University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Self-adaptive multi-robot construction using gene regulatory networks

Guo, H, Meng, Y and Jin, Y (2009) Self-adaptive multi-robot construction using gene regulatory networks

[img]
Preview
PDF
GRNMultiRobot.pdf
Available under License : See the attached licence file.

Download (911kB)
[img]
Preview
PDF (licence)
SRI_deposit_agreement.pdf

Download (33kB)

Abstract

Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, infinite richness, and limited availability of information. Gene regulatory networks (GRNs) are models of genes and gene interactions at the expression level. In this paper, inspired by the biological organisms and GRNs models, a distributed multi-robot self-construction method is proposed. By using this method, a multi-robot system can self-construct to different predefined shapes, and self-reorganize to adapt to dynamic environments. Various case studies have been conducted in the simulation, and the simulation results demonstrate the efficiency and convergence of the proposed method.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions: Faculty of Engineering and Physical Sciences > Computing Science
Depositing User: Symplectic Elements
Date Deposited: 06 Jul 2012 13:50
Last Modified: 23 Sep 2013 19:27
URI: http://epubs.surrey.ac.uk/id/eprint/532824

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800