Route Planning Algorithm For Fixed-Wing UAV

With the development,innovation and application of UAV(Unmanned Aerial Vehicle)technology,the UAV has become more and more dependent on energy,communication and logistics,and its task space are also become more complex.So,it needs more efficient tracks to adapt to the complex and changeable task space.The energy consumption,formation collaboration ability,threat avoidance ability and the dependence on guarantee systems are all dependence on the tracks,and the tracks also guarantee the successful completion of the tasks.Under this background,this thesis study the track planning method of UAV,and proposed three algorithms to deal with the track planning,formation maintenance and multitask allocation.This thesis proposed a track planning algorithm based on improved PSO method,a formation track planning algorithm based on improved APF method and a multitask allocation algorithm based on improved ant colony method.It mainly includes the following aspects:The UAV trajectory planning problem in complex task space is investigated and analyzed.A track planning algorithm based on Metropolis criterion and PSO algorithm improved by RTS smoothing algorithm is proposed.Firstly,the task space digital map is constructed for UAV trajectory planning,the trajectory planning model and the trajectory representation model are introduced,the trajectory constraint model is designed,a new fitness function is proposed,and the trajectory planning algorithm based on improved PSO algorithm is proposed.PSO algorithm is simple in form and fast in search,so it is applied to the basic algorithm of route planning algorithm,and Metropolis criterion is introduced as a new acceptance strategy to ensure the global convergence of PSO algorithm and avoid the PSO algorithm falling into the local optimal problem.RTS smoothing algorithm is introduced to smoothen the planned route in order to provide a feasible smooth path for UAV and avoid the twists and turns of the track.In addition,a real-time on-line route planning algorithm(LPSO)based on route planning algorithm is proposed to deal with sudden threats in complex task space.Finally,the simulation results show the effectiveness of the proposed algorithm and its superiority over other algorithms,and verify the effectiveness of the real-time online route planning algorithm in the face of sudden threats.Aiming at the problem of UAV formation trajectory planning,a formation trajectory planning algorithm based on Leader-Follower structure and improved APF algorithm is designed.Firstly,aiming at the formation flying problem of UAV,the formation mathematical model is introduced.Where the tracks of main engine(Leader)in the Leader-Follower structure used the route planning algorithm proposed in this paper.The route planning of the bureaucracy is mainly based on the improved APF algorithm.The variable step design is introduced into the algorithm,which makes the bureaucracy maintain the formation of the host computer and effectively avoid track crossing and overlap.The validity and rationality of the proposed UAV formation path planning algorithm are verified by track simulation of 4-aircraft formation and 16-aircraft formation.Aiming at the multi-task assignment problem of UAV in complex task space,a multi-task assignment algorithm based on improved ant colony algorithm is proposed.Firstly,the multi-task assignment problem for UAV is investigated and analyzed,and the formation mathematical model and comprehensive situation assessment function are constructed,and the AHP algorithm is introduced to realize the qualitative and quantitative analysis of the evaluation weight.Aiming at the discrete characteristics of multi-task assignment problem,the ant colony optimization(ACO)algorithm is adopted as the basic algorithm of multi-task assignment algorithm.Aiming at the lack of local search ability of ACO algorithm,2-Opt algorithm is used to improve the local search ability of ACO algorithm.Finally a multi-task assignment algorithm based on improved ACO algorithm is designed.In the end,this thesis design two examples and verified the effectiveness and rationality of the proposed multi-task allocation algorithm.