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

Area of Science:

• Robotics and Control Systems
• Network Science
• Complex Systems

Background:

• Unmanned aerial vehicle (UAV) swarms offer expanded applications but pose significant modeling challenges due to their complex and dynamic nature.
• Ensuring the robustness and reliability of UAV swarming systems is critical for their successful deployment.

Purpose of the Study:

• To develop a comprehensive modeling framework for UAV swarming systems using complex network theory.
• To analyze the dynamics, control strategies, and network characteristics of UAV swarms.

Main Methods:

• Proposed a modeling framework based on complex network theory with three interdependent network layers.
• Developed algorithms for systematically building the framework, considering system characteristics.
• Adapted network measurements for UAV swarming systems and performed static and dynamic topology analysis.

Main Results:

• Illustrated visualization models and statistical characteristics of UAV swarming networks.
• Demonstrated the framework's application in a case study for robustness evaluation.
• Provided a method to analyze system robustness and reliability in high-confrontation environments.

Conclusions:

• The complex network theory framework effectively models UAV swarming systems.
• The framework aids in understanding system dynamics, analyzing robustness, and evaluating reliability.
• This approach is valuable for assessing UAV swarming system performance, especially in challenging environments.