.
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