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Collaborative Control of Multiple Robots Using Genetic Fuzzy Systems

Published online by Cambridge University Press:  15 April 2019

Anoop Sathyan Open the ORCID record for Anoop Sathyan [Opens in a new window]  and
Ou Ma
Anoop Sathyan*
Affiliation:
Department of Aerospace Engineering, University of Cincinnati, Cincinnati, OH 45221, USA. E-mail: [email protected]
Ou Ma
Affiliation:
Department of Aerospace Engineering, University of Cincinnati, Cincinnati, OH 45221, USA. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper introduces an approach of collaborative control for individual robots to collaboratively perform a common task, without the need for a centralized controller to coordinate the group. The approach is illustrated by an application example involving multiple robots performing a collaborative task to achieve a common goal. The objective of this example problem is to control multiple robots that are connected to an object through elastic cables in order to bring the object to a target position. There is no communication between the robots, and hence each robot is unaware of how the other robots are going to react at any instant. Only the information pertaining to the object and the target is available to all the robots at any instant. Genetic fuzzy system (GFS) is used to develop controller for each of the robots. The nonlinearity of fuzzy logic systems coupled with the search capability of genetic algorithms provides a tool to design controllers for such collaborative tasks. A set of training scenarios are developed to train the individual robot controllers for this task. The trained controllers are then tested on an extensive set of scenarios. This paper describes the development process of GFS controllers for dynamic case involving systems consisting of three robots. It is also shown that the GFS controllers are scalable for the more complex systems involving more than three robots.

Keywords

Collaborative controlGenetic fuzzy systemDecentralized controlIntelligent systemsMachine learningCable robot
Type
Articles
Information
Robotica , Volume 37 , Issue 11 , November 2019 , pp. 1922 - 1936
Copyright
© Cambridge University Press 2019 

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