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Related Experiment Video

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Operation of the Collaborative Composite Manufacturing CCM System

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Genetic Fuzzy Based Scalable System of Distributed Robots for a Collaborative Task.

Anoop Sathyan¹, Kelly Cohen¹, Ou Ma¹

¹Department of Aerospace Engineering, University of Cincinnati, Cincinnati, OH, United States.

Frontiers in Robotics and AI

|January 27, 2021

Summary

This study presents a scalable, decentralized robot control system using a Genetic Fuzzy System (GFS). The GFS enables teams of homogenous robots to perform collaborative tasks without communication, adapting to varying team sizes and moving targets.

Keywords:

cable robot collaborative control decentralized control genetic fuzzy system intelligent systems machine learning

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Decentralized control of homogenous robots for collaborative tasks presents scalability challenges.
Existing methods often require robot communication or retraining for different team sizes.

Purpose of the Study:

To introduce a novel Genetic Fuzzy System (GFS) paradigm for scalable, decentralized control of homogenous robots.
To enable robot teams to perform collaborative tasks without inter-robot communication and adapt to dynamic environments.

Main Methods:

A Genetic Fuzzy System (GFS) framework was employed to train controllers for individual robots.
A single GFS model was shared among all homogenous robots, leveraging their uniformity.
The system was tested with varying numbers of robots, stationary and moving targets, and diverse robot placements.

Main Results:

The GFS-based approach demonstrated effective collaborative control without explicit robot communication.
The system proved scalable, allowing team size adjustments without additional training.
Successful task completion was achieved even with moving targets and unconstrained robot placements.

Conclusions:

The proposed scalable GFS framework offers an effective solution for decentralized, homogenous robot teams in collaborative tasks.
This methodology enhances adaptability and scalability in robotic systems, reducing training overhead.
The GFS paradigm shows promise for complex robotic applications requiring flexible and robust control.