Autonomous Uav Formation Based On Flock Behavior Algorithm

In recent years,research on Unmanned Aerial Vehicle(UAV)and related technologies has received extensive attention in various fields.Not only has military strategic significance,such as investigation and combat,but also in the civilian sector,it has become more and more sought after by enterprises and enthusiasts.Compared to a single drone,drone formation has many advantages,such as reducing the load on single drone.The future UAV formation needs autonomy,flexibility,stability,and high-performance and high-efficiency to complete detection,item transportation,and even more complex tasks to meet the practical needs of different scenarios.In nature,group behaviors such as fish,birds,or swarms formed by groups of simple rules are inspiring for UAV group design.From the perspective of cost reduction,the UAV formation has more advantages than the single UAV.The large-scale bee colony consisting of only a few UAVs with computing equipment and sensors will have a wide application field.The UAV formation task includes formation algorithms such as formation design,formation transformation,split reorganization,etc.Considering obstacles and dynamic threats in the real environment,research on design of the UAV formation scheme also needs to be combined with the obstacle avoidance method.In order to design an autonomous formation algorithm with the ability to avoid obstacles and expand its scale,we proposed a distributed formation strategy of the drone combined with the flock behavior method and the artificial potential field method.In the flock behavior method,only the basic behaviors of the individual drones are specified,and the group consensus can be generated by mutual image according to the movement rules in the group.The artificial potential field method sets the potential field for objects in space,and the drones determine their motion according to the influence of the potential field.Combining the rule design of flock behavior method with the potential field function in artificial potential field method which smoothly change the motion state,the UAV refers to the motion state of the neighboring drones in the team to make its own motion and the group’s reach an agreement.At the same time,combined with the potential field function,the obstacles in the sensing range of the environment are autonomously avoided.The traditional pilot-following formation is poorly robust,and the lead-down of the line basically causes the task to fail.Therefore,the virtual leader is used to guide the movement of the drone formation.Finally,in view of the real obstacles in real life,the virtual speed barrier is calculated for the UAV based on the dynamic obstacles to enhance the obstacle avoidance success rate,and the virtual shell designing for the formation speeds up UAVs’ regrouping.This paper aims to improve the traditional flock behavior and artificial potential field algorithm for the large-scale scalable autonomous formation requirements in the future,innovatively combines the formation algorithm and obstacle avoidance algorithm,and optimizes the drone for the scene with dynamic obstacles.The formation process of 50 UAVs in free space and static/dynamic obstacles environments are simulated with MATLAB.Validit y and stability of the algorithm are verified.Random UAVs can successfully avoid the obstacles,and after exit the obstacle zone,a formation with the desired size is presented.