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Swarm Intelligence for Collaborative Play in Humanoid Soccer Teams.

Farzad Nadiri1, Ahmad B Rad1

  • 1Autonomous and Intelligent Systems Laboratory, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada.

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Summary
This summary is machine-generated.

This study introduces a biologically inspired swarm intelligence framework for humanoid soccer robots, enhancing teamwork and decision-making. The novel approach significantly boosts scoring and ball possession compared to traditional methods.

Keywords:
decentralized controlhumanoid soccer robotsrobot communicationswarm intelligence

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Swarm Intelligence

Background:

  • Humanoid soccer robots require advanced teamwork and decision-making in dynamic environments.
  • Current systems often struggle with real-time collaboration and adaptability.

Purpose of the Study:

  • To propose a biologically inspired swarm intelligence framework for humanoid soccer.
  • To enhance robot performance through decentralized coordination and adaptive behaviors.

Main Methods:

  • Implemented a low-overhead User Datagram Protocol (UDP) for communication.
  • Utilized Ant Colony Optimization (ACO) for decentralized role allocation.
  • Employed Reynolds' flocking for formation control and an adaptive layer for failure recovery.

Main Results:

  • Simulations showed a 25-40% increase in goals scored.
  • Average ball possession improved by 8-10% compared to centralized baselines.
  • The framework demonstrated robustness under robot dropouts.

Conclusions:

  • The proposed swarm intelligence framework significantly improves humanoid soccer robot performance.
  • Decentralized mechanisms and adaptive behaviors are key to competitive robotic soccer.
  • This approach offers a scalable and resilient solution for multi-robot systems.