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Fuzzy-based self organizing aggregation method for swarm robots.

Oğuz Mısır1, Levent Gökrem1, M Serhat Can2

  • 1Tokat Gaziosmanpaşa Universty, Faculty of Engineering and Architecture, Mechatronics Engineering Department, Tokat, Turkey.

Bio Systems
|June 30, 2020
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Summary

This study introduces a fuzzy-based self-organizing aggregation method for swarm robots. This approach enables robots to navigate obstacles and aggregate effectively using fuzzy logic, even with noisy sensor data.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Swarm robotics often relies on traditional self-organizing methods.
  • Limited sensor input poses a challenge for effective swarm behavior.
  • Obstacle avoidance is crucial for robots operating in confined environments.

Purpose of the Study:

  • To propose a novel fuzzy-based self-organizing aggregation method for swarm robots.
  • To enable swarm robots to evaluate sensor data using fuzzy logic for aggregation and obstacle avoidance.
  • To assess the performance, scalability, and flexibility of the proposed method under various conditions.

Main Methods:

  • A fuzzy-based self-organizing aggregation method was developed for swarm robots.
  • Fuzzy logic controllers were utilized to interpret limited sensor input.
  • Systematic experiments were conducted in different arena sizes with varying numbers of robots and detection areas.
  • Sensor noise was introduced to test method robustness.

Main Results:

  • Swarm robots successfully displayed aggregation behavior.
  • The method facilitated obstacle escape within a bounded arena.
  • Performance remained consistent despite changes in robot numbers and detection distances.
  • The fuzzy logic approach proved effective even with noisy sensor inputs.

Conclusions:

  • The fuzzy-based self-organizing aggregation method is effective for swarm robots.
  • The approach demonstrates scalability and flexibility in dynamic environments.
  • Fuzzy logic provides a robust solution for processing limited sensor data in swarm robotics.