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UAV Swarms Behavior Modeling Using Tracking Bigraphical Reactive Systems.

Piotr Cybulski1, Zbigniew Zieliński1

  • 1Faculty of Cybernetics, Military University of Technology, ul. gen. S. Kaliskiego 2, 00-908 Warsaw, Poland.

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Summary
This summary is machine-generated.

A new method models Unmanned Aerial Vehicle (UAV) swarm missions using bigraphs. It determines optimal autonomous behaviors for UAVs, enhancing swarm efficiency and cooperation in complex scenarios.

Keywords:
UAVsagent behaviorbigraphsmodelingmulti-agent systemsplanningswarmswarm roboticsunmanned aerial vehicles

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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.