Research On Cooperate Mission Planning Method For The Heterogeneous Multiple Aerial Units

With the continuous development of technology,the new combat style of multi-air unit(such as UAV group,cooperative/unmanned aerial vehicle,loyal wingman,etc.)has become a research hotspot.Heterogeneous air units are the combination of different performance units,from the superposition of isomorphic simple quantity to the combination of capabilities.The coordination of heterogeneous units can break through the limitation of individual ability and flexibly allocate resources,which is suitable for a variety of task scenarios.Collaborative task allocation is the top-level planning of cluster application.The reasonable correlation between each unit and task is established to strengthen the collaborative advantage of cluster.Taking UAV as the research objective,this paper explores an efficient collaborative task assignment method for multiple UAVs: according to environmental situation,platform function difference,task requirements and other factors,a reasonable task mapping relationship,accurate time node,appropriate load configuration and feasible flight track can be planned.The main innovations of this paper are as follows:1.Establish the cooperative task assignment model of heterogeneous UAV: with the task background of anti-aircraft suppression against the enemy,establish the cooperative task assignment model of heterogeneous UAV with time window constraint,and consider the task timing constraint,time window requirement,UAV load,UAV dynamic performance and other practical constraints;Four optimization objectives including mission completion time,mission revenue,mission loss and voyage are established.In order to solve the interference of different kinds of constraints which affect each other to the solution of the problem,a hierarchical constraint processing strategy is established by combining various constraint relations.2.Compare the performance differences of task assignment models of multi-type algorithms: select centralized quantum particle swarm optimization algorithm and distributed and consistent crowdsourcing algorithm,and improve and solve the model based on the characteristics of the assignment model.The solution scheme of quantum particle swarm is of good quality and takes a long time.Consistent crowdsourcing algorithm has excellent solution efficiency and general solution quality.It is suitable for dynamic task adjustment and can meet the task requirements of high time sensitivity.3.Multi-objective quantum-particle swarm optimization algorithm with high solution efficiency: fine local search of solution space oriented to optimization objective can improve search efficiency.The non-dominant solution evaluation method considers the convergence and distribution properties of the non-dominant solution and sorts them comprehensively to solve the problem of the declining dominance of the high-dimensional multi-objective.Multi-objective optimization algorithm is used to comprehensively consider multiple optimization objectives to improve decision-making efficiency.4.Improved consistent crowd-sourcing algorithm based on local adjustment: with the change of UAV and the number of tasks as the main change factor,the rapid redistribution strategy for local adjustment of the original plan was studied.The number of tasks involved in the reprogramming and the number of drones affect the convergence rate of the redistribution.Under the condition of incomplete redistribution,the redistribution algorithm dynamically selects the number of tasks involved in the reset according to the value of the new task and the specified response time,so as to balance the convergence rate and the overall benefits.