用改进变异粒子算法实现突发威胁下的无人机航迹规划

Path Planning with Unexpected Threats Based on Improved Method of PSO

针对无人飞行器航迹规划问题,提出一种改进变异粒子群算法及航迹节点拓展法,有效解决了突发威胁下的航迹规划问题,并进行仿真验证。通过引入维量化活性度解决了粒子群算法搜索后期速度下降问题,通过相对坐标转换避免了采用一元多项式函数作为水平航迹丢失部分解的情况。仿真表明,利用改进的变异粒子群算法能够有效地提高搜索速度和精度,适用于突发威胁下的航迹规划问题。

Aiming at the path planning problem of Unmanned Aerial Vehicle （UAV） with unexpected threats, an improved mutation Particle Swarm Optimization （PSO）algorithm and an expanding method of flight path nodes were proposed. Simulations were carried out. The problem of descended speed in late stage of PAO search was solved by introducing acuity variety complication. Coordinates conversion was used to avoid the lost of the local solutions when using unary polynomial as the horizontal path. Simulation result showed that the improved mutation PSO can improve the search speed and precision effectively, which is applicable to UAV path planning with unexpected threats.