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

Abstract

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.

Clustering Algorithm Based on the Ground-Air Cooperative Architecture in Border Patrol Scenarios

“…However, the method is not suitable for fast and high-mobility scenarios. Ma Ting et al [18] introduced a modified K-means algorithm to select a supercluster head UAV agent with low latency, gaining efficient swarm management. The clustering algorithm based on area division mainly considers energy consumption and load balancing, which is not suitable for high mobility scenarios.…”

Voting-Based Scheme for Leader Election in Lead-Follow UAV Swarm with Constrained Communication

“…On the contrary, a lead-follow enabled swarm is a centralized swarm system and is unified by nature, because only one center is responsible for commanding discrete elements. This intrinsic feature thus gives the leader an indispensable role in several tasks [56,57].…”

“…The method merely considers two energy-related factors and has difficulty fitting in highly dynamic applications. Ma Ting et al [57] introduce a modified k-means algorithm to select a super cluster head UAV agent with low latency, gaining an efficient swarm management. The authors in [31] are dedicated to optimal drone communication and put forward a bio-inspired cluster head selection algorithm.…”

Structure management scheme of flying ad-hoc networks (FANET): A dynamic weight values weighted clustering algorithm

“…To solve such problems, FANETs need to use some way of dividing the network into regions, into cluster heads(CHs) and cluster members(CMs), and managing them through a distributed structure, thus improving network performance. The clustering algorithm is a common method of network partitioning and is used in wireless networks such as sensor networks, mobile ad-hoc networks, vehicular networks and FANETs [11]. Classical clustering algorithms include the maximum node degree algorithm, the minimum mobility algorithm and the weighted clustering algorithm(WCA).…”