无人作战飞机侦察监视航路规划研究

Reconnaissance and surveillance combat air path planning for unmanned vehicles

侦察监视航路规划是无人作战飞机（unmanned combat aerial vechicles，UCAV）任务规划系统的一项重要研究内容，其目的是找到一条从起始点出发并最终回到起始点，在某种性能指标下最优的飞行路径。本文提出了一种航路规划的遗传粒子群优化算法，设计了算法流程，给出了规划方法，解决了大范围内单／多机的侦察监视航路规划问题，仿真结果证明了该算法的有效性。

Reconnaissance surveillance path planning is an important research field of the mission planning system in unmanned combat aerial vehicles（UCAV）. Finding an optimal flying path in accordance with some criterion from the start position and back it in the end is the main purpose. A large-scale discrete genetic particle swarm optimization path planning method for one or more UCAVs was proposed here. The arithmetic flow and planning approach was presented. The algorithm was implemented, and the results demonstrate its validity.