Research On Key Technologies Of UAV Swarm Cooperative Mission Planning

UAV has strong battlefield adaptability and survivability,and will play an increasingly important role in the battlefield.Due to the limited ability of a single UAV in terms of its own navigation time and load,the cooperative execution of tasks by multiple UAVs is one of the important directions of future UAV technology development.Unmanned Aerial Vehicle(UAV)cluster collaborative mission planning technology is a technical support for guiding UAVs to achieve autonomous flight and complete designated combat tasks,which can improve the utilization efficiency of battlefield resources and give full play to the advantages of UAV cluster collaborative reconnaissance platforms.Based on the research background of UAV cluster collaborative search and tracking task,this paper studies the key technologies of UAV cluster collaborative task planning under heterogeneous type,dynamic target,long endurance and complex environment,and proposes a UAV cluster collaborative search and tracking scheme with long endurance and high performance.The main contents of the paper are as follows:In the cooperative search task for dynamic targets,the task planning problem under the condition of heterogeneous maneuvering performance and detection performance of UAV swarm was studied,and an improved task planning scheme for heterogeneous UAV swarm cooperative search was established.Firstly,in order to solve the path coding problem caused by heterogeneous maneuvability performance,a decision input solution based on polar coordinate motion model was proposed,and the flight path of heterogeneous UAV swarm was continuously encoded.Then,in order to solve the cooperative search perception problem caused by heterogeneous detection performance,an improved target probability graph algorithm was proposed,which classified four different types of prior information.Based on the heterogeneous UAV detection model,a target detection probability distribution graph model was established,which solved the trajectory prediction problem of heterogeneous UAV clusters for dynamic targets.At the same time,an improved digital pheromone graph algorithm was proposed to improve the collaborative search efficiency of clusters.Then,aiming at the problem of building and solving the cooperative task optimization model caused by the heterogeneous flight constraints,a cooperative search task optimization model based on cluster cooperative search sensing data was established,and the joint evaluation of cluster search benefits was realized.An improved Particle swarm optimizationreceding horizon control(PSO-RHC)algorithm is designed to optimize the cooperative search optimization model,which can obtain the optimal decision input solution satisfying the system constraints.Then,the optimal search path of UAV swarm is generated.Finally,the simulation analysis of heterogeneous UAV swarm cooperative search task for dynamic targets is carried out.The simulation results show that the proposed method has strong superiority and innovation in both algorithm optimization performance and task execution efficiency under the condition of 4UAVs cooperating to search 8 unknown targets.The collaborative task efficiency of heterogeneous UAV swarm is improved by 227%.In the cooperative tracking task of dynamic target in complex environment,the influence of threat region and dynamic turbulent wind field on the cooperative tracking process of UAV swarm in low altitude is studied,and an improved cooperative tracking task optimization planning scheme of UAV swarm in long endurance is proposed.Firstly,in view of the impact of turbulent wind field on UAV flight energy consumption,the UAV energy consumption algorithm based on Dubins motion curve was established,and the key variables of energy consumption optimization were proposed to realize the estimation of UAV flight energy consumption under the influence of turbulent wind field.Then,for the problem of threat avoidance and target trajectory prediction,the detection and update mechanism of threat distribution map and target trajectory probability map was established to realize the reliable tracking of dynamic targets by UAV swarm.Then,for the problem of long-endurance cooperative tracking,an energy consumption optimization model based on the improved was established.By optimizing the flight speed and flight path of the UAV swarm,the energy consumption efficiency of the UAV swarm in the process of cooperative tracking was improved,and the continuous tracking of the dynamic target was realized.Finally,in order to study the optimization performance of the proposed optimization method in the two indicators of mission execution efficiency and flight energy utilization efficiency,several simulations were carried out,and the simulation results before and after optimization were compared.The simulation results show that the proposed optimization method can sense the target motion trajectory and turbulent wind field environment,so that the UAV swarm can optimize the motion state of the UAV in real time according to the relative position of the target and the wind field conditions,so as to effectively improve the endurance of the UAV and the duration of the tracking task.Compared with the simulation results before optimization using the proposed method,the optimization method in this paper improves the tracking task duration by 46.9% and the energy utilization efficiency by 40.2% in the cooperative tracking task of UAV swarm.