Moving target recognition and unmanned aerial vehicle following based on deep deterministic policy gradient
In order to solve the problems of loss of moving targets due to the flight status of the unmanned aerial vehicle(UAV)itself,the interference of the environment,the randomness of the target and other reasons in the process of collecting moving target image information by the UAV platform,a deep deterministic policy gradient(DDPG)algorithm UAV following method based on moving target recognition was proposed.Facing the vehicle target on the highway,the relationship among the height,posture and high-speed vehicle motion of the UAV was analyzed,the velocity adaptive model of the target detection frame rate of the mobile platform was estab-lished,and the flight attitude and speed of the UAV were corrected in real time according to the motion state of the target,so that the UAV could maintain the relative position and angle with the target.Then,based on the value network of DDPG algorithm,the value of UAV taking spe-cific actions in different states was estimated;the strategy network generates the strategy of UAV taking actions in a given state,and gives UAV flight altitude and speed control parameters for target tracking,so that the UAV could automatically adjust the flight state according to the movement change of the target,and realize the adaptive tracking of the moving target.Simulation experiments show that the DDPG algorithm can provide stable flight attitude data and a reliable control basis for the UAV following task,and the UAV can track the ground moving target in a circular area with a speed range of 0～33 m/s and a radius of 120 m in real time,and can achieve continuous and stable tracking within the endurance range.
万方数据
维普
