Research On Path Planning And Cooperative Target Assignment For Multiple UAV

Multiple Unmanned Aerial Vehicles(Multi-UAVs)cooperative operations have gradually become the form of UAVs in modern battlefield.There are two kinds of challenges about the Multi-UAVs such as Simultaneous aggregation and target assignment.Rendezvous of Multi-UAVs is one of important cooperative control problems for UAVs,and has great significance for air-refueling,attacking,searching and tracking.Multi-UAVs cooperative target assignment(MUCTA)is a kind of complex and Multi-constrained assignment optimization problem.In this paper,the path planning of Multi-UAVs for simultaneous aggregation and target assignment based on real battlefield environment are studied.The works are as follows:(1)For path planning based on rendezvous of multiple UAV,the serial strategy based on directional A* algorithm is designed for the problem in the paper.Firstly,The directional A* algorithm is improved to make route smooth and feasible in the step of pre-planning.Specifically,the algorithm reduces expansion area of node based on constraints of the maximum pitch and yaw angles.Secondly,the Serial algorithms in the step of re-planning is discussed to make the UAVs near the same target.there are three parts: variable step size and multiple search node,three-dimensional circle maneuver,and virtual threats and so on.Finally,the numerical simulations are presented to verify the effectiveness of the method.(2)For the problem of Multi-UAVs cooperative target assignment,firstly,the UAV allocation order influences total revenue of strike task in the real and dynamic battlefield environment,so the optimization model of UAVs cooperative target assignment with allocation order is established.And there are two kinds of costs of flight path length and task in the constraint conditions.Secondly,the genetic algorithm of MUCTA is designed.Gene encoding is designed according to characteristics of the model;the crossover operator shows the change of allocation order directly based on state transition;the mutation operator of single mutate shows the change of relationship between UAVs and targets;the selection operator includes Optimal individual selection and roulette.Finally,simulation results verify the effectiveness of the algorithm.