战场环境中多无人机任务分配的快速航路预估算法

战场环境中多无人机任务分配的航路预估是一个具有多路径和实时性双重要求的路径规划问题。采用概率路标图方法对多无人机多任务的航路预估问题开展研究,将航路预估分为离线学习和在线查询两个阶段。通过将战场中威胁的影响转化为各路标间航段的风险代价,提出了基于代价变换的概率路标图方法,当战场态势发生变化时,在不需重构路标图的条件下可以通过局部航段风险代价的调整快速规划出新的预估航路。根据规划条件采取不同的采样策略,可以在规划时间和航路质量之间实现协调以满足不同的战术要求。仿真结果表明该方法是一种快速有效的航路预估方法。

Fast Route Predicting Algorithm for Multiple UAVs Task Allocation in Battlefield

The route prediction for multiple unmanned aerial vehicles(UAVs) task allocation in dynamic battlefield is a complicated path planning problem characterized by multiple paths and real time demands.Probabilistic Road Maps(PRM) method wais used to plan combinatorial predictive routes for multiple UAVs and multiple tasks in this paper.The route predicting process was split into two phases: the off-line learning phase and the on-line query phase.The paper presented Cost transform based PRM(CTPRM) algorithm,which transforms the influence of enemy threats to the risk cost of the route segments among roadmaps.When battlefield situation was changed,without reconstructing roadmaps,CTPRM could plan new predictive routes rapidly by updating the risk cost of some route segments.According to different planning condition,relevant sampling strategy could be set flexibly,so that the planning time and route quality could be coordinated to achieve tactical goal.Simulation results demonstrate the feasibility of the approach.