战场环境下无人车辆战术机动路径规划方法

针对不同类型威胁体存在的战场环境中无人车辆战术机动路径规划问题，提出了一种基于威胁代价地图的粒子群优化（Particle Swarm Optimization，PSO）方法。借助极坐标系中关键点的极角进行路径描述，并使用分段3次Hermite插值方法形成光滑路径，将路径规划问题转化为关键点极角的参数优化问题。针对基本PSO（BPSO）算法存在的早熟收敛和后期迭代效率低的缺陷，借鉴以群集方式生活的物种按照不同任务对种群进行分工的机制，提出了一种基于多任务子群协同的改进粒子群优化（Particle Swarm Optimization based on the Multi—tasking Subpopu—lation Cooperation，PSO-MSC）算法。借助该算法的快速收敛和全局寻优特性实现了最优路径规划。实验结果表明：该算法可以快速有效地实现战场环境下无人车辆的战术机动路径规划，且规划路径安全、平滑。

Tactical Path Planning Method for Military Unmanned Vehicle in Battlefield

A novel algorithm of improved Particle Swarm Optimization （PSO） based on threat cost map is proposed for military unmanned vehicle tactical path planning in battlefield existing different types of threats. The path is described by some key points＇ polar angle in polar coordinates, and it is smoothed by piecewise cubic Hermite interpolation method, thus the path planning is equivalent to parameter optimiza- tion of polar angles. Because Basic Particle Swarm Optimization （BPSO） is easy to fall into the local op- timum as the swarm activity of population gets worse during the evolution, an improved PSO based on the Multi-tasking Subpopulation Cooperation （PSO-MSC） is developed by introducing the idea of multi-tas- king subpopulation mechanism used by gregarious species. PSO-MSC is introduced to get the optimal path for its fast convergence and global search character. Experimental results show that a safe and smooth tacti- cal path can be found fleetly and effectively in complicated battlefield environment by the proposed method.