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Updated: Jun 23, 2026
11:53
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
Published on: October 14, 2017
A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory
Hongliang Guo1, Yan Meng, Yaochu Jin
1Department of Electrical and Computer Engineering, Stevens Institute of Technology, NJ 07030, USA. hguo@stevens.edu
Bio Systems
|May 19, 2009
Summary
This study introduces a novel Gene Regulatory Network (GRN)-based algorithm for multi-robot systems, enabling autonomous self-organization into shapes and adaptive reorganization in dynamic environments for efficient construction tasks.
Area of Science:
- Robotics
- Artificial Intelligence
- Developmental Biology
Background:
- Multi-robot systems face challenges in predicting emergent behaviors from local agent interactions.
- Biological systems exhibit robust, complex behaviors from simple local interactions, offering inspiration for artificial systems.
- Gene Regulatory Networks (GRNs) are crucial for understanding biological development and evolution.
Purpose of the Study:
- To propose a distributed Gene Regulatory Network (GRN)-based algorithm for multi-robot construction tasks.
- To enable autonomous self-organization and adaptive self-reorganization in multi-robot systems.
- To optimize the developmental process for reduced travel distance and convergence time.
Main Methods:
- A distributed algorithm inspired by biological Gene Regulatory Networks (GRNs).
- Multi-objective optimization algorithm to evolve the developmental process.
- Theoretical proof of system convergence.
Main Results:
- Robots autonomously self-organize into predefined shapes.
- Robots adaptively self-reorganize in dynamic environments.
- Simulations demonstrate the efficiency and convergence of the proposed GRN-based method.
Conclusions:
- The proposed GRN-based algorithm effectively facilitates self-organization and adaptation in multi-robot systems.
- The method achieves optimized performance in terms of travel distance and convergence time.
- This biologically inspired approach offers a promising direction for complex multi-robot coordination.
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