Collision Avoidance Method for Self-Organizing Unmanned Aerial Vehicle Flights

IEEE Access

Yang Huang¹  Jun Tang²  Songyang Lao³ 

[1] College of Systems Engineering, National University of Defense Technology, Changsha, China;Technical Innovation Cluster on Aeronautical Management, Universitat Aut&x00F2;noma de Barcelona, Sabadell, Spain;

关键词: Collision avoidance;    parameter optimization;    Reynolds rules;    self-organized;    unmanned aerial vehicle cluster;   

DOI  :  10.1109/ACCESS.2019.2925633

来源: DOAJ

【 摘 要 】

Autonomous unmanned aerial vehicle (UAV) swarm flights have been investigated widely. In the presence of a high airspace density and increasingly complex flight conditions, collision avoidance between UAV swarms is very important; however, this problem has not been fully addressed, particularly among self-organizing flight clusters. In this paper, we developed a method for avoiding collisions between different types of self-organized UAV clusters in various flight situations. The Reynolds rules were applied to self-organized flights of UAVs and a parameter optimization framework was used to optimize their organization, before developing a collision avoidance solution for UAV swarms. The proposed method can self-organize the flight of each UAV swarm during the overall process and the UAV swarm can continue to fly according to the self-organizing rules in the collision avoidance process. The UAVs in the airspace all make decisions according to their individual type. The UAVs in different UAV swarms can merge in the same space while avoiding collisions, where the UAV's self-organized flight process and collision avoidance process are very closely linked, and the trajectory is smooth to satisfy the actual operational needs. The numerical and experimental tests were conducted to demonstrate the effectiveness of the proposed algorithm. The results confirmed the effectiveness of this approach where self-organized flight cluster collision avoidance was successfully achieved by the UAV swarms.

【 授权许可】

Unknown