Research Of Multi-UAVs Cooperative Mission Planning Technology In Battlefield Environments

In recent years,unmanned aerial vehicles(UAVs)have been widely used in modern warfare.With the increasingly complex battlefield environments and situations,multi-UAVs cooperative combat has gradually become the major air combat mode.Therefore,the research on multi-UAVs mission planning algorithm becomes particularly important.This paper focuses on the study of task assignment and path planning,which are two essential parts of multi-UAVs mission planning technology,and the main work is as follows:In order to solve the problem of slow convergence and easy to get into local optimal solution in the single task type assignment process of large-scale multi-homogeneous UAVs,the paper proposes a task assignment algorithm based on improved clustering Max-Min Ant System(MMAS).Firstly,the k-means clustering algorithm is applied to cluster task points to reduce the problem scale.Secondly,the solution set updating strategy is introduced and the pheromone concentration limiting formula is optimized to improve the MMAS.Finally,to optimize the performance of the algorithm,the "three steps" strategy is used to select the parameters of the algorithm.The simulation results show that the improved clustering MMAS can solve the task assignment problem of large-scale multi-homogeneous UAVs,accelerate the convergence speed effectively and prevent falling into the local optimal solution.To solve the problems of long path length and low communication efficiency in multi-task type assignment of large-scale multi-heterogeneous UAVs with task time window constraints,this paper proposes a distributed task assignment algorithm based on the improved ConsensusBased Boundle Algorithm(CBBA).First of all,the problem is deeply studied to establish the algorithm model,and the relevant parameters of UAV in the allocation process are discussed.Secondly,in order to improve the communication efficiency between UAVs and overcome the disadvantages of the original allocation scheme,the density clustering is preferred to dispose the task points and the distance reward and punishment coefficient is introduced to improve the CBBA algorithm.Finally,combining the clustering algorithm and the improved CBBA algorithm to realize the conflict-free task assignment among the multi-heterogeneous UAVs.In order to verify the effectiveness of the algorithm,this paper designs a variety of UAVs and mission scale scenarios and multiple task aggregation scenarios.Simulation experiments show that the improved clustering CBBA algorithm reduces the allocation cost and improves the security and communication efficiency of UAVs.Aiming at reducing planning path points and path length,as well as raising practicability in a battlefield mountain environment,this paper proposes an improved three-dimensional sparse A-Star path planning algorithm based on variable step size.First,the performance constraints of UAV are analyzed and incorporated into the two-layer node sparse expansion rules to improve the search efficiency of the algorithm.Secondly,the battlefield environment and constraints are modeled and analyzed.The heuristic function and constraint information are integrated into the improved cost function.Thirdly,the variable step size node extension strategy is applied to reduce the number of path points so as to smooth the trajectory and improve the convergence speed of the algorithm to a certain extent.Finally,the planned path is smoothed to meet the actual flight requirements.The simulation results show that the improved algorithm can complete the path planning with a shorter path length,fewer path points and higher practicability when there are low threats or safe zones within the planned area.