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随着现代战场环境的日益复杂和作战范围的不断扩大,给无人机(unmanned aerial vehicle,UAV)执行空中侦察、监视、作战等任务带来了严重挑战。为了提高UAV的作战效率和生存概率,从UAV的威胁空间建模出发,根据战场分布的威胁区域,先利用威胁回避技术快速地给出一条从起始点到目标点的粗选航路;然后在此基础上,应用粒群算法和遗传算法中交叉和变异操作相结合的思想,对粗选规划航路进行全局优化,从而找出一条能确保自身安全并威胁代价最小的最优飞行航路。仿真结果说明,该方法是有效、可行的。
With the increasing complexity of the modern battlefield environment and the continuous expansion of the combat range, serious challenges have been posed to missions such as unmanned aerial vehicles (UAVs) to carry out aerial reconnaissance, surveillance and operations. In order to improve the operational efficiency and survival probability of UAV, starting from the UAV threat space modeling, according to the threat area of the battlefield distribution, the threat avoidance technique is used to quickly give a rough route from the starting point to the target point; then Based on the idea of combination of swarm optimization and crossover and mutation operation in genetic algorithm, the optimal routing of routed routes is optimized, so as to find an optimal flight path that can ensure its own safety and minimize the threat. Simulation results show that this method is effective and feasible.