Research On Multi-UAVs Cooperative Searching And Tracking Of Dynamic Targets Based On Optimization Algorithms

In recent years,Unmanned Aerial Vehicles(UAVs)have been widely utilized in multiple fields to perform operations such as emergencies,earthquake relief,military and civilian mission et al.How to realize the UAVs execute heterogeneous tasks cooperatively in complex environment which become an important problem in application fields.The thesis studies several key issues such as wireless network routing planning,consistency processing of environmental information,online path planning and obstacle avoidance for UAVs cooperative searching and tracking.The main contents of this thesis as follows:(1)In order to solve the problem of network resource occupation,information conflict,link redundancy and broadcast storm in broadcast process,a broadcast strategy which is proposed based on greedy ant colony algorithm(GACA).Firstly,the target function is established by combining the location information,the time of information transmission and the number of forwarding nodes.Then,the GACA is used to achieve the minimum of the object function value and program the path of information broadcast based on predicted result of the UAV motion state.Finally,the forwarding nodes complete the message forwarding according to the path requirement.Simulation results show that the strategy can satisfy information transmission requirements,utilize network resources reasonably,reduce information redundancy and avoid broadcast storms.(2)The problem of information inconsistency caused by data packet loss in UAV communication process in discussed in this thesis,and the UAV environmental information description model is proposed based on searching map,and the multi-sources information fusion algorithm is presented to solve the information inconsistency problem generated by data loss during global communication based on least square method(LSM)and weighted averaging method(WAM).Bayesian principles are employed to analysis the problems of prediction updating and communication updating of the searching map.The fitting function is constructed by introducing probability density function,and the weighted value is calculated based on the uncertainly of information.In order to verify the influence of consistent environmental information on searching success rate and searching time,an modified Genetic Algorithm(MGA)is proposed to plan the UAV searching path in real time.Furthermore,by the comparison simulations with several other classical searching methods,such as Zamboni searching,Greedy searching and Ant Colony searching,the proposed method can improve the searching efficiency and reduce the searching time based on consistency environmental information.(3)Aiming at solving the problem of continuous detection and tracking after UAVs finding targets,an online path planning framework is proposed based on hierarchical collaboration,which is divided into two parts: online tracking path planning and obstacles avoidance path planning.To track the flying targets in dynamic environment,a modified Lyapunov guidance vector field(LGVF)method is used to plan UAV’s velocity,where a time-varying vertical component is incorporated into the traditional LGVF function to satisfy the constraints of cluster communication.In order to avoid obstacles,a three-dimensional local collision-free guidance vector field(TLCGVF)method is proposed for UAVs to plan collision-free path.To simultaneously track the target and avoid obstacles,the vector field by LGVF is used as the original vector field of TLCGVF.