Research On Adaptive Collaborative Control Of UAV Swarm For Persistent Surveillance

With the continuous development of unmanned aerial vehicles(UAVs),its potential application prospects on the modern battlefield are growing.Thanks to its broad perspective,high mobility and support for multiple sensing modules,reconnaissance UAVs are increasingly becoming an important means of quickly acquiring information rights on the battlefield.This dissertation takes the persistent surveillance mission of UAV swarms as the research background,the environment model,the optimization method of the amount of UAVs,the strategy of dynamic deployment of UAV swarm and an Agent-based collaborative swarm control are studied in this dissertation.The contents of the dissertation are as follows:(1)The persistent surveillance problem of UAV swarm was analyzed.Combined with the characteristics of the mission of the UAVs,the characteristics of the UAV swarm persistent surveillance problem were clarified,and the key issues in the persistent surveillance process of the UAV swarm were analyzed,which lays a foundation for further designing the persistent surveillance strategy of UAV swarm.(2)An improved artificial potential field method was presented to indicate the dynamic change of the priority of the reconnaissance environment task.In view of the characteristics of task priority in continuous tasks,the artificial potential field based environment model was discussed in detail.The ecological environment of “vegetation-herbivore” in nature was simulated in this model,which can self-evolve as well as interact with the surveillance UAV swarm.The artificial potential field was improved to quantify the task priority evolution process,which provides a basis for the swarm to adaptively move and adjust its mission region.(3)A method for optimizing the number of UAV clusters based on community matrix was presented.Based on the growth and balance mechanism of biological populations,a model of "digital turf" based on regional information entropy and its "probability(abundance)" model was constructed.The model introduces the population balance mechanism,mimics the ecological balance dynamics model based on the “target area-unmanned aerial vehicle cluster” community matrix.The balance and convergence of the system were described by mathematical derivation.The mathematical model was used to describe the balance and convergence of the system.The dissertation discussed the model of the UAV swarm and the surveillance tasks by simulation.(4)Iterative data clustering based strategy were designed for UAV swarm dynamic deployment.The dynamic deployment control framework for UAV swarm was discussed.Rasterized digital turf model was used for a grid-based dynamic data clustering algorithm to adaptively adjust the sub-swarm attribute.(5)Agent-based persistent surveillance modeling and simulation of UAV swarm.Through the Agent-based modeling methods,the factors affecting the persistent surveillance effect of the UAV swarms were analyzed,and the improved and the improved deployment strategy was compared with other strategies to prove its effectiveness.