Research On Collision Avoidance Method For UAV Cluster

With the development of UAV technology,multi-UAVs have become more and more widely used in military and civilian fields.As the density of airspace is getting larger,the situation of multi-UAVs cooperating with tasks is becoming more and more normal.The multi-UAV group flight collision avoidance problem is a key issue that needs to be solved in the UAV cluster control area.Most of the existing algorithms only study the collision avoidance problem of a small number of UAVs,and most of them are static obstacles cases.There are few collision avoidance algorithms for studying large scale UAVs.In this paper,the collision avoidance of the UAV group is divided into three different aspects and the corresponding collision avoidance algorithm is provided.The three aspects are the collision avoidance problem under the multi-UAV random flight conditions,the formation flight conditions and the self-organized flight conditions.The multi-UAV random flight condition refers to the collision avoidance problem when multiple UAVs meet in a small airspace.This paper innovatively uses complex network theory to model the scenario and abstract the UAV as a node.Then the model abstracts the threat relationship between the UAVs as an edge.After the network is constructed,the critical nodes are determined by calculating the threat degree of the nodes in the network,and the selected key nodes are UAVs with collision avoidance priority.After selecting the UAV corresponding to the key nodes,the collision avoidance direction of the UAV is determined by comparing the influence of the UAV on the robustness of the network in different directions.Finally,a simulation experiment is designed to verify the feasibility of the algorithm.The influence of different key node selection methods on the algorithm’s effect is analyzed.In order to solve the problem of collision avoidance of UAV group under formation flight conditions,this paper proposes a method of collision avoidance for UAV group based on second-order consensus algorithm and improved artificial potential field.Based on this method,the UAV group can form a predetermined formation from any initial state and fly to the target position and can avoid collisions according to the improved smooth artificial potential field method when encountering an obstacle.The UAV group adopts the “Leader-Follower” strategy,that is,the leader UAV is the controller and independently flies according to the mission requirements,and the follower UAV follows the leader UAV based on the second-order consensus algorithm and flight in formation.In the collision avoidance process,the improved artificial potential field method can solve the problem that the traditional artificial potential field method produces severe jitter on the UAV.Simulation experiments of two scenarios were designed to verify the collision avoidance effect and formation retention effect of static obstacles and dynamic obstacles.Experimental results demonstrate the effectiveness of the method.This paper also solves the problem of collision avoidance between different UAV groups in the case of self-organizing flight.In this paper,the Reynolds rule is used to model the self-organizing flight of the UAV.The parameter optimization framework is used to optimize the self-organizing flight of the UAV,and the collision avoidance problem between the UAV group and the UAV group is innovatively solved.The self-organizing flight and collision avoidance algorithm of the UAV group successfully realized the self-organized flight collision avoidance between UAVs.In order to prove that the method is effective,simulations of various scenarios have been carried out.The results confirm the effectiveness of the proposed method and successfully realize the self-organized flight collision avoidance of the UAV group.This paper mainly studies collision avoidance method of the UAV group,and attempts to study the collision avoidance methods of the three types of UAV clusters.The paper has certain theoretical value and application value for the collision avoidance of multi-UAV group.