Research And Application Of Coordinated Path Planning Method For Multi-UAV

With the development of unmanned technology,as a new type of aerial weaponry,unmanned aerial vehicle(UAV)has attracted wide attention due to its small size,high mobility,low cost and its potential to work in complex and dangerous environment.Given the limited capabilities of a single UAV,the ability of multiple UAVs to perform missions together will greatly improve the overall performance.At the same time,in order to ensure that multiple UAVs can successfully complete all tasks at a minimum cost,it is necessary to build a reasonable task assignment model and obtain the best task assignment scheme by solving the model.Then,an efficient path planning algorithm is designed to plan the optimal path for each UAV.In addition,due to the complexity of the mission environment,the influence of the actual flight path on the task assignment should also be considered,so a fast path planning strategy is proposed to improve the quality of the task assignment results.In the path planning stage,because of the unknown and dynamic nature of the mission environment,how to make the UAV avoid the unknown static and dynamic obstacles in real time is also a problem that must be solved.Based on the research status of UAV mission planning,this paper analyzes and studies its task allocation,global path planning and real-time dynamic obstacle avoidance.The main work is as follows:(1)Summarize the existing mission planning techniques.Introduces the current research status of existing related algorithms,analyzes the advantages 、 disadvantages and application scenarios of various algorithms,clarifies the key problems to be solved,and lays the foundation for the subsequent design of collaborative mission planning algorithms for multi-UAV.(2)Fast path planning based on improved A* algorithm.An improved A* algorithm is designed for fast path planning,which can provide reliable actual flight path information for the task assignment model.Since the original A* algorithm is difficult to be applied in three-dimensional space and has the defect of excessive computation when the number of grids increases,the two methods are combined to improve it.The first method is to determine the height of the current grid based on the terrain height of the center of the neighboring grid,thereby gridding the three-dimensional space.The second method is to refer to the idea of sparse A* algorithm,search in the height direction,and eliminate invalid search domains according to the maneuverability constraints of the UAV.Because method one requires less computation and method two has high flexibility,the advantages of the two methods are combined by first planning using method one,and if it fails,using method two.(3)Design a multi-UAV collaborative task assignment method.First,use the improved A*algorithm to plan the rough path from each base to the target,and calculate the cost value of each path according to the length,height,and smoothness of the path,so as to obtain the cost matrix required for task assignment.Then comprehensively consider the coordination of multiple UAVs,that is,the completion rate of tasks and the time difference between each UAV to reach the target point,and establish a reasonable task assignment model.Finally,an improved parallel ant colony optimization(ACO)algorithm is used to find the optimal task assignment scheme.(4)Multi-UAV cooperative path planning based on cooperative particle swarm optimization(PSO)algorithm.By studying the existing PSO algorithm and combining the current application scenarios,a hybrid collaborative particle swarm algorithm is proposed.Firstly,the particles are divided into different subgroups,and each subgroup searches at different speeds in different space ranges to improve the global exploration ability of the algorithm.At the same time,an escape strategy is designed to improve the convergence precision of the algorithm.Then,a symbiotic organisms search(SOS)algorithm is introduced to improve the local exploitation ability of the algorithm without changing the complexity of the algorithm.The simulation results show that the proposed hybrid path planning algorithm has greater advantages than other algorithms in terms of convergence speed,quality of optimal solution and stability.(5)Real-time obstacle avoidance based on rolling window method.In order to quickly avoid unknown static and dynamic obstacles in the task environment,a local path planning method based on the rolling window is proposed.The UAV first flies along a pre-planned flight path and monitors changes in the surrounding environment in real time.When the unknown obstacle is perceived,the current environment information will be handed over to the decision-making system for local path planning in the current rolling window,and finally the dynamic system will implement real-time obstacle avoidance.In addition,in order to deal with the threat of dynamic obstacles,the motion information of dynamic obstacles will be predicted,and the dynamic obstacle avoidance will be realized by establishing its expansion model.The simulation results show that the online obstacle avoidance strategy proposed in this paper can quickly avoid the threat of unknown obstacles with a high success rate.(6)Design a simulation platform for multi-UAV collaborative mission planning.Based on the above algorithm,design a dynamic simulation test and evaluation environment with man-machine interface and comprehensive parameter input,and a more perfect simulation and verification system for multi-UAV cooperative mission planning is built,which integrates "environment import--task assignment--path planning--dynamic obstacle avoidance".