Research On Uavs Cooperative Formation Control Based On Distributed Search And Capture Algorithm

As the technology develops from single-UAV to multi-UAV collaboration,the application scenarios where multiple UAVs are supposed to adapt to different needs to achieve formation control have been increasing rapidly.Compared to one single UAV,multiple UAVs can carry different equipment and complete tasks that cannot be completed by a single UAV.Currently,multi-UAV formation control still has low coordination capabilities and insufficient autonomy to respond to emergencies.The need of low real-time performance cannot be met in formation control for different flight missions.Therefore,it is urgent to conduct multi-UAV formation control researches in the direction of group intelligent control.In order to solve the above problems,the multi-UAV collaborative formation control problem is divided into two subproblems of formation reconstruction and formation control.Swarm intelligence-based algorithms are introduced.Based on information interaction and decision-making,the distributed UAV formation control algorithm is designed to improve the self-organizing control capability of UAVs.The algorithm helps UAVs to form a specific formation smoothly and to switch between different formations according to different scenarios.Aiming at the problem of formation reconstruction with random initial positions,a distributed search and capture algorithm is proposed,a mathematical model is established,and mission objectives are determined.According to the tasks of each stage of the hunting behavior,the model is divided into five parts to constitute the information flow of decision generation,decision improvement,and decision confirmation.The status information table is set up to coordinate the goals between the UAVs to avoid conflicts to achieve the consistency of decision-making in the entire formation process.The time label reduces and eliminates the influence of status information return and improves the real-time performance of the formation algorithm.Fusion and verification of the status information table prevent the loss of information during the transmission process and improve the stability of the formation.The simulation proves the convergence of the formation reconstruction algorithm.By comparing the movement trajectory before and after the introduction of the state information table,the state information table is verified to improve the coordination ability and the time label is reduced to eliminate the influence of state information return.This paper focuses on the formation change problem with determining initial positions,which is transformed into a multipoint-to-multipoint path planning problem.A mathematical model of formation transformation with optimize functions and constraints is established to achieve the goal of "short,less and average" path planning.Game among multiple UAVs is conducted to select targets.Through the deductive method,we optimize the state of the previous moment,divide the optimization process into equal optimization and cross fine-tuning,and optimize the target path of multiple UAVs corresponding to the optimization goals "equal" and "less".The calculation is transformed into a new formation goal.In the simulation,the smoothness of the transformation from the source formation to the target formation is demonstrated,and the performance is compared with the reconstruction algorithm.Aiming at the multi-UAV obstacle avoidance coordination problem,this paper designs the algorithm of obstacle avoidance coordination with an improved artificial potential field method as a complement to avoid collisions between UAVs or obstacles.The state information table is used to construct the obstacle avoidance coordination graph,and the obstacle avoidance block is obtained by the depth-first search,and the obstacle avoidance coordination problem is transformed into the formation change problem.When flying to the target,the UAV uses the potential field method to fine-tune to ensure that there is no collision between UAVs.Under the obstacle avoidance order,the priority of obstacle avoidance coordination is higher than that of the improved artificial potential field method,which enhances the adaptive ability of the UAVs when conducting obstacle avoidance.