Research On Vision-based Multi-UAV Collaborative Target Tracking And Path Planning

Vision-based UAV target tracking and obstacle avoidance highlights the advantages in the UAV target search,combat and other field,getting important application.The paper studies two aspects of vision-based target tracking and multi-UAV collaborative obstacle avoidance route planning.Aiming at the problems of target deformation,illumination change and object occlusion in the process of UAV visual target tracking,an improved KCF target tracking algorithm is proposed.Firstly,the HOG feature and the FAST feature of the target are fused.The fusion of the features is studied and trained.It enhances the dimension and depth of the target expression and improve the tracking accuracy.Then,the virtual target block is generated and the target is matched by the gray histogram,so that the size of the tracking target frame can adapt to the change of the actual target size.Finally,the simulation shows that the proposed improved KCF algorithm has good tracking accuracy and tracking speed,and can achieve adaptive change of target frame size with target size.Aiming at the problem of cooperative route planning for multiple Unmanned Aerial Vehicles(UAVs)to avoid collision,an improved artificial potential field(APF)method that combined with Bezier curve is proposed to increase the self-repulsion field of UAV.Firstly,UAV’s own repulsive potential field that has the characteristics of piecewise continuity is defined.Secondly,a method based on virtual obstacle is designed to escape from the local optimal.Then,a piecewise Bezier curve smoothing algorithm is proposed to optimize the flight path of the UAV in real time,which eliminates the path oscillation in the route planning.Finally,the simulation results show that the proposed method can achieve multi-UAV collaborative target tracking and obstacle avoidance path planning.Aiming at the problem of environment-model-free route planning for UAV,a route planning method PF-DQN based on potential function reward under unknown environmental information and continuous state is proposed.Firstly,the state space of the uav in the environment is established.Secondly,after the 360 degree is divided into several angles as the heading angle of the uav,the action space of the uav is established.Then,the potential function reward of the target and the potential function reward of the obstacle are formulated.Finally,the simulation results show that the PF-DQN algorithm can realize the environment-model-free route planning under unknown environmental information and continuous state,and the potential function reward accelerates the speed of the UAV route planning.This paper designed and developed a multi-UAV collaborative target tracking experimental platform.Firstly,it introduced the hardware design of the experimental platform.Then it elaborated the design of the system software.Finally,flight experiment is carried out.and the experiment results show that the whole platform is systematic and scalable,so it is a reliable algorithm verification platform for multi-UAV collaborative target tracking research.