Research On Multi-Unmanned Aerial Vehicle Route Planning And Control Method For Sea Area Target Search

With the rapid development of unmanned aerial vehicle(UAV)technology,UAV has been widely used in military and civil fields.The problems of the limited load and the low task execution efficiency of a single UAV have gradually become prominent.The cooperative task execution of multi-UAVs is an important manifestation of the autonomous capability of UAV,which is a higher level of autonomy.Therefore,the cooperation of multi-UAVs to accomplish tasks has become an important research topic.Among them,as a key problem to be solved urgently in multi-UAVs cooperation,path planning and control play a crucial role in improving the autonomous mission capability of UAV.This paper takes multi-UAVs cooperative search of sea area targets as the research background,and studies the key technologies involved in Route planning and control.The main research works are as follows:Firstly,in the view of the path planning problem in unknown sea environment cooperative search,sea environment model,the environmental information update model and the optimization model for cooperative search task are established;While considering the task execution process of UAV mutual collision problem,the pheromone exclusive factor is introduced to improve the state transition rules of the ant colony algorithm,and the improved multi-ant colony algorithm is proposed to realize the UAV cooperative decision-making.The simulation results show that the proposed improved multi-ant colony algorithm can effectively improve the probability of target discovery and avoid collision compared with the existing traditional search algorithms,which is suitable for target search in a large range of sea areas.Secondly,for the target probability map inaccurate problem which is caused by the unreliable positing information in the multiple UAVs cooperative search,this paper proposes a multi-UAVs cooperative positing algorithm under global communication environment with the location information uncertainty,designs the federal Kalman filter algorithm to improve the unmanned aerial vehicle location accuracy.and environmental information is updated according to the optimized UAV state information,which makes the UAV cluster obtain accurate environmental information and provides the guarantee for the cooperative search.Finally,the simulation results show the effectiveness of the proposed method.Finally,aiming at the trajectory tracking problem of UAV under external disturbance,the trajectory tracking model is established,and the idea of model prediction control is used to transform the problem into a constrained standard quadratic programming problem.Due to the kinematics constraints,the control increment constraint and relaxation factor are considered in the objective function.According to the real-time requirement of UAV the dynamic neural network is introduced to solve the quadratic programming problem iteratively in real time,reduces the operational complexity and improves the operational efficiency.At the same time,considering the existence of external disturbance,the disturbance compensation observer is designed to overcome the disturbance,and the effectiveness and stability of the proposed control strategy are verified by the simulation.