UAV Swarms Behavior Modeling Using Tracking Bigraphical Reactive Systems

Area of Science:

• Robotics and Autonomous Systems
• Artificial Intelligence
• Control Theory

Background:

• Increasing interest in Unmanned Aerial Vehicle (UAV) swarms for civilian and military applications.
• Need for efficient operation of UAV swarms requires addressing autonomous behaviors, cooperation, and complex scenario suitability.
• Current methods may lack scalability or flexibility for diverse swarm tasks.

Purpose of the Study:

• To develop a novel method for modeling UAV swarm missions and determining element behaviors.
• To enable efficient operation and cooperation of UAV swarms in complex scenarios.
• To provide a scalable, automated, and problem-agnostic approach to UAV swarm management.

Main Methods:

• Utilized bigraphs with tracking to model UAV swarm missions, tasks, and agent activities.
• Developed a key algorithm to determine all possible behavior policies for swarm elements.
• Separated mission modeling from behavior determination for future algorithm integration.

Main Results:

• A novel, scalable, and automated method for modeling UAV swarm missions and behaviors.
• An algorithm capable of determining optimal behavior policies for swarm elements.
• Demonstrated the method's effectiveness through two simulation case studies.

Conclusions:

• The proposed bigraph-based method effectively models UAV swarm missions and determines optimal behaviors.
• The approach is scalable, automated, and problem-agnostic, applicable to various swarm tasks.
• Separating mission modeling and behavior determination enhances future adaptability and algorithm integration.