Group Mobility Model for Complex Multimission Cooperation of UAV Swarm

Abstract

In the unmanned aerial vehicle (UAV) swarm combat system, multiple UAVs’ collaborative operations can solve the bottleneck of the limited capability of a single UAV when they carry out complicated missions in complex combat scenarios. As one of the critical technologies of UAV collaborative operation, the mobility model is the basic infrastructure that plays an important role for UAV networking, routing, and task scheduling, especially in high dynamic and real-time scenarios. Focused on real-time guarantee and complex mission cooperative execution, a multilevel reference node mobility model based on the reference node strategy, namely, the ML-RNGM model, is proposed. In this model, the task decomposition and task correlation of UAV cluster execution are realized by using the multilayer task scheduling model. Based on the gravity model of spatial interaction and the correlation between tasks, the reference node selection algorithm is proposed to select the appropriate reference node in the process of node movement. This model can improve the real-time performance of individual tasks and the overall mission group carried out by UAVs. Meanwhile, this model can enhance the connectivity between UAVs when they are performing the same mission group. Finally, OMNeT++ is used to simulate the ML-RNGM model with three experiments, including the different number of nodes and clusters. Within the three experiments, the ML-RNGM model is compared with the random class mobility model, the reference class mobility model, and the associated class mobility model for the network connectivity rate, the average end-to-end delay, and the overhead caused by algorithms. The experimental results show that the ML-RNGM model achieves an obvious improvement in network connectivity and real-time performance for missions and tasks.

Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process

“…ArGoS [44] A framework for simulating cooperative UAV swarms performing joint missions. OMNET++ [45] Task decomposition and task correlation of UAV swarm OMNET++ [46] Multiple UAVs collaborating for improved field phenotyping. Gazebo [47] Resource balancing for MAV (Mobile Aerial Vehicle).…”

A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications

“…In order to guarantee the stability and dependability of UAVs, many researchers have already made significant progress in task allocation and cooperative execution. The idea of an avionics cloud based on a UAV swarm was proposed by [5], wherein the cloud model for avionics system integration was discussed. Regarding the communication mechanism of an intelligent UAV swarm, UAVs are highly mobile communication nodes that may function as transmitters or relays simultaneously.…”