Path Planning Algorithm For UAV Based On Intelligent And Cognition

The intelligent and cognitive level of unmanned aerial vehicle(UAV)is of great significance to comprehensively promote the construction of cognitive electronic warfare capability and accelerate the transformation of cognitive electronic warfare system to actual combat.However,the path planning of intelligent UAV has become the technical challenges of effective electronic warfare countermeasures.Aiming at the intelligent path planning problem of UAV under the conditions of complex combat task,dynamic and changeable combat environment and sudden battlefield state,the research focuses on the path planning modeling,swarm intelligence optimization algorithm and deep reinforcement learning technology.Firstly,the related problems of UAV path planning are described.Combined with the change of UAV’s flight attitude,the motion constraints of UAV’s path planning are set,and the path planning environment is constructed.The threat models are established,which include the terrain,radar threat,anti-aircraft gun threat and electronic jamming.The simulation results verify the feasibility of the model.Secondly,we construct the objective function of flight range and flight altitude,and design constraints.Combined with the swarm intelligence optimization mechanism,the artificial fish swarm algorithm is used to optimize the objective function to achieve the initial path planning of UAV in static environment.Thirdly,in order to improve the moving step size of the artificial fish swarm algorithm,we combine the Levy flight optimization principle of cuckoo algorithm to speed up the convergence.In this paper,we propose a new path re-planning method based on the improved artificial fish swarm algorithm,which can solve the problem of slow response speed in the condition of sudden threat.Finally,the continuous attitude constraints of UAV route planning are optimized,and the visual platform of route planning is built.Combined with reinforcement learning model and its various models,we design UAV path planning state,action and reward.On this basis,this paper proposes a route re-planning method based on the improved depth deterministic strategy gradient algorithm.We use bacteria foraging to improve the artificial fish swarm algorithm to optimize the parameters of the action evaluation network in the depth deterministic strategy gradient algorithm.The reliability of the improved algorithm is verified by simulation,and the feasibility of the platform is tested.At the same time,the effective path re-planning of UAV in dynamic environment is realized.It provides a new idea to promote the development of artificial intelligence in UAV path planning.