A Complex Network Theory-Based Modeling Framework for Unmanned Aerial Vehicle Swarms

Abstract

Unmanned aerial vehicle (UAV) swarms is an emerging technology that will significantly expand the application areas and open up new possibilities for UAVs, while also presenting new requirements for the robustness and reliability of the UAV swarming system. However, its complex and dynamic characteristics make it extremely challenging and uncertain to model such a system. In this study, to reach a full understanding of the swarming system, a modeling framework based on complex network theory is presented. First, the scope of work is identified from the point of view of control algorithms considering the dynamics and research novelty of the development of UAV swarming control strategy and three control structures consisting of three interdependent network layers are proposed. Second, three algorithms that systematically build the modeling framework considering all characteristics of the system are also developed. Finally, some network measurements are introduced by adjusting the fundamental ones into the UAV swarming system. The proposed framework is applied to a case study to illustrate the visualization models and estimate the statistical characteristics of the proposed networks with static and dynamic topology analysis. Furthermore, a simple demonstration of the robustness evaluation of the network is also presented. The networks obtained from this framework can be used to further analyze the robustness or reliability of a UAV swarming system in a high-confrontation battlefield environment the effect of cascading failure in ad-hoc network on system.

A Group Maintenance Method of Drone Swarm Considering System Mission Reliability

“…Based on the above three fundamental indicators of a complex network, reference [22] proposes network reliability indicators:…”

IHBO_CA: an improved honey-badger optimization-based communication approach for energy-efficient deployment of secure flying ad-hoc network (FANET)

“…The unstable and constrained connectivity between UAVs over FANET is improved via optimum mobility and topology changes in UAVs [55,56]. The various routing protocols like AODV (Ad-hoc On-demand Distance Vector), DSDV (Destination Sequenced Distance Vector), DSR (Dynamic Source Routing), and OLSR (Optimized Link State Routing) are introduced for packet transmission among UAVs over FANET with e cient software and hardware con guration.…”

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

“…In practical networks, nodes generally have multidimensional nonlinear dynamics (Refs. [29][30][31]). For example, in unmanned aerial vehicle systems, the mechanical equations, kinematic equations and navigation equations are all multi-dimensional nonlinear differential equations (Ref.…”

“…For example, in unmanned aerial vehicle systems, the mechanical equations, kinematic equations and navigation equations are all multi-dimensional nonlinear differential equations (Ref. [29]), such as in the kinematic equation, it usually has multidimensional state variables including location, velocity, and attitude angle. In the ecological network, the Lotka-Volterra model used to describe the interactions between predators and prey is also multi-dimensional nonlinear (Ref.…”