A large-scale clustering and 3D trajectory optimization approach for UAV swarms

With the significant development of unmanned aerial vehicles (UAVs) technologies, a rapid increase on the use of UAV swarms in a wide range of civilian and emergency applications has been witnessed. However, how to efficiently network the large-scale UAVs and implement the swarms applications without infrastructure support in remote areas is challenging. In this paper, we investigate a hierarchal large-scale infrastructure-less UAV swarm scenario, where numerous UAVs surveil and collect data from the ground and a ferry UAV (Ferry UAV) is designated to carry back all their collected data. We can divide UAV swarms into different areas based on their geographic locations due to the wide range of surveillance. To improve data collection efficiency of Ferry UAV, we introduce a single super cluster head (Super-CH) UAV in each area which can be selected by the proposed modified k-means clustering algorithm with low latency. Then, we design an iterative approach to optimize the 3-dimensional (3D) trajectory of Ferry UAV such that its data collection mission completion time is minimized. Numerical results show the efficiency and low-latency of the proposed clustering algorithm, and the proposed 3D optimal trajectory design for large-scale UAV swarms data collection admits better performance than that with fixed altitude.