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Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm.

Sabyasachi Mondal1, Antonios Tsourdos1

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Summary

New agents can be autonomously added to Multi-Agent Systems (MASs) for beyond visual line-of-sight (BVLOS) missions. This study minimizes consensus energy increase by optimizing new agent connections using a Two-Dimensional Genetic Algorithm.

Keywords:
BVLOSautonomous missionconsensusdistributed controloptimal topologytwo-dimensional genetic algorithm

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Area of Science:

  • Robotics
  • Computer Science
  • Control Theory

Background:

  • Multi-Agent Systems (MASs) are crucial for complex tasks like beyond visual line-of-sight (BVLOS) missions.
  • Adding new agents to existing MASs can enhance operational capabilities.
  • However, agent addition typically alters communication topology and increases consensus control energy.

Purpose of the Study:

  • To develop a method for autonomous agent addition to MASs.
  • To minimize the increase in consensus control energy during agent addition.
  • To ensure the updated network topology maintains stability by including a spanning tree.

Main Methods:

  • Proposing an approach for autonomous agent integration into existing MASs.
  • Utilizing a Two-Dimensional Genetic Algorithm to solve for optimal topology.
  • Formulating the problem as minimizing additional consensus control energy.

Main Results:

  • Demonstrated a method to add agents autonomously without disrupting the existing communication topology.
  • Successfully minimized the increase in consensus control energy.
  • The updated topology ensures network stability by maintaining a spanning tree.

Conclusions:

  • Autonomous agent addition is feasible and strategically advantageous for BVLOS missions.
  • The proposed method effectively balances network expansion with energy efficiency.
  • The Two-Dimensional Genetic Algorithm provides an optimal solution for topology updates in MASs.