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Updated: May 25, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

A hierarchical gene regulatory network for adaptive multirobot pattern formation.

Yaochu Jin¹, Hongliang Guo, Yan Meng

¹Department of Computing, University of Surrey, Guildford, Surrey, UK. yaochu.jin@surrey.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 8, 2012

Summary

This study introduces a hierarchical gene regulatory network (H-GRN) enabling multirobot systems to adapt pattern formation in dynamic environments using only local perception. The H-GRN model ensures adaptive pattern generation and decentralized control for robust performance.

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Last Updated: May 25, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Robotics
Artificial Intelligence
Computational Biology

Background:

Existing multirobot pattern formation systems lack adaptability in dynamic environments.
Adaptation in current systems often requires global information, which is impractical for robots with local perception.

Purpose of the Study:

To propose a novel hierarchical gene regulatory network (H-GRN) for adaptive multirobot pattern generation and formation.
To enable multirobot systems to dynamically adjust patterns based on environmental changes using local perception.

Main Methods:

A two-layer gene regulatory network (GRN) architecture was developed.
The first layer handles adaptive pattern generation, optimized via an evolutionary algorithm.
The second layer provides decentralized control for robot movement towards the generated pattern.

Main Results:

Simulations confirmed the H-GRN's effectiveness in forming desired patterns within changing environments.
The model demonstrated robustness against individual robot failures.
Proof-of-concept experiments with e-puck robots validated the H-GRN's feasibility.

Conclusions:

The H-GRN provides an effective solution for adaptive multirobot pattern formation in dynamic settings.
The proposed model enhances system robustness and adaptability through a decentralized, locally-perceptive approach.