基于改进人工蜂群算法的多无人机灭火任务规划

Multi-UAV fire fighting mission planning based on improved artificial bee colony algorithm

针对当前多无人机通过投放灭火弹对多处山火进行灭火救援任务规划中,因各点火情的时变而导致规划效率低下的问题,提出了一种多无人机对多火点火场灭火救援任务规划方法。首先,建立了目标价值随时间变化的多无人机任务规划模型,可实现根据指挥者的决策意图来进行合理救援。然后,根据模型提出了一种基于整数编码的改进人工蜂群算法,利用该算法求解各无人机的救援序列可以提高无人机救援效率。最后根据救援序列及预先规划的可飞路径生成各无人机的救援路径。仿真结果表明,在相同任务规模下,该方法的寻优效率较高,寻优时间仅为现有方法的15.2%;在总价值提高的情况下,完成任务的时间与现有方法相比平均缩短了4.9%。

In order to solve the problem that the planning efficiency is low due to the time-varying situation of each fire in the fire fighting and rescue mission planning of multiple mountain fires by dropping fire extinguishing bombs by multiple UAVs,a mission planning method of fire fighting and rescue of multi-UAV for multiple fires is proposed.First of all,a multi-UAV mission planning model in which the target value varies with time is established,which can be used to carry out reasonable rescue according to the commander's decision.Then,according to the model,an improved artificial bee colony algorithm based on integer coding is proposed.Using the algorithm to solve the rescue sequence of UAV can improve the rescue efficiency of UAV.Finally,the rescue path of each UAV is generated according to the rescue sequence and the pre-planned flying path.The simulation results show that,under the same task scale,the optimization efficiency of the proposed method is high,and the optimization time is only 15.2%of that of the existing methods;in the case of an increase in the total value,the average time to complete the task is reduced by 4.9%compared with the existing methods.