针对动态不确定战场环境下多无人机对多区域、多目标的协同察打任务规划过程中存在的信息不确定、任务多约束及航迹强耦合的多目标优化与决策问题,结合Dubins航迹规划算法,提出了一种融合多种改进策略的灰狼优化算法(grey wolf optimization algorithm incorporating multiple improvement strategies,IMISGWO).首先,针对动态环境带来的无人机巡航速度及察打任务消失时间的不确定性,基于可信性理论建立了以最大化任务收益为指标的任务规划数学模型;其次,为实现该问题的快速求解,设计了初始解均匀分布、个体通信机制调整、动态权重更新和跳出局部最优等策略,提升算法解搜索能力;最后,构建了多无人机察打一体典型任务仿真场景,通过数字仿真以及虚实结合半实物仿真试验验证了算法的可行性和有效性.仿真结果表明:算法在求解不确定环境下耦合航迹的多无人机察打一体任务规划问题时,能够生成多机高效的任务执行序列和满足无人机飞行性能约束的飞行轨迹,且能够适用于无人机数量增加导致问题复杂度增加情形下此类问题的求解.
Methodology for Integrated Reconnaissance-Strike Mission Planning of Multi-UAV Under Uncertain Environments
In response to multiple unmanned aerial vehicles(multi-UAV)cooperative reconnaissance and strike mission planning process in dynamic and uncertain battlefield environments,characterized by information uncer-tainty,multiple constraints,and strong coupling of trajectories across multiple areas and targets,combined with Dubins trajectory planning algorithm,a grey wolf optimization algorithm incorporating multiple improvement strategies(IMISGWO)was proposed.Firstly,to address the uncertainty in UAV cruise speed and reconnaissance-strike task disappearance time caused by dynamic environments,a reconnaissance-strike integrated mission plan-ning mathematical model maximizing task benefits was established based on credibility theory;Secondly,to achieve rapid problem-solving,the strategies of uniform distribution of initial solution,adjustment of individual communication mechanism,dynamic weight updating,and jumping out of the local optimization were designed to improve the solution search capability of the algorithm;Finally,a typical simulation scenario of multi-UAV reconnaissance-strike integrated tasks was constructed,and the feasibility and effectiveness of the algorithm were verified through numerical simulation and hardware-in-loop(HIL)technique.The simulation results demon-strate that the proposed algorithm can efficiently generate multi-UAV task execution sequences and flight traject-ories that satisfy the flight performance constraints of multi-UAV when solving the multi-UAV reconnaissance and strike mission planning problem with coupled trajectories under uncertain environments,and it can be ap-plied to solving this kind of problem in situations where the complexity increases due to the growing number of multi-UAV.
multiple unmanned aerial vehicles(multi-UAV)uncertain environmentsintegrated reconnais-sance and strike missionmission planninggrey wolf optimization algorithm incorporating mul-tiple improvement strategies(IMISGWO)
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维普
