Multiple UAV Cooperative Path Planning And Task Allocation For Tracking Moving Targets

With the development of aviation technology,multiple unmanned aerial vehicle(UAV)is used widely in the dirty and dangerous environment to perform tasks,the target tracking is the focus in many applications of UAV,such as tracking,forest rescue,military strikes.Due to the complex flight environment and many constraints,single UAV is not enough to achieve the continuous tracking of the target,so it is necessary to cooperate with many UAVs to track the target continuously and accurately.As a result,the path planning and task allocation caused by multiple UAV cooperative tracking of ground moving targets has attracted much attention.The path planning of UAV depends on the result of task allocation,and the objective functions of the task allocation are related to the path planning of UAV.Both of them influence each other and restraint each other.Therefore,to achieve the target tracking continuously in urban environment,a path planning algorithm of multiple UAV cooperative task allocation is discussed.The research is performed for the multi-objective path planning problem with priority constraints.Firstly,the factors including line of sight occlusion from buildings,UAVs control input and energy consumptions of sensors are considered.Correspondingly,the objective functions are designed as target coverage degree,control input cost and sensor energy consumption in switch value respectively such that the multiple UAV cooperative tracking problem is transformed into a multi-objective programming problem;Then,based on distributed predictive control,the predictive states of each UAV in a finite horizon are exchanged to build up collision avoidance constraint between UAVs.Combining the minimum turning radius constraints,the distributed cooperative path planning model is formulated.Finally,for the different important levels requirement,all the objectives are fuzzified by fuzzy satisfactory optimization concept.According to the principle that the objective with higher priority has higher satisfactory degree,preemptive priorities are modeled into the order of relaxed satisfactory degrees.The local preferred path of each UAV is worked out online in a finite period.As the target motion state is unknown for target tracking,it is important to estimate and predict the target state.Then,extended kalman filtering(EKF)and probability filtering is used for target state estimation and prediction.When the target along the road straight movement,EKF is adopted to estimate and predict the target state,when the target turns,the target state is estimated and predicted by probability filtering.Finally,the estimation and prediction of target state are used to optimize UAV path with reliable information.The research is performed for multiple UAV cooperative tracking multiple targets.In order to ensure that the target is tracked by at least a UAV,and each UAV performs the tracking task,the cooperative path planning and task allocation problem of multiple UAVs are studied.Firstly,the multiple UAV cooperative tracking is transformed into a complex 0-1 mixed integer programming problem.Then,a hierarchical method and a integration method based on particle swarm optimization(PSO)are given:a hierarchical method is that the multiple UAV paths are optimized using the path planning method and then the PSO is proposed for real-time task allocation;a integration method is that the PSO is used not only to optimize the variables O and 1,but also optimize the tracking path.The task allocation and path planning are performed simultaneously.Combined with the urban environment,the idea of event triggering is used and the event triggering mechanism is designed to shorten the whole time of the integration solution and realize the task allocation.