Research On Cooperative Task Assignment And Planning Of UAV Swarm

The unmanned aerial vehicle(UAV)swarm has become a breakthrough technology in numerous fields such as environment monitoring,target tracking,reconnaissance and attack due to its high autonomy and intelligence.Cooperative task assignment and planning is a key point to the effectiveness of the swarm.However,when the number of UAVs increases,the search space for the planning problem is greatly expanded,which leads to problems such as uneven assignment,redundant trajectory and insufficient coordination by adopting the traditional mission planning methods.To solve these issues,this thesis researches a novel task assignment and trajectory planning method for UAV swarm to achieve quick matching of large-scale targets,and on this basis,realize the optimal selection of feasible trajectories,which improve the efficiency and synergy of task planning.The main work of this thesis is twofold:(1)Considering the situation that UAV swarm performs various tasks cooperatively against multiple aerial moving targets,this thesis develops a multi-population assignment method based on swarm intelligence optimization algorithm.Firstly,a task assignment model for UAV swarm is constructed by introducing the aerial situation model,which designs damage cost and time cost as a dual objective function.Secondly,this method regards multi-UAV as several parallel sub-populations and adopts the layered encoding and multi-objective optimization strategy to preserve the optimal individuals of each sub-population simultaneously.Additionally,an archive-shared strategy and a matching rule are designed to obtain the optimal solution of the whole swarm.Finally,compared with the conventional intelligent algorithms,the performance of the proposed method is verified.The experiments show that for scenarios with a large number of targets,the proposed method integrates dual-cost function and multiple constraints.Moreover,compared with multi-objective grey wolf optimizer and multi-objective particle swarm optimizer,it has better stability and convergence performance.(2)Aiming at the problem of coordinated trajectory planning of UAV swarm against multiple targets,this thesis investigates a hierarchical planning method based on time coordination.Firstly,considering the flight trajectory of one UAV,a single-UAV trajectory planning model is established by designing the trajectory cost function.Secondly,considering the time coordination,a many-to-one trajectory planning model is constructed by introducing a time coordination function to realize that multiple UAVs reach one target at the same time.Additionally,considering obstacle factors further,a many-to-many trajectory planning model is built,which designs obstacle constraints to achieve cooperative obstacle avoidance between UAV and obstacle,as well as between UAV and other UAVs.Finally,the simulation verifies the flight trajectory and convergence curve under scenarios with different obstacles and UAVs distribution.The experiments show that for scenarios with increasing obstacles,the proposed method achieves time coordination and obstacle avoidance for multiple UAVs.In addition,it has good convergence performance.This thesis applies the UAV swarm task assignment and trajectory planning method to the large-scale target distribution scenarios.The results show that this method has a task assignment rate over 70%,and outperforms traditional assignment algorithm in terms of the average costs and convergence.On the basis of effective allocation,it can plan cooperative trajectories for three UAVs on the same target at the same time,and makes a collision-free and low-cost trajectory planning for UAV swarm.